public void TestBackAndForthConversionWithDifferentOrder()
{
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
const int imageWidth = 130;
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 images = new ImageLoadingTransformer(env, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(env, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(env, "ImagePixels", "ImageCropped", ImagePixelExtractingEstimator.ColorBits.All, orderOfExtraction: ImagePixelExtractingEstimator.ColorsOrder.ABRG).Transform(cropped);
IDataView backToBitmaps = new VectorToImageConvertingTransformer(env, "ImageRestored", imageHeight, imageWidth, "ImagePixels",
ImagePixelExtractingEstimator.ColorBits.All, orderOfColors: ImagePixelExtractingEstimator.ColorsOrder.ABRG).Transform(pixels);
var fname = nameof(TestBackAndForthConversionWithDifferentOrder) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(backToBitmaps));
backToBitmaps = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
using (var cursor = backToBitmaps.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageRestored"]);
Bitmap restoredBitmap = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageCropped"]);
Bitmap croppedBitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref restoredBitmap);
Assert.NotNull(restoredBitmap);
bitmapCropGetter(ref croppedBitmap);
Assert.NotNull(croppedBitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var c = croppedBitmap.GetPixel(x, y);
var r = restoredBitmap.GetPixel(x, y);
if (c != r)
{
Assert.False(true);
}
Assert.True(c == r);
}
}
}
}
Done();
}
public void TestOldSavingAndLoading()
{
//skip running for x86 as this test using too much memory (over 2GB limit on x86)
//and very like to hit memory related issue when running on CI
//TODO: optimized memory usage in related code and enable x86 run
if (!Environment.Is64BitProcess)
{
return;
}
var samplevector = GetSampleArrayData();
var dataView = ML.Data.LoadFromEnumerable(
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = "data_0";
var outputNames = "output_1";
var est = ML.Transforms.DnnFeaturizeImage(outputNames, m => m.ModelSelector.ResNet18(m.Environment, m.OutputColumn, m.InputColumn), inputNames);
var transformer = est.Fit(dataView);
var result = transformer.Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
using (var cursor = loadedView.GetRowCursor(loadedView.Schema[outputNames]))
{
VBuffer <float> softMaxValue = default;
var softMaxGetter = cursor.GetGetter <VBuffer <float> >(loadedView.Schema[outputNames]);
float sum = 0f;
int i = 0;
while (cursor.MoveNext())
{
softMaxGetter(ref softMaxValue);
var values = softMaxValue.DenseValues();
foreach (var val in values)
{
sum += val;
if (i == 0)
{
Assert.InRange(val, 0.0, 0.00001);
}
if (i == 7)
{
Assert.InRange(val, 0.62935, 0.62940);
}
if (i == 500)
{
Assert.InRange(val, 0.15521, 0.155225);
}
i++;
}
}
Assert.InRange(sum, 83.50, 84.50);
}
}
}
public void TestOldSavingAndLoading()
{
var data = new[] {
new TestClass()
{
A = 1, B = 3, C = new float[2] {
1, 2
}, D = new double[2] {
3, 4
}
},
new TestClass()
{
A = float.NaN, B = double.NaN, C = new float[2] {
float.NaN, float.NaN
}, D = new double[2] {
double.NaN, double.NaN
}
},
new TestClass()
{
A = float.NegativeInfinity, B = double.NegativeInfinity, C = new float[2] {
float.NegativeInfinity, float.NegativeInfinity
}, D = new double[2] {
double.NegativeInfinity, double.NegativeInfinity
}
},
new TestClass()
{
A = float.PositiveInfinity, B = double.PositiveInfinity, C = new float[2] {
float.PositiveInfinity, float.PositiveInfinity,
}, D = new double[2] {
double.PositiveInfinity, double.PositiveInfinity
}
},
new TestClass()
{
A = 2, B = 1, C = new float[2] {
3, 4
