/// <summary>
/// Factory for the ModelParamLoader : Creates a ModelParamLoader and runs the checks
/// on it.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="sensors">Attached sensor components</param>
/// <param name="actuatorComponents">Attached actuator components</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <param name="behaviorType">BehaviorType or the Agent to check.</param>
/// <returns>A IEnumerable of the checks that failed</returns>
public static IEnumerable <FailedCheck> CheckModel(
Model model,
BrainParameters brainParameters,
ISensor[] sensors,
ActuatorComponent[] actuatorComponents,
int observableAttributeTotalSize = 0,
BehaviorType behaviorType = BehaviorType.Default
)
{
List <FailedCheck> failedModelChecks = new List <FailedCheck>();
if (model == null)
{
var errorMsg = "There is no model for this Brain; cannot run inference. ";
if (behaviorType == BehaviorType.InferenceOnly)
{
errorMsg += "Either assign a model, or change to a different Behavior Type.";
}
else
{
errorMsg += "(But can still train)";
}
failedModelChecks.Add(FailedCheck.Info(errorMsg));
return(failedModelChecks);
}
var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks);
if (!hasExpectedTensors)
{
return(failedModelChecks);
}
var modelApiVersion = model.GetVersion();
if (modelApiVersion < (int)ModelApiVersion.MinSupportedVersion || modelApiVersion > (int)ModelApiVersion.MaxSupportedVersion)
{
failedModelChecks.Add(
FailedCheck.Warning($"Version of the trainer the model was trained with ({modelApiVersion}) " +
$"is not compatible with the current range of supported versions: " +
$"({(int)ModelApiVersion.MinSupportedVersion} to {(int)ModelApiVersion.MaxSupportedVersion}).")
);
return(failedModelChecks);
}
var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memorySize == -1)
{
failedModelChecks.Add(FailedCheck.Warning($"Missing node in the model provided : {TensorNames.MemorySize}"
));
return(failedModelChecks);
}
if (modelApiVersion == (int)ModelApiVersion.MLAgents1_0)
{
failedModelChecks.AddRange(
CheckInputTensorPresenceLegacy(model, brainParameters, memorySize, sensors)
);
failedModelChecks.AddRange(
CheckInputTensorShapeLegacy(model, brainParameters, sensors, observableAttributeTotalSize)
);
}
else if (modelApiVersion == (int)ModelApiVersion.MLAgents2_0)
{
failedModelChecks.AddRange(
CheckInputTensorPresence(model, brainParameters, memorySize, sensors)
);
failedModelChecks.AddRange(
CheckInputTensorShape(model, brainParameters, sensors, observableAttributeTotalSize)
);
}
failedModelChecks.AddRange(
CheckOutputTensorShape(model, brainParameters, actuatorComponents)
);
failedModelChecks.AddRange(
CheckOutputTensorPresence(model, memorySize)
);
return(failedModelChecks);
}
/// <summary>
/// Factory for the ModelParamLoader : Creates a ModelParamLoader and runs the checks
/// on it.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="sensors">Attached sensor components</param>
/// <param name="actuatorComponents">Attached actuator components</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <param name="behaviorType">BehaviorType or the Agent to check.</param>
/// <param name="deterministicInference"> Inference only: set to true if the action selection from model should be
/// deterministic. </param>
/// <returns>A IEnumerable of the checks that failed</returns>
public static IEnumerable <FailedCheck> CheckModel(
Model model,
BrainParameters brainParameters,
ISensor[] sensors,
ActuatorComponent[] actuatorComponents,
int observableAttributeTotalSize = 0,
BehaviorType behaviorType = BehaviorType.Default,
bool deterministicInference = false
)
{
List <FailedCheck> failedModelChecks = new List <FailedCheck>();
if (model == null)
{
var errorMsg = "There is no model for this Brain; cannot run inference. ";
if (behaviorType == BehaviorType.InferenceOnly)
{
errorMsg += "Either assign a model, or change to a different Behavior Type.";
}
else
{
errorMsg += "(But can still train)";
}
failedModelChecks.Add(FailedCheck.Info(errorMsg));
return(failedModelChecks);
}
var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks, deterministicInference);
if (!hasExpectedTensors)
{
return(failedModelChecks);
}
var modelApiVersion = model.GetVersion();
var versionCheck = CheckModelVersion(model);
if (versionCheck != null)
{
failedModelChecks.Add(versionCheck);
}
var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memorySize == -1)
{
failedModelChecks.Add(FailedCheck.Warning($"Missing node in the model provided : {TensorNames.MemorySize}"
));
return(failedModelChecks);
}
if (modelApiVersion == (int)ModelApiVersion.MLAgents1_0)
{
failedModelChecks.AddRange(
CheckInputTensorPresenceLegacy(model, brainParameters, memorySize, sensors)
);
failedModelChecks.AddRange(
CheckInputTensorShapeLegacy(model, brainParameters, sensors, observableAttributeTotalSize)
);
}
else if (modelApiVersion == (int)ModelApiVersion.MLAgents2_0)
{
failedModelChecks.AddRange(
CheckInputTensorPresence(model, brainParameters, memorySize, sensors, deterministicInference)
);
failedModelChecks.AddRange(
CheckInputTensorShape(model, brainParameters, sensors, observableAttributeTotalSize)
);
}
failedModelChecks.AddRange(
CheckOutputTensorShape(model, brainParameters, actuatorComponents)
);
failedModelChecks.AddRange(
CheckOutputTensorPresence(model, memorySize, deterministicInference)
);
return(failedModelChecks);
}