[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowGettingSchemaMultipleTimes()
{
var modelLocation = "cifar_saved_model";
var mlContext = new MLContext(seed: 1, conc: 1);
for (int i = 0; i < 10; i++)
{
var schema = TensorFlowUtils.GetModelSchema(mlContext, modelLocation);
Assert.NotNull(schema);
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowTransformCifarSavedModel()
{
var modelLocation = "cifar_saved_model";
var mlContext = new MLContext(seed: 1, conc: 1);
var loadModelSchema = TensorFlowUtils.GetModelSchema(mlContext, modelLocation);
Assert.Equal(335, loadModelSchema.Count);
var tensorFlowModel = TensorFlowUtils.LoadTensorFlowModel(mlContext, modelLocation);
var schema = tensorFlowModel.GetInputSchema();
Assert.True(schema.TryGetColumnIndex("Input", out int column));
var type = (VectorType)schema[column].Type;
var imageHeight = type.Dimensions[0];
var imageWidth = type.Dimensions[1];
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = mlContext.Data.ReadFromTextFile(dataFile, columns: new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
);
var images = new ImageLoaderTransformer(mlContext, imageFolder, ("ImagePath", "ImageReal")).Transform(data);
var cropped = new ImageResizerTransformer(mlContext, "ImageReal", "ImageCropped", imageWidth, imageHeight).Transform(images);
var pixels = new ImagePixelExtractorTransformer(mlContext, "ImageCropped", "Input", interleave: true).Transform(cropped);
IDataView trans = new TensorFlowTransformer(mlContext, tensorFlowModel, "Input", "Output").Transform(pixels);
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursor(col => col == output))
{
var buffer = default(VBuffer <float>);
var getter = cursor.GetGetter <VBuffer <float> >(output);
var numRows = 0;
while (cursor.MoveNext())
{
getter(ref buffer);
Assert.Equal(10, buffer.Length);
numRows += 1;
}
Assert.Equal(4, numRows);
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowInputsOutputsSchemaTest()
{
var mlContext = new MLContext(seed: 1, conc: 1);
var model_location = "mnist_model/frozen_saved_model.pb";
var schema = TensorFlowUtils.GetModelSchema(mlContext, model_location);
Assert.Equal(86, schema.Count);
Assert.True(schema.TryGetColumnIndex("Placeholder", out int col));
var type = (VectorType)schema[col].Type;
Assert.Equal(2, type.Dimensions.Length);
Assert.Equal(28, type.Dimensions[0]);
Assert.Equal(28, type.Dimensions[1]);
var metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowOperatorTypeKind].Type;
Assert.NotNull(metadataType);
Assert.True(metadataType is TextType);
ReadOnlyMemory <char> opType = default;
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowOperatorTypeKind, ref opType);
Assert.Equal("Placeholder", opType.ToString());
metadataType = schema[col].Metadata.Schema.GetColumnOrNull(TensorFlowUtils.TensorflowUpstreamOperatorsKind)?.Type;
Assert.Null(metadataType);
Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D/ReadVariableOp", out col));
type = (VectorType)schema[col].Type;
Assert.Equal(new[] { 5, 5, 1, 32 }, type.Dimensions);
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowOperatorTypeKind].Type;
Assert.NotNull(metadataType);
Assert.True(metadataType is TextType);
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowOperatorTypeKind, ref opType);
Assert.Equal("Identity", opType.ToString());
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowUpstreamOperatorsKind].Type;
Assert.NotNull(metadataType);
VBuffer <ReadOnlyMemory <char> > inputOps = default;
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowUpstreamOperatorsKind, ref inputOps);
Assert.Equal(1, inputOps.Length);
Assert.Equal("conv2d/kernel", inputOps.GetValues()[0].ToString());
Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D", out col));
type = (VectorType)schema[col].Type;
Assert.Equal(new[] { 28, 28, 32 }, type.Dimensions);
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowOperatorTypeKind].Type;
Assert.NotNull(metadataType);
Assert.True(metadataType is TextType);
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowOperatorTypeKind, ref opType);
Assert.Equal("Conv2D", opType.ToString());
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowUpstreamOperatorsKind].Type;
Assert.NotNull(metadataType);
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowUpstreamOperatorsKind, ref inputOps);
Assert.Equal(2, inputOps.Length);
Assert.Equal("reshape/Reshape", inputOps.GetValues()[0].ToString());
Assert.Equal("conv2d/Conv2D/ReadVariableOp", inputOps.GetValues()[1].ToString());
Assert.True(schema.TryGetColumnIndex("Softmax", out col));
type = (VectorType)schema[col].Type;
Assert.Equal(new[] { 10 }, type.Dimensions);
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowOperatorTypeKind].Type;
Assert.NotNull(metadataType);
Assert.True(metadataType is TextType);
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowOperatorTypeKind, ref opType);
Assert.Equal("Softmax", opType.ToString());
metadataType = schema[col].Metadata.Schema[TensorFlowUtils.TensorflowUpstreamOperatorsKind].Type;
Assert.NotNull(metadataType);
schema[col].Metadata.GetValue(TensorFlowUtils.TensorflowUpstreamOperatorsKind, ref inputOps);
Assert.Equal(1, inputOps.Length);
Assert.Equal("sequential/dense_1/BiasAdd", inputOps.GetValues()[0].ToString());
model_location = "model_matmul/frozen_saved_model.pb";
schema = TensorFlowUtils.GetModelSchema(mlContext, model_location);
char name = 'a';
for (int i = 0; i < schema.Count; i++)
{
Assert.Equal(name.ToString(), schema[i].Name);
type = (VectorType)schema[i].Type;
Assert.Equal(new[] { 2, 2 }, type.Dimensions);
name++;
}
}
/// <summary>
/// Get <see cref="DataViewSchema"/> for only those nodes which are marked "Placeholder" in the TensorFlow model.