}, D = new double[2] {
5, 6
}
},
};
var dataView = ML.Data.LoadFromEnumerable(data);
var pipe = ML.Transforms.ReplaceMissingValues(
new MissingValueReplacingEstimator.ColumnOptions("NAA", "A", MissingValueReplacingEstimator.ReplacementMode.Mean),
new MissingValueReplacingEstimator.ColumnOptions("NAB", "B", MissingValueReplacingEstimator.ReplacementMode.Mean),
new MissingValueReplacingEstimator.ColumnOptions("NAC", "C", MissingValueReplacingEstimator.ReplacementMode.Mean),
new MissingValueReplacingEstimator.ColumnOptions("NAD", "D", MissingValueReplacingEstimator.ReplacementMode.Mean));
var result = pipe.Fit(dataView).Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
}
}
public void TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset()
{
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
const int imageWidth = 130;
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 images = new ImageLoaderTransformer(env, imageFolder, ("ImagePath", "ImageReal")).Transform(data);
var cropped = new ImageResizerTransformer(env, "ImageReal", "ImageCropped", imageWidth, imageHeight).Transform(images);
var pixels = new ImagePixelExtractorTransformer(env, "ImageCropped", "ImagePixels").Transform(cropped);
IDataView backToBitmaps = new VectorToImageTransform(env, new VectorToImageTransform.Arguments()
{
InterleaveArgb = false,
Column = new VectorToImageTransform.Column[1] {
new VectorToImageTransform.Column()
{
Source = "ImagePixels", Name = "ImageRestored", ImageHeight = imageHeight, ImageWidth = imageWidth, ContainsAlpha = false
}
}
}, pixels);
var fname = nameof(TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(backToBitmaps));
backToBitmaps = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
backToBitmaps.Schema.TryGetColumnIndex("ImageRestored", out int bitmapColumn);
backToBitmaps.Schema.TryGetColumnIndex("ImageCropped", out int cropBitmapColumn);
using (var cursor = backToBitmaps.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(bitmapColumn);
Bitmap restoredBitmap = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(cropBitmapColumn);
Bitmap croppedBitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref restoredBitmap);
Assert.NotNull(restoredBitmap);
bitmapCropGetter(ref croppedBitmap);
Assert.NotNull(croppedBitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var c = croppedBitmap.GetPixel(x, y);
var r = restoredBitmap.GetPixel(x, y);
Assert.True(c.R == r.R && c.G == r.G && c.B == r.B);
}
}
}
Done();
}
}
void TestOldSavingAndLoading(int?gpuDeviceId, bool fallbackToCpu)
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var modelFile = "squeezenet/00000001/model.onnx";
var samplevector = GetSampleArrayData();
var dataView = ML.Data.ReadFromEnumerable(
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = new[] { "data_0" };
var outputNames = new[] { "softmaxout_1" };
var est = new OnnxScoringEstimator(Env, outputNames, inputNames, modelFile, gpuDeviceId, fallbackToCpu);
var transformer = est.Fit(dataView);
var result = transformer.Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
loadedView.Schema.TryGetColumnIndex(outputNames[0], out int softMaxOut1);
using (var cursor = loadedView.GetRowCursor(loadedView.Schema[softMaxOut1]))
{
VBuffer <float> softMaxValue = default;
var softMaxGetter = cursor.GetGetter <VBuffer <float> >(softMaxOut1);
float sum = 0f;
int i = 0;
while (cursor.MoveNext())
{
softMaxGetter(ref softMaxValue);
var values = softMaxValue.DenseValues();
foreach (var val in values)
{
sum += val;
if (i == 0)
{
Assert.InRange(val, 0.00004, 0.00005);
}
if (i == 1)
{
Assert.InRange(val, 0.003844, 0.003845);
}
if (i == 999)
{
Assert.InRange(val, 0.0029566, 0.0029567);
}
i++;
}
}
