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 = ComponentCreation.CreateDataView(Env, data);
var pipe = new MissingValueReplacingEstimator(Env,
new NAReplaceTransform.ColumnInfo("A", "NAA", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean),
new NAReplaceTransform.ColumnInfo("B", "NAB", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean),
new NAReplaceTransform.ColumnInfo("C", "NAC", NAReplaceTransform.ColumnInfo.ReplacementMode.Mean),
new NAReplaceTransform.ColumnInfo("D", "NAD", NAReplaceTransform.ColumnInfo.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 TestOldSavingAndLoading()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var samplevector = GetSampleArrayData();
var dataView = ML.Data.ReadFromEnumerable(
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = "data_0";
var outputNames = "output_1";
var est = new DnnImageFeaturizerEstimator(Env, 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);
loadedView.Schema.TryGetColumnIndex(outputNames, out int softMaxOut1);
using (var cursor = loadedView.GetRowCursor(loadedView.Schema[outputNames]))
{
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);
}
}
}
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()
{
//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(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 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.IndicateMissingValues(new[] {
new InputOutputColumnPair("NAA", "A"),
new InputOutputColumnPair("NAB", "B"),
new InputOutputColumnPair("NAC", "C"),
new InputOutputColumnPair("NAD", "D")
});
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);
}
}
