/// <summary>
/// Create a worker that will execute `asset` using the best backend that is available for a given `device` type.
/// This is just a convenience function that internally calls `ModelLoader.Load` followed by ``WorkerFactory.CreateWorker`.
/// `asset` is the associated NNModel asset.
/// `additionalOutputs` are the additional outputs to track but not directly specified by the model.
/// `trimOutputs` are the outputs not discard even if they are specified by the model.
/// `device` is the device type to run worker on. For example `WorkerFactory.Device.GPU` specifies the fast GPU path.
/// `verbose` will log scheduling of layers execution to the console (default == false).
/// </summary>
public static IWorker CreateWorker(this NNModel asset,
string[] additionalOutputs, string[] trimOutputs, WorkerFactory.Device device = WorkerFactory.Device.Auto, bool verbose = false)
{
var model = ModelLoader.Load(asset);
return(model.CreateWorker(additionalOutputs, trimOutputs, device, verbose));
}
/// <summary>
/// Create a worker that will execute `asset` using the best backend that is available for a given `device` type.
/// This is just a convenience function that internally calls `ModelLoader.Load` followed by ``WorkerFactory.CreateWorker`.
/// `asset` is the associated NNModel asset.
/// `device` is the preferred device for execution. For example `WorkerFactory.Device.GPU` specifies the fast GPU path.
/// `verbose` will log scheduling of layers execution to the console.
/// </summary>
public static IWorker CreateWorker(this NNModel asset,
WorkerFactory.Device device = WorkerFactory.Device.Auto, bool verbose = false)
{
var model = ModelLoader.Load(asset);
return(model.CreateWorker(device, verbose));
}
/// <summary>
/// Scripted importer callback
/// </summary>
/// <param name="ctx">Asset import context</param>
public override void OnImportAsset(AssetImportContext ctx)
{
var converter = new ONNXModelConverter(optimizeModel, treatErrorsAsWarnings, forceArbitraryBatchSize, importMode);
var model = converter.Convert(ctx.assetPath);
NNModelData assetData = ScriptableObject.CreateInstance <NNModelData>();
using (var memoryStream = new MemoryStream())
using (var writer = new BinaryWriter(memoryStream))
{
ModelWriter.Save(writer, model);
assetData.Value = memoryStream.ToArray();
}
assetData.name = "Data";
assetData.hideFlags = HideFlags.HideInHierarchy;
NNModel asset = ScriptableObject.CreateInstance <NNModel>();
asset.modelData = assetData;
ctx.AddObjectToAsset("main obj", asset, LoadIconTexture());
ctx.AddObjectToAsset("model data", assetData);
ctx.SetMainObject(asset);
}
/// <summary>
/// Return an object oriented representation (aka: `Model`) of a neural network from a binary representation of type `NNModel`.
/// By default details are not logged to the console, set `verbose` to true to see loading details.
/// </summary>
/// <param name="nnModel">binary representation of model</param>
/// <param name="model">object-oriented representation of model (must initialize before calling method)</param>
/// <param name="verbose">verbose</param>
/// <param name="skipWeights">skip loading weights (fast loading, metadata only)</param>
/// <param name="maxTimePerYield">the maximum amount of time to spend between in computation before yielding</param>
/// <returns>IEnumerator (use with StartCoroutine)</returns>
public static IEnumerator LoadAsync(NNModel nnModel, Model model, bool verbose = false, bool skipWeights = false, float maxTimePerYield = 0.01f)
{
Assert.IsNotNull(model);
var enumerator = LoadAsync(Open(nnModel.modelData.Value), model, verbose, true, skipWeights, maxTimePerYield);
while (enumerator.MoveNext())
{
model = (Model)enumerator.Current;
if (model != null)
{
yield return(null);
}
}
}
/// <summary>
/// Scripted importer callback
/// </summary>
/// <param name="ctx">Asset import context</param>
public override void OnImportAsset(AssetImportContext ctx)
{
ONNXModelConverter.ModelImported += BarracudaAnalytics.SendBarracudaImportEvent;
var converter = new ONNXModelConverter(optimizeModel, treatErrorsAsWarnings, forceArbitraryBatchSize, importMode);
var model = converter.Convert(ctx.assetPath);
if (weightsTypeMode == ONNXModelConverter.DataTypeMode.ForceHalf)
{
model.ConvertWeights(DataType.Half);
}
else if (weightsTypeMode == ONNXModelConverter.DataTypeMode.ForceFloat)
{
model.ConvertWeights(DataType.Float);
}
NNModelData assetData = ScriptableObject.CreateInstance <NNModelData>();
using (var memoryStream = new MemoryStream())
using (var writer = new BinaryWriter(memoryStream))
{
ModelWriter.Save(writer, model);
assetData.Value = memoryStream.ToArray();
}
assetData.name = "Data";
assetData.hideFlags = HideFlags.HideInHierarchy;
NNModel asset = ScriptableObject.CreateInstance <NNModel>();
asset.modelData = assetData;
ctx.AddObjectToAsset("main obj", asset, LoadIconTexture());
ctx.AddObjectToAsset("model data", assetData);
ctx.SetMainObject(asset);
}
/// <summary>
/// Return an object oriented representation (aka: `Model`) of a neural network from a binary representation of type `NNModel`.
/// By default details are not logged to the console, set `verbose` to true to see loading details.
/// </summary>
/// <param name="model">model</param>
/// <param name="verbose">verbose</param>
/// <param name="skipWeights">skip loading weights (fast loading, metadata only)</param>
/// <returns>loaded Model</returns>
public static Model Load(NNModel model, bool verbose = false, bool skipWeights = false)
{
return(Load(model.modelData.Value, verbose, skipWeights));
}