/// This method is convenient for exploring the model input(s) in case TensorFlow graph is very large.
/// </summary>
public DataViewSchema GetInputSchema()
{
return(TensorFlowUtils.GetModelSchema(_env, Session.Graph, "Placeholder"));
}
/// <summary>
/// Get <see cref="DataViewSchema"/> for complete model. Every node in the TensorFlow model will be included in the <see cref="DataViewSchema"/> object.
/// </summary>
public DataViewSchema GetModelSchema()
{
return(TensorFlowUtils.GetModelSchema(_env, Session.Graph));
}
/// <summary>
/// Get <see cref="Schema"/> for complete model. Every node in the TensorFlow model will be included in the <see cref="Schema"/> object.
/// </summary>
internal Schema GetModelSchema()
{
return(TensorFlowUtils.GetModelSchema(_env, Session.Graph));
}
public void TensorFlowInputsOutputsSchemaTest()
{
using (var env = new ConsoleEnvironment(seed: 1, conc: 1))
{
var model_location = "mnist_model/frozen_saved_model.pb";
var schema = TensorFlowUtils.GetModelSchema(env, model_location);
Assert.Equal(54, schema.ColumnCount);
Assert.True(schema.TryGetColumnIndex("Placeholder", out int col));
var type = schema.GetColumnType(col).AsVector;
Assert.Equal(2, type.DimCount);
Assert.Equal(28, type.GetDim(0));
Assert.Equal(28, type.GetDim(1));
var metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col);
Assert.NotNull(metadataType);
Assert.True(metadataType.IsText);
ReadOnlyMemory <char> opType = default;
schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType);
Assert.Equal("Placeholder", opType.ToString());
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col);
Assert.Null(metadataType);
Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D/ReadVariableOp", out col));
type = schema.GetColumnType(col).AsVector;
Assert.Equal(4, type.DimCount);
Assert.Equal(5, type.GetDim(0));
Assert.Equal(5, type.GetDim(1));
Assert.Equal(1, type.GetDim(2));
Assert.Equal(32, type.GetDim(3));
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col);
Assert.NotNull(metadataType);
Assert.True(metadataType.IsText);
schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType);
Assert.Equal("Identity", opType.ToString());
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col);
Assert.NotNull(metadataType);
VBuffer <ReadOnlyMemory <char> > inputOps = default;
schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps);
Assert.Equal(1, inputOps.Length);
Assert.Equal("conv2d/kernel", inputOps.Values[0].ToString());
Assert.True(schema.TryGetColumnIndex("conv2d/Conv2D", out col));
type = schema.GetColumnType(col).AsVector;
Assert.Equal(3, type.DimCount);
Assert.Equal(28, type.GetDim(0));
Assert.Equal(28, type.GetDim(1));
Assert.Equal(32, type.GetDim(2));
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col);
Assert.NotNull(metadataType);
Assert.True(metadataType.IsText);
schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType);
Assert.Equal("Conv2D", opType.ToString());
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col);
Assert.NotNull(metadataType);
schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps);
Assert.Equal(2, inputOps.Length);
Assert.Equal("reshape/Reshape", inputOps.Values[0].ToString());
Assert.Equal("conv2d/Conv2D/ReadVariableOp", inputOps.Values[1].ToString());
Assert.True(schema.TryGetColumnIndex("Softmax", out col));
type = schema.GetColumnType(col).AsVector;
Assert.Equal(1, type.DimCount);
Assert.Equal(10, type.GetDim(0));
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.OpType, col);
Assert.NotNull(metadataType);
Assert.True(metadataType.IsText);
schema.GetMetadata(TensorFlowUtils.OpType, col, ref opType);
Assert.Equal("Softmax", opType.ToString());
metadataType = schema.GetMetadataTypeOrNull(TensorFlowUtils.InputOps, col);
Assert.NotNull(metadataType);
schema.GetMetadata(TensorFlowUtils.InputOps, col, ref inputOps);
Assert.Equal(1, inputOps.Length);
Assert.Equal("sequential/dense_1/BiasAdd", inputOps.Values[0].ToString());
model_location = "model_matmul/frozen_saved_model.pb";
schema = TensorFlowUtils.GetModelSchema(env, model_location);
char name = 'a';
for (int i = 0; i < schema.ColumnCount; i++)
{
Assert.Equal(name.ToString(), schema.GetColumnName(i));
type = schema.GetColumnType(i).AsVector;
Assert.Equal(2, type.DimCount);
Assert.Equal(2, type.GetDim(0));
Assert.Equal(2, type.GetDim(1));
name++;
}
}
}
/// <summary>
/// Get <see cref="DataViewSchema"/> for only those nodes which are marked "Placeholder" in the TensorFlow model.
/// This method is convenient for exploring the model input(s) in case TensorFlow graph is very large.
/// </summary>
public DataViewSchema GetInputSchema()
{
return(TensorFlowUtils.GetModelSchema(_env, Session.graph, TreatOutputAsBatched, "Placeholder"));
}
