/// <summary>
/// Initializes a new instance of the <see cref="TinyYoloV2OnnxModelRunner"/> class.
/// </summary>
/// <param name="pipeline">The pipeline to add the component to.</param>
/// <param name="modelFileName">The name of the model.</param>
/// <param name="gpuDeviceId">The GPU device ID to run execution on, or null to run on CPU.</param>
/// <remarks>
/// To run on a GPU, use the Microsoft.Psi.Onnx.ModelRunners.Gpu library instead of Microsoft.Psi.Onnx.ModelRunners.Cpu, and set
/// the value of the <pararef name="gpuDeviceId"/> parameter to a valid non-negative integer. Typical device ID values are 0 or 1.
/// </remarks>
public TinyYoloV2OnnxModelRunner(Pipeline pipeline, string modelFileName, int?gpuDeviceId = null)
: base(pipeline)
{
// create an ONNX model, with a configuration that matches the structure
// of the Tiny Yolo V2 model
this.onnxModel = new OnnxModel(new OnnxModelConfiguration()
{
ModelFileName = modelFileName,
InputVectorName = "image",
InputVectorSize = 3 * 416 * 416,
OutputVectorName = "grid",
GpuDeviceId = gpuDeviceId,
});
}
/// <summary>
/// Initializes a new instance of the <see cref="ImageNetModelRunner"/> class.
/// </summary>
/// <param name="pipeline">The pipeline to add the component to.</param>
/// <param name="configuration">The configuration for the compoinent.</param>
/// <remarks>
/// To run on a GPU, use the Microsoft.Psi.Onnx.ModelRunners.Gpu library instead of Microsoft.Psi.Onnx.ModelRunners.Cpu, and set
/// the value of the <pararef name="gpuDeviceId"/> parameter to a valid non-negative integer. Typical device ID values are 0 or 1.
/// </remarks>
public ImageNetModelRunner(Pipeline pipeline, ImageNetModelRunnerConfiguration configuration)
: base(pipeline)
{
// create an ONNX model based on the supplied ImageNet model runner configuration
this.onnxModel = new OnnxModel(new OnnxModelConfiguration()
{
ModelFileName = configuration.ModelFilePath,
InputVectorName = configuration.InputVectorName,
InputVectorSize = 3 * 224 * 224,
OutputVectorName = configuration.OutputVectorName,
GpuDeviceId = configuration.GpuDeviceId,
});
this.outputParser = new ImageNetModelOutputParser(configuration.ImageClassesFilePath, configuration.NumberOfPredictions, configuration.ApplySoftmaxToModelOutput);
}
/// <summary>
/// Initializes a new instance of the <see cref="OnnxModelRunner"/> class, based on a given configuration.
/// </summary>
/// <param name="pipeline">The pipeline to add the component to.</param>
/// <param name="configuration">The component configuration.</param>
/// <param name="name">An optional name for the component.</param>
/// <remarks>The configuration parameter specifies the model filename, the
/// name of the input and output vectors in that ONNX model, as well as
/// the input vector size.</remarks>
public OnnxModelRunner(Pipeline pipeline, OnnxModelConfiguration configuration, string name = nameof(OnnxModelRunner))
: base(pipeline, name)
{
this.inputVectorSize = configuration.InputVectorSize;
this.onnxModel = new OnnxModel(configuration);
}
/// <summary>
/// Initializes a new instance of the <see cref="OnnxModelRunner"/> class, based on a given configuration.
/// </summary>
/// <param name="pipeline">The pipeline to add the component to.</param>
/// <param name="configuration">The component configuration.</param>
/// <remarks>The configuration parameter specifies the model filename, the
/// name of the input and output vectors in that ONNX model, as well as
/// the input vector size.</remarks>
public OnnxModelRunner(Pipeline pipeline, OnnxModelConfiguration configuration)
: base(pipeline)
{
this.inputVectorSize = configuration.InputVectorSize;
this.onnxModel = new OnnxModel(configuration);
}
