public static Model CreateV4Trainable(int inputSize, int classCount, ReadOnlySpan <int> strides)
{
if (inputSize <= 0)
{
throw new ArgumentOutOfRangeException(nameof(inputSize));
}
if (classCount <= 0)
{
throw new ArgumentOutOfRangeException(nameof(classCount));
}
Tensor input = tf.keras.Input(new TensorShape(inputSize, inputSize, 3), name: "image");
var featureMaps = YOLOv4.Apply(input, classCount: classCount);
var anchors = tf.constant(YOLOv4.Anchors);
var bboxTensors = new PythonList <Tensor>();
foreach (var(scaleIndex, featureMap) in Tools.Enumerate(featureMaps.SSBox, featureMaps.MBBox, featureMaps.LBBox))
{
int featuresOutputSize = (inputSize / 8) >> scaleIndex;
var bbox = DecodeTrain(featureMap, classCount: classCount,
outputSize: featuresOutputSize,
anchors: anchors, strides: strides,
scaleIndex: scaleIndex, xyScale: YOLOv4.XYScale);
bboxTensors.Add(featureMap);
bboxTensors.Add(bbox);
}
return(new Model(new { inputs = input, outputs = bboxTensors }.AsKwArgs()));
}
public static Model CreateRaw(int inputSize, int classCount)
{
Tensor input = tf.keras.Input(new TensorShape(inputSize, inputSize, 3));
var featureMaps = YOLOv4.Apply(input, classCount: classCount);
var featureMapTensors = new PythonList <Tensor> {
featureMaps.SSBox, featureMaps.MBBox, featureMaps.LBBox
};
return(new Model(new { inputs = input, outputs = featureMapTensors }.AsKwArgs()));
}
public static Model CreateSaveable(int inputSize, int classCount,
ReadOnlySpan <int> strides, Tensor <int> anchors,
ReadOnlySpan <float> xyScale,
float scoreThreshold)
{
Tensor input = tf.keras.Input(new TensorShape(inputSize, inputSize, 3));
var featureMaps = YOLOv4.Apply(input, classCount: classCount);
return(CreateSaveable(inputSize: inputSize, input: input, featureMaps,
classCount: classCount,
strides: strides, anchors: anchors, xyScale: xyScale,
scoreThreshold: scoreThreshold));
}
public static Model CreateV4EvalOnly(int inputSize, int classCount)
{
if (inputSize <= 0)
{
throw new ArgumentOutOfRangeException(nameof(inputSize));
}
if (classCount <= 0)
{
throw new ArgumentOutOfRangeException(nameof(classCount));
}
Tensor input = tf.keras.Input(new TensorShape(inputSize, inputSize, 3));
var featureMaps = YOLOv4.Apply(input, classCount: classCount);
var bboxTensors = new PythonList <Tensor>();
foreach (var featureMap in new[] { featureMaps.SSBox, featureMaps.MBBox, featureMaps.LBBox })
{
var bbox = DecodeEval(featureMap, classCount: classCount);
bboxTensors.Add(bbox);
}
return(new Model(new { inputs = input, outputs = bboxTensors }.AsKwArgs()));
}