public void TensorFlowTransformCifarInvalidShape() { var modelLocation = "cifar_model/frozen_model.pb"; var mlContext = new MLContext(seed: 1, conc: 1); var imageHeight = 28; var imageWidth = 28; 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").Transform(cropped); var thrown = false; try { IDataView trans = new TensorFlowTransformer(mlContext, modelLocation, "Input", "Output").Transform(pixels); } catch { thrown = true; } Assert.True(thrown); }
public void TensorFlowTransformInceptionTest() { var modelLocation = @"C:\models\TensorFlow\tensorflow_inception_graph.pb"; var mlContext = new MLContext(seed: 1, conc: 1); var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); var data = mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); var images = new ImageLoaderTransformer(mlContext, imageFolder, ("ImagePath", "ImageReal")).Transform(data); var cropped = new ImageResizerTransformer(mlContext, "ImageReal", "ImageCropped", 224, 224).Transform(images); var pixels = new ImagePixelExtractorTransformer(mlContext, "ImageCropped", "input").Transform(cropped); var tf = new TensorFlowTransformer(mlContext, modelLocation, "input", "softmax2_pre_activation").Transform(pixels); tf.Schema.TryGetColumnIndex("input", out int input); tf.Schema.TryGetColumnIndex("softmax2_pre_activation", out int b); using (var curs = tf.GetRowCursor(col => col == b || col == input)) { var get = curs.GetGetter <VBuffer <float> >(b); var getInput = curs.GetGetter <VBuffer <float> >(input); var buffer = default(VBuffer <float>); var inputBuffer = default(VBuffer <float>); while (curs.MoveNext()) { getInput(ref inputBuffer); get(ref buffer); } } }
public static CommonOutputs.TransformOutput ImagePixelExtractor(IHostEnvironment env, ImagePixelExtractorTransformer.Arguments input) { var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImagePixelExtractorTransform", input); var xf = ImagePixelExtractorTransformer.Create(h, input, input.Data); return(new CommonOutputs.TransformOutput() { Model = new TransformModelImpl(h, xf, input.Data), OutputData = xf }); }
[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); } }
public void TensorFlowTransformObjectDetectionTest() { var modelLocation = @"C:\models\TensorFlow\ssd_mobilenet_v1_coco_2018_01_28\frozen_inference_graph.pb"; var mlContext = new MLContext(seed: 1, conc: 1); var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); var data = mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); var images = new ImageLoaderTransformer(mlContext, imageFolder, ("ImagePath", "ImageReal")).Transform(data); var cropped = new ImageResizerTransformer(mlContext, "ImageReal", "ImageCropped", 32, 32).Transform(images); var pixels = new ImagePixelExtractorTransformer(mlContext, "ImageCropped", "image_tensor", asFloat: false).Transform(cropped); var tf = new TensorFlowTransformer(mlContext, modelLocation, new[] { "image_tensor" }, new[] { "detection_boxes", "detection_scores", "num_detections", "detection_classes" }).Transform(pixels); tf.Schema.TryGetColumnIndex("image_tensor", out int input); tf.Schema.TryGetColumnIndex("detection_boxes", out int boxes); tf.Schema.TryGetColumnIndex("detection_scores", out int scores); tf.Schema.TryGetColumnIndex("num_detections", out int num); tf.Schema.TryGetColumnIndex("detection_classes", out int classes); using (var curs = tf.GetRowCursor(tf.Schema["image_tensor"], tf.Schema["detection_boxes"], tf.Schema["detection_scores"], tf.Schema["detection_classes"], tf.Schema["num_detections"])) { var getInput = curs.GetGetter <VBuffer <byte> >(input); var getBoxes = curs.GetGetter <VBuffer <float> >(boxes); var getScores = curs.GetGetter <VBuffer <float> >(scores); var getNum = curs.GetGetter <VBuffer <float> >(num); var getClasses = curs.GetGetter <VBuffer <float> >(classes); var buffer = default(VBuffer <float>); var inputBuffer = default(VBuffer <byte>); while (curs.MoveNext()) { getInput(ref inputBuffer); getBoxes(ref buffer); getScores(ref buffer); getNum(ref buffer); getClasses(ref buffer); } } }
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() { Columns = new[] { new TextLoader.Column("ImagePath", DataKind.TX, 0), new TextLoader.Column("Name", DataKind.TX, 1), } }, new MultiFileSource(dataFile)); var images = new ImageLoaderTransformer(env, imageFolder, ("ImageReal", "ImagePath")).Transform(data); var cropped = new ImageResizerTransformer(env, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images); var pixels = new ImagePixelExtractorTransformer(env, "ImagePixels", "ImageCropped").Transform(cropped); IDataView backToBitmaps = new VectorToImageTransform(env, new VectorToImageTransform.Arguments() { InterleaveArgb = false, Columns = new VectorToImageTransform.Column[1] { new VectorToImageTransform.Column() { Name = "ImageRestored", Source = "ImagePixels", 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(); } }