static void Main(string[] args)
{
//create a machine learning context
var mlContext = new MLContext();
//load the tsv file with data
var data = mlContext.Data.LoadFromTextFile <ImageNetData>("./images/tags.tsv", hasHeader: true);
var pipeline = mlContext.Transforms
.LoadImages(
outputColumnName: "input",
imageFolder: "images",
inputColumnName: nameof(ImageNetData.ImagePath))
.Append(mlContext.Transforms
.ResizeImages(
outputColumnName: "input",
imageWidth: 224,
imageHeight: 224,
inputColumnName: "input"))
.Append(mlContext.Transforms.ExtractPixels(
outputColumnName: "input",
interleavePixelColors: true,
offsetImage: 117))
.Append(mlContext.Model.LoadTensorFlowModel("./models/tensorflow_inception_graph.pb")
.ScoreTensorFlowModel(
outputColumnNames: new[] { "softmax2" },
inputColumnNames: new[] { "input" },
addBatchDimensionInput: true));
Console.WriteLine("Start training model...");
var model = pipeline.Fit(data);
Console.WriteLine("Model training complete");
var engine = mlContext.Model.CreatePredictionEngine <ImageNetData, ImageNetPrediction>(model);
var labels = File.ReadAllLines("models/imagenet_comp_graph_label_strings.txt");
Console.WriteLine("Predicting image contents...");
var images = ImageNetData.ReadFromCsv("images/tags.tsv");
foreach (var image in images)
{
Console.Write($" [{image.ImagePath}]: ");
var prediction = engine.Predict(image).PredictLabels;
var i = 0;
var best = (from p in prediction
select new { Index = i++, Prediction = p })
.OrderByDescending(p => p.Prediction)
.First();
var predictedLabel = labels[best.Index];
Console.WriteLine($"{predictedLabel} {(predictedLabel != image.Label ? "**WRONG**" : "")}");
}
}
static void Main(string[] args)
{
// create a machine learning context
var mlContext = new MLContext();
// load the TSV file with image names and corresponding labels
var data = mlContext.Data.LoadFromTextFile <ImageNetData>("images/tags.tsv", hasHeader: true);
// set up a learning pipeline
var pipeline = mlContext.Transforms
// step 1: load the images
.LoadImages(
outputColumnName: "input",
imageFolder: "images",
inputColumnName: nameof(ImageNetData.ImagePath))
// step 2: resize the images to 224x224
.Append(mlContext.Transforms.ResizeImages(
outputColumnName: "input",
imageWidth: 224,
imageHeight: 224,
inputColumnName: "input"))
// step 3: extract pixels in a format the TF model can understand
// these interleave and offset values are identical to the images the model was trained on
.Append(mlContext.Transforms.ExtractPixels(
outputColumnName: "input",
interleavePixelColors: true,
offsetImage: 117))
// step 4: load the TensorFlow model
.Append(mlContext.Model.LoadTensorFlowModel("models/tensorflow_inception_graph.pb")
// step 5: score the images using the TF model
.ScoreTensorFlowModel(
outputColumnNames: new[] { "softmax2" },
inputColumnNames: new[] { "input" },
addBatchDimensionInput: true));
// train the model on the data file
Console.WriteLine("Start training model....");
var model = pipeline.Fit(data);
Console.WriteLine("Model training complete!");
// create a prediction engine
var engine = mlContext.Model.CreatePredictionEngine <ImageNetData, ImageNetPrediction>(model);
// load all imagenet labels
var labels = File.ReadAllLines("models/imagenet_comp_graph_label_strings.txt");
// predict what is in each image
Console.WriteLine("Predicting image contents....");
var images = ImageNetData.ReadFromCsv("images/tags.tsv");
foreach (var image in images)
{
Console.Write($" [{image.ImagePath}]: ");
var prediction = engine.Predict(image).PredictedLabels;
// find the best prediction
var i = 0;
var best = (from p in prediction
select new { Index = i++, Prediction = p }).OrderByDescending(p => p.Prediction).First();
var predictedLabel = labels[best.Index];
// show the corresponding label
Console.WriteLine($"{predictedLabel} {(predictedLabel != image.Label ? "***WRONG***" : "")} - {best.Prediction}");
}
}
