public async Task <MLModelVariable> EvaluateAsync(MLModelVariable input)
{
_binding.Bind("float_input", input.Variable);
var id = Guid.NewGuid().ToString();
var wait = _session.EvaluateAsync(_binding, id);
while (wait.Status != Windows.Foundation.AsyncStatus.Completed)
{
Thread.Sleep(100);
}
var result = wait.GetResults();
return(new MLModelVariable
{
Variable = result.Outputs["variable"] as TensorFloat
});
}
static async Task <int> Main(string[] args)
{
try
{
//
// Parse options
//
Options = new AppOptions();
Options.Parse(args);
if (Options.ShowList)
{
}
if (Options.Exit)
{
return(-1);
}
if (string.IsNullOrEmpty(Options.FileName))
{
throw new ApplicationException("Please use --file to specify which file to use");
}
//
// Init module client
//
if (Options.UseEdge)
{
Log.WriteLine($"{AppOptions.AppName} module starting.");
await BlockTimer("Initializing Azure IoT Edge", async() => await InitEdge());
}
cts = new CancellationTokenSource();
AssemblyLoadContext.Default.Unloading += (ctx) => cts.Cancel();
Console.CancelKeyPress += (sender, cpe) => cts.Cancel();
//
// Load model
//
MLModel model = null;
Console.WriteLine($"Loading model from: '{Options.ModelPath}', Exists: '{File.Exists(Options.ModelPath)}'");
await BlockTimer($"Loading modelfile '{Options.ModelPath}' on the {(Options.UseGpu ? "GPU" : "CPU")}",
async() =>
{
var d = Directory.GetCurrentDirectory();
var path = d + "\\" + Options.ModelPath;
StorageFile modelFile = await AsAsync(StorageFile.GetFileFromPathAsync(path));
model = await MLModel.CreateFromStreamAsync(modelFile);
});
do
{
//
// Open file
//
var rows = new List <DataRow>();
try
{
using (var fs = new StreamReader(Options.FileName))
{
// I just need this one line to load the records from the file in my List<CsvLine>
rows = new CsvHelper.CsvReader(fs).GetRecords <DataRow>().ToList();
Console.WriteLine($"Loaded {rows.Count} row(s)");
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
Console.WriteLine(rows);
//
// Main loop
//
foreach (var row in rows)
{
//
// Evaluate model
//
var inputShape = new long[2] {
1, 4
};
var inputFeatures = new float[4] {
row.Temperature, row.Pressure, row.Humidity, row.ExternalTemperature
};
MLModelVariable result = null;
var evalticks = await BlockTimer("Running the model",
async() =>
{
result = await model.EvaluateAsync(new MLModelVariable()
{
Variable = TensorFloat.CreateFromArray(inputShape, inputFeatures)
});
});
//
// Print results
//
var message = new MessageBody
{
result = result.Variable.GetAsVectorView().First()
};
message.metrics.evaltimeinms = evalticks;
var json = JsonConvert.SerializeObject(message);
Log.WriteLineRaw($"Recognized {json}");
//
// Send results to Edge
//
if (Options.UseEdge)
{
var eventMessage = new Message(Encoding.UTF8.GetBytes(json));
await ioTHubModuleClient.SendEventAsync("resultsOutput", eventMessage);
// Let's not totally spam Edge :)
await Task.Delay(500);
}
Console.WriteLine("Waiting 1 second...");
Thread.Sleep(1000);
}
}while (Options.RunForever && !cts.Token.IsCancellationRequested);
}
catch (Exception ex)
{
Console.WriteLine(ex);
return(-1);
}
return(0);
}
