private static void DumpNetwork(InferenceEngineNetwork network)
{
Console.WriteLine($"Network name: {network.NetworkName}");
DumpShape(network);
DumpInputs(network);
DumpOutputs(network);
}
public static void Main()
{
try
{
//using var bitmap = (System.Drawing.Bitmap)System.Drawing.Image.FromFile(ImageName);
using var image = Image.Load <Rgb24>(ImageName);
Console.WriteLine($"image: {image}");
if (image.TryGetSinglePixelSpan(out var pixelSpan))
{
DumpVersion();
using var core = new InferenceEngineCore();
DumpCoreInformation(core);
using var network = new InferenceEngineNetwork(core, NetworkName);
DumpNetwork(network);
var mainInputName = network.GetInputName(0);
var mainOutputName = network.GetOutputName(0);
network.SetInputResizeAlgorithm(mainInputName, resize_alg_e.RESIZE_BILINEAR);
network.SetInputLayout(mainInputName, layout_e.NCHW);
network.SetInputPrecision(mainInputName, precision_e.U8);
Console.WriteLine("Create executable network");
using var executableNetwork = new InferenceEngineExecutableNetwork(network, "GPU");
Console.WriteLine("Create request");
using var request = new InferenceEngineRequest(executableNetwork);
var imageDimensions = new dimensions_t(1, 3, image.Height, image.Width);
var tensorDescription = new tensor_desc_t(layout_e.NHWC, imageDimensions, precision_e.U8);
Console.WriteLine("Create blob");
using var inputBlob = new Blob(tensorDescription, MemoryMarshal.Cast <Rgb24, byte>(pixelSpan));
request.SetBlob(mainInputName, inputBlob);
for (var i = 0; i < 10; i++)
{
Console.WriteLine($"Infer {i}");
request.Infer();
Console.WriteLine($"Infer {i} done");
}
using var outputBlob = request.GetBlob(mainOutputName);
Console.WriteLine($"Output blob. Sizes = {outputBlob.Size} {outputBlob.ByteSize}. [{outputBlob.Layout} {outputBlob.Precision}] {outputBlob.Dimensions}");
}
}
catch (Exception ex)
{
Console.Error.WriteLine($"Error: {ex.Message}");
}
}
private static void DumpShape(InferenceEngineNetwork network)
{
var inputShapes = network.GetInputShapes();
for (var i = 0; i < inputShapes.Length; i++)
{
var shape = inputShapes[i];
Console.WriteLine($"Input shape[{i}] = {shape.Name} {shape.Dimensions}");
}
}
public Detector(InferenceEngineCore core, InferenceEngineNetwork network)
{
Core = core;
_network = network;
var mainInputName = network.GetInputName(0);
var inputDimensions = network.GetInputDimensions(mainInputName);
C = (int)inputDimensions[1];
H = (int)inputDimensions[2];
W = (int)inputDimensions[3];
FrameSize = C * H * W;
}
private static void DumpOutputs(InferenceEngineNetwork network)
{
var numberOfOutputs = network.NumberOfOutputs;
for (var i = 0; i < numberOfOutputs; i++)
{
var name = network.GetOutputName(i);
var precision = network.GetOutputPrecision(name);
var layout = network.GetOutputLayout(name);
var dimensions = network.GetOutputDimensions(name);
Console.WriteLine($"Output[{i}] = {name} [{precision} {layout}] {dimensions}");
}
}
public Detector(string networkName)
{
var core = new InferenceEngineCore();
try
{
_network = new InferenceEngineNetwork(core, networkName);
Core = core;
}
catch (Exception)
{
core.Dispose();
throw;
}
}
public static async Task <Detector> LoadAsync(InferenceEngineCore core, string networkName, CancellationToken ct)
{
var network = await InferenceEngineNetwork.LoadAsync(core, networkName, ct : ct);
return(new Detector(core, network));
}
