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 void TensorFlowTransformInceptionTest()
{
var model_location = @"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 = ImageLoaderTransform.Create(mlContext, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(mlContext, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = 224, ImageWidth = 224, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(mlContext, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "input", UseAlpha = false, InterleaveArgb = true, Convert = true
}
}
}, cropped);
var tf = TensorFlowTransform.Create(mlContext, pixels, model_location, new[] { "softmax2_pre_activation" }, new[] { "input" });
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 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 TensorFlowTransformObjectDetectionTest()
{
var model_location = @"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 = ImageLoaderTransform.Create(mlContext, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(mlContext, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = 32, ImageWidth = 32, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(mlContext, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "image_tensor", UseAlpha = false, InterleaveArgb = true, Convert = false
}
}
}, cropped);
var tf = TensorFlowTransform.Create(mlContext, pixels, model_location,
new[] { "detection_boxes", "detection_scores", "num_detections", "detection_classes" },
new[] { "image_tensor" });
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(col => col == classes || col == num || col == scores || col == boxes || col == input))
{
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);
}
}
}
