/// <summary>
/// Generates failed checks that correspond to inputs shapes incompatibilities between
/// the model and the BrainParameters.
/// </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 sensors</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <returns>A IEnumerable of the checks that failed</returns>
static IEnumerable <FailedCheck> CheckInputTensorShape(
Model model, BrainParameters brainParameters, ISensor[] sensors,
int observableAttributeTotalSize)
{
var failedModelChecks = new List <FailedCheck>();
var tensorTester =
new Dictionary <string, Func <BrainParameters, TensorProxy, ISensor[], int, FailedCheck> >()
{
{ TensorNames.PreviousActionPlaceholder, CheckPreviousActionShape },
{ TensorNames.RandomNormalEpsilonPlaceholder, ((bp, tensor, scs, i) => null) },
{ TensorNames.ActionMaskPlaceholder, ((bp, tensor, scs, i) => null) },
{ TensorNames.SequenceLengthPlaceholder, ((bp, tensor, scs, i) => null) },
{ TensorNames.RecurrentInPlaceholder, ((bp, tensor, scs, i) => null) },
};
foreach (var mem in model.memories)
{
tensorTester[mem.input] = ((bp, tensor, scs, i) => null);
}
for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
{
var sens = sensors[sensorIndex];
if (sens.GetObservationSpec().Rank == 3)
{
tensorTester[TensorNames.GetObservationName(sensorIndex)] =
(bp, tensor, scs, i) => CheckVisualObsShape(tensor, sens);
}
if (sens.GetObservationSpec().Rank == 2)
{
tensorTester[TensorNames.GetObservationName(sensorIndex)] =
(bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sens);
}
if (sens.GetObservationSpec().Rank == 1)
{
tensorTester[TensorNames.GetObservationName(sensorIndex)] =
(bp, tensor, scs, i) => CheckRankOneObsShape(tensor, sens);
}
}
// If the model expects an input but it is not in this list
foreach (var tensor in model.GetInputTensors())
{
if (!tensorTester.ContainsKey(tensor.name))
{
failedModelChecks.Add(FailedCheck.Warning("Model contains an unexpected input named : " + tensor.name
));
}
else
{
var tester = tensorTester[tensor.name];
var error = tester.Invoke(brainParameters, tensor, sensors, observableAttributeTotalSize);
if (error != null)
{
failedModelChecks.Add(error);
}
}
}
return(failedModelChecks);
}
/// <summary>
/// Checks that the shape of the discrete action output is the same in the
/// model and in the Brain Parameters.
/// </summary>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="actuatorComponents">Array of attached actuator components.</param>
/// <param name="modelDiscreteBranches"> The Tensor of branch sizes.
/// </param>
/// <returns>
/// If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.
/// </returns>
static FailedCheck CheckDiscreteActionOutputShape(
BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, Tensor modelDiscreteBranches)
{
var discreteActionBranches = brainParameters.ActionSpec.BranchSizes.ToList();
foreach (var actuatorComponent in actuatorComponents)
{
var actionSpec = actuatorComponent.ActionSpec;
discreteActionBranches.AddRange(actionSpec.BranchSizes);
}
int modelDiscreteBranchesLength = modelDiscreteBranches?.length ?? 0;
if (modelDiscreteBranchesLength != discreteActionBranches.Count)
{
return(FailedCheck.Warning("Discrete Action Size of the model does not match. The BrainParameters expect " +
$"{discreteActionBranches.Count} branches but the model contains {modelDiscreteBranchesLength}."
));
}
for (int i = 0; i < modelDiscreteBranchesLength; i++)
{
if (modelDiscreteBranches[i] != discreteActionBranches[i])
{
return(FailedCheck.Warning($"The number of Discrete Actions of branch {i} does not match. " +
$"Was expecting {discreteActionBranches[i]} but the model contains {modelDiscreteBranches[i]} "
));
}
}
return(null);
}
/// <summary>
/// Checks that the shape of the rank 2 observation input placeholder is the same as the corresponding sensor.
/// </summary>
/// <param name="tensorProxy">The tensor that is expected by the model</param>
/// <param name="sensor">The sensor that produces the visual observation.</param>
/// <returns>
/// If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.
