void TestOldSavingAndLoading()
{
var modelFile = "model_matmul/frozen_saved_model.pb";
var dataView = ComponentCreation.CreateDataView(Env,
new List <TestData>(new TestData[] {
new TestData()
{
a = new[] { 1.0f, 2.0f, 3.0f, 4.0f },
b = new[] { 1.0f, 2.0f, 3.0f, 4.0f }
},
new TestData()
{
a = new[] { 2.0f, 2.0f, 2.0f, 2.0f },
b = new[] { 3.0f, 3.0f, 3.0f, 3.0f }
},
new TestData()
{
a = new[] { 5.0f, 6.0f, 10.0f, 12.0f },
b = new[] { 10.0f, 8.0f, 6.0f, 6.0f }
}
}));
var est = new TensorFlowEstimator(Env, modelFile, new[] { "a", "b" }, new[] { "c" });
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);
ValidateTensorFlowTransformer(loadedView);
}
}
void TestSimpleCase()
{
var modelFile = "model_matmul/frozen_saved_model.pb";
var dataView = ComponentCreation.CreateDataView(Env,
new List <TestData>(new TestData[] {
new TestData()
{
a = new[] { 1.0f, 2.0f, 3.0f, 4.0f },
b = new[] { 1.0f, 2.0f, 3.0f, 4.0f }
},
new TestData()
{
a = new[] { 2.0f, 2.0f, 2.0f, 2.0f },
b = new[] { 3.0f, 3.0f, 3.0f, 3.0f }
}
}));
var xyData = new List <TestDataXY> {
new TestDataXY()
{
A = new float[4], B = new float[4]
}
};
var stringData = new List <TestDataDifferntType> {
new TestDataDifferntType()
{
a = new string[4], b = new string[4]
}
};
var sizeData = new List <TestDataSize> {
new TestDataSize()
{
a = new float[2], b = new float[2]
}
};
var pipe = new TensorFlowEstimator(Env, modelFile, new[] { "a", "b" }, new[] { "c" });
var invalidDataWrongNames = ComponentCreation.CreateDataView(Env, xyData);
var invalidDataWrongTypes = ComponentCreation.CreateDataView(Env, stringData);
var invalidDataWrongVectorSize = ComponentCreation.CreateDataView(Env, sizeData);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
pipe.GetOutputSchema(SchemaShape.Create(invalidDataWrongVectorSize.Schema));
try
{
pipe.Fit(invalidDataWrongVectorSize);
Assert.False(true);
}
catch (ArgumentOutOfRangeException) { }
catch (InvalidOperationException) { }
}
static void Main(string[] args)
{
Console.Write("Image directory: ");
var imagePath = Console.ReadLine();
var context = new MLContext();
// this is necessary for calling Fit() later
var fakeData = context.Data.ReadFromEnumerable(new List <InputModel>());
// loads images from disk
var imageLoadingEstimator = new ImageLoadingEstimator(
context,
imagePath,
("ImageData", nameof(InputModel.Path)));
// resizes images to required size
var imageResizingEstimator = new ImageResizingEstimator(
context,
"ImageResized",
64,
64,
"ImageData",
ImageResizerTransformer.ResizingKind.IsoPad);
// transforms images to float vectors
var imagePixelExtractingEstimator = new ImagePixelExtractingEstimator(context,
new ImagePixelExtractorTransformer.ColumnInfo(
"input_1",
"ImageResized",
ImagePixelExtractorTransformer.ColorBits.Rgb, interleave: true,
scale: 1 / 255f));
// loads the TF model from disk
var tensorFlowEstimator = new TensorFlowEstimator(
context,
new[] { "final_layer/Sigmoid" },
new[] { "input_1" },
"Model/MalariaModel.pb");
// create a ML pipeline
var pipeline = imageLoadingEstimator
.Append(imageResizingEstimator)
.Append(imagePixelExtractingEstimator)
.Append(tensorFlowEstimator);
// fit, otherwise we can't create a prediction engine
var model = pipeline.Fit(fakeData);
var predictor = model.CreatePredictionEngine <InputModel, OutputModel>(context);
foreach (var file in Directory.GetFiles(imagePath, "*.png").Take(100).ToList())
{
var newName = ImageUtilities.ResizeImage(file, 64, 64, imagePath);
var prediction = predictor.Predict(new InputModel {
Path = newName
});
Console.WriteLine($"{Path.GetFileName(file)}: {(prediction.IsInfected() ? "Infected" : "Healthy")}");
}
Console.ReadLine();
}