public override int Run(string[] remainingArguments)
{
var trainable = YOLO.CreateV4Trainable(inputSize: this.InputSize,
classCount: this.ClassCount,
strides: this.Strides);
trainable.load_weights(this.WeigthsPath);
var output = YOLOv4.Output.Get(trainable);
Tensor input = trainable.input_dyn;
var savable = YOLO.CreateSaveable(inputSize: this.InputSize, input, output,
classCount: this.ClassCount,
strides: this.Strides,
anchors: tf.constant(this.Anchors),
xyScale: YOLOv4.XYScale,
scoreThreshold: this.ScoreThreshold);
savable.summary();
savable.save(this.OutputPath, save_format: "tf", include_optimizer: false);
return(0);
}
private void openPic_Click(object sender, EventArgs e)
{
if (this.openPicDialog.ShowDialog(this) != DialogResult.OK)
{
return;
}
using var image = Image.Load <Rgb24>(this.openPicDialog.FileName);
var timer = Stopwatch.StartNew();
ObjectDetectionResult[] detections = YOLO.Detect(this.infer,
supportedSize: new Size(MS_COCO.InputSize, MS_COCO.InputSize),
image: image);
timer.Stop();
image.Mutate(context => {
var font = SystemFonts.CreateFont("Arial", 16);
var textColor = Color.White;
var boxPen = new Pen(Color.White, width: 4);
foreach (var detection in detections)
{
string className = detection.Class < MS_COCO.ClassCount && detection.Class >= 0
? MS_COCO.ClassNames[detection.Class] : "imaginary class";
string text = $"{className}: {detection.Score:P0}";
var box = Scale(detection.Box, image.Size());
context.DrawText(text, font, textColor, TopLeft(box));
var drawingBox = new RectangularPolygon(box);
context.Draw(boxPen, drawingBox);
}
});
using var temp = new MemoryStream();
image.SaveAsBmp(temp);
temp.Position = 0;
this.pictureBox.Image = new System.Drawing.Bitmap(temp);
this.Text = "YOLO " + string.Join(", ", detections.Select(d => MS_COCO.ClassNames[d.Class]))
+ " in " + timer.ElapsedMilliseconds + "ms";
}
public Tensor call(IEnumerable <IGraphNodeBase> trainableOutputs)
{
var output = trainableOutputs.ToArray();
var loss = YOLO.Loss.Zero;
for (int scaleIndex = 0; scaleIndex < this.strides.Length; scaleIndex++)
{
IGraphNodeBase conv = output[scaleIndex * 2];
IGraphNodeBase pred = output[scaleIndex * 2 + 1];
loss += YOLO.ComputeLoss((Tensor)pred, (Tensor)conv,
targetLabels: this.trueLabels[scaleIndex],
targetBBoxes: this.trueBoxes[scaleIndex],
strideSize: this.strides[scaleIndex],
classCount: this.classCount,
intersectionOverUnionLossThreshold: YOLO.DefaultIntersectionOverUnionLossThreshold);
}
this.add_loss(loss.Conf);
this.add_loss(loss.GIUO);
this.add_loss(loss.Prob);
return(loss.Conf + loss.GIUO + loss.Prob);
}
public override int Run(string[] remainingArguments)
{
Trace.Listeners.Add(new ConsoleTraceListener(useErrorStream: true));
tf.debugging.set_log_device_placement(this.LogDevicePlacement);
if (this.GpuAllowGrowth)
{
dynamic config = config_pb2.ConfigProto.CreateInstance();
config.gpu_options.allow_growth = true;
tf.keras.backend.set_session(Session.NewDyn(config: config));
}
if (this.TestRun)
{
this.Annotations = this.Annotations.Take(this.BatchSize * 3).ToArray();
}
var dataset = new ObjectDetectionDataset(this.Annotations,
classNames: this.ClassNames,
strides: this.Strides,
inputSize: this.InputSize,
anchors: this.Anchors,
anchorsPerScale: this.AnchorsPerScale,
maxBBoxPerScale: this.MaxBBoxPerScale);
var model = YOLO.CreateV4Trainable(dataset.InputSize, dataset.ClassNames.Length, dataset.Strides);
var learningRateSchedule = new YOLO.LearningRateSchedule(
totalSteps: (long)(this.FirstStageEpochs + this.SecondStageEpochs) * dataset.BatchCount(this.BatchSize),
warmupSteps: this.WarmupEpochs * dataset.BatchCount(this.BatchSize));
// https://github.com/AlexeyAB/darknet/issues/1845
var optimizer = new Adam(learning_rate: learningRateSchedule, epsilon: 0.000001);
if (this.ModelSummary)
{
model.summary();
}
if (this.WeightsPath != null)
{
model.load_weights(this.WeightsPath);
}
var callbacks = new List <ICallback> {
new LearningRateLogger(),
new TensorBoard(log_dir: this.LogDir, batch_size: this.BatchSize, profile_batch: 4),
};
if (!this.Benchmark && !this.TestRun)
{
callbacks.Add(new ModelCheckpoint("yoloV4.weights.{epoch:02d}", save_weights_only: true));
}
YOLO.TrainGenerator(model, optimizer, dataset, batchSize: this.BatchSize,
firstStageEpochs: this.FirstStageEpochs,
secondStageEpochs: this.SecondStageEpochs,
callbacks: callbacks);
if (!this.Benchmark && !this.TestRun)
{
model.save_weights("yoloV4.weights-trained");
}
// the following does not work due to the need to name layers properly
// https://stackoverflow.com/questions/61402903/unable-to-create-group-name-already-exists
// model.save("yoloV4-trained");
return(0);
}