Assert.InRange(sum, 1.0, 1.00001);
}
}
}
public void TestOldSavingAndLoading(int?gpuDeviceId, bool fallbackToCpu)
{
var modelFile = "squeezenet/00000001/model.onnx";
var samplevector = GetSampleArrayData();
var dataView = ML.Data.LoadFromEnumerable(
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = new[] { "data_0" };
var outputNames = new[] { "softmaxout_1" };
var est = ML.Transforms.ApplyOnnxModel(outputNames, inputNames, modelFile, gpuDeviceId, fallbackToCpu);
var transformer = est.Fit(dataView);
var result = transformer.Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
var sofMaxOut1Col = loadedView.Schema[outputNames[0]];
using (var cursor = loadedView.GetRowCursor(sofMaxOut1Col))
{
VBuffer <float> softMaxValue = default;
var softMaxGetter = cursor.GetGetter <VBuffer <float> >(sofMaxOut1Col);
float sum = 0f;
int i = 0;
while (cursor.MoveNext())
{
softMaxGetter(ref softMaxValue);
var values = softMaxValue.DenseValues();
foreach (var val in values)
{
sum += val;
if (i == 0)
{
Assert.InRange(val, 0.00004, 0.00005);
}
if (i == 1)
{
Assert.InRange(val, 0.003844, 0.003845);
}
if (i == 999)
{
Assert.InRange(val, 0.0029566, 0.0029567);
}
i++;
}
}
Assert.InRange(sum, 0.99999, 1.00001);
}
(transformer as IDisposable)?.Dispose();
}
}
public void TestGreyscaleTransformImages()
{
IHostEnvironment env = new MLContext();
var imageHeight = 150;
var imageWidth = 100;
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 images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Name = "ImageCropped", Source = "ImageReal", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
IDataView grey = ImageGrayscaleTransform.Create(env, new ImageGrayscaleTransform.Arguments()
{
Column = new ImageGrayscaleTransform.Column[1] {
new ImageGrayscaleTransform.Column()
{
Name = "ImageGrey", Source = "ImageCropped"
}
}
}, cropped);
var fname = nameof(TestGreyscaleTransformImages) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(grey));
grey = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
grey.Schema.TryGetColumnIndex("ImageGrey", out int greyColumn);
using (var cursor = grey.GetRowCursor((x) => true))
{
var bitmapGetter = cursor.GetGetter <Bitmap>(greyColumn);
Bitmap bitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref bitmap);
Assert.NotNull(bitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var pixel = bitmap.GetPixel(x, y);
// greyscale image has same values for R,G and B
Assert.True(pixel.R == pixel.G && pixel.G == pixel.B);
}
}
}
}
Done();
}
public void TestOldSavingAndLoading()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var samplevector = getSampleArrayData();
var dataView = ComponentCreation.CreateDataView(Env,
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = "data_0";
var outputNames = "output_1";
var est = new DnnImageFeaturizerEstimator(Env, m => m.ModelSelector.ResNet18(m.Environment, m.InputColumn, m.OutputColumn), inputNames, outputNames);
var transformer = est.Fit(dataView);
var result = transformer.Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
loadedView.Schema.TryGetColumnIndex(outputNames, out int softMaxOut1);
using (var cursor = loadedView.GetRowCursor(col => col == softMaxOut1))
{
VBuffer <float> softMaxValue = default;
var softMaxGetter = cursor.GetGetter <VBuffer <float> >(softMaxOut1);
float sum = 0f;
int i = 0;
while (cursor.MoveNext())
{
softMaxGetter(ref softMaxValue);
var values = softMaxValue.DenseValues();
foreach (var val in values)
{
sum += val;
if (i == 0)
{
Assert.InRange(val, 0.0, 0.00001);
}
if (i == 7)
{
Assert.InRange(val, 0.62935, 0.62940);
}
if (i == 500)
{
Assert.InRange(val, 0.15521, 0.155225);
}
i++;
}
}
Assert.InRange(sum, 83.50, 84.50);
}
}
}