/// </returns>
static FailedCheck CheckRankOneObsShape(
TensorProxy tensorProxy, ISensor sensor)
{
var shape = sensor.GetObservationSpec().Shape;
var dim1Bp = shape[0];
var dim1T = tensorProxy.Channels;
var dim2T = tensorProxy.Width;
var dim3T = tensorProxy.Height;
if ((dim1Bp != dim1T))
{
var proxyDimStr = $"[?x{dim1T}]";
if (dim2T > 1)
{
proxyDimStr = $"[?x{dim1T}x{dim2T}]";
}
if (dim3T > 1)
{
proxyDimStr = $"[?x{dim3T}x{dim2T}x{dim1T}]";
}
return(FailedCheck.Warning($"An Observation of the model does not match. " +
$"Received TensorProxy of shape [?x{dim1Bp}] but " +
$"was expecting {proxyDimStr} for the {sensor.GetName()} Sensor."
));
}
return(null);
}
/// <summary>
/// Generates failed checks that correspond to outputs expected by the model that are not
/// present in the BrainParameters.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="memory">The memory size that the model is expecting/</param>
/// <returns>
/// A IEnumerable of the checks that failed
/// </returns>
static IEnumerable <FailedCheck> CheckOutputTensorPresence(Model model, int memory)
{
var failedModelChecks = new List <FailedCheck>();
// If there is no Recurrent Output but the model is Recurrent.
if (memory > 0)
{
var netHasMemories = false;
var modelVersion = model.GetVersion();
if (modelVersion < (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents2_0)
{
var memOutputs = model.memories.Select(x => x.output).ToList();
netHasMemories = memOutputs.Any(x => x.EndsWith("_h")) &&
memOutputs.Any(x => x.EndsWith("_c"));
}
else
{
var allOutputs = model.GetOutputNames().ToList();
netHasMemories = allOutputs.Any(x => x == TensorNames.RecurrentOutput);
}
if (!netHasMemories)
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Recurrent Output Node but has memory_size.")
);
}
}
return(failedModelChecks);
}
/// <summary>
/// Generates failed checks that correspond to inputs expected by the model that are not
/// present in the BrainParameters.
/// </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="memory">
/// The memory size that the model is expecting.
/// </param>
/// <param name="sensors">Array of attached sensor components</param>
/// <returns>
/// A IEnumerable of the checks that failed
/// </returns>
static IEnumerable <FailedCheck> CheckInputTensorPresence(
Model model,
BrainParameters brainParameters,
int memory,
ISensor[] sensors
)
{
var failedModelChecks = new List <FailedCheck>();
var tensorsNames = model.GetInputNames();
for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
{
if (!tensorsNames.Contains(
TensorNames.GetObservationName(sensorIndex)))
{
var sensor = sensors[sensorIndex];
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain an Observation Placeholder Input " +
$"for sensor component {sensorIndex} ({sensor.GetType().Name}).")
);
}
}
// If the model has a non-negative memory size but requires a recurrent input
if (memory > 0)
{
var modelVersion = model.GetVersion();
var netHasMemories = false;
if (modelVersion < (int)BarracudaModelParamLoader.ModelApiVersion.MLAgents2_0)
{
netHasMemories = tensorsNames.Any(x => x.EndsWith("_h")) &&
tensorsNames.Any(x => x.EndsWith("_c"));
}
else
{
netHasMemories = tensorsNames.Any(x => x == TensorNames.RecurrentInPlaceholder);
}
if (!netHasMemories)
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Recurrent Input Node but has memory_size.")
);
}
}
// If the model uses discrete control but does not have an input for action masks
if (model.HasDiscreteOutputs())
{
if (!tensorsNames.Contains(TensorNames.ActionMaskPlaceholder))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain an Action Mask but is using Discrete Control.")
);
}
}
return(failedModelChecks);
}
/// <summary>
/// Checks that the shape of the Previous Vector Action input placeholder is the same in the
/// model and in the Brain Parameters.
/// </summary>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="tensorProxy"> The tensor that is expected by the model</param>
/// <param name="sensors">Array of attached sensor components (unused).</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes (unused).</param>
/// <returns>If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.</returns>
static FailedCheck CheckPreviousActionShape(
BrainParameters brainParameters, TensorProxy tensorProxy,
ISensor[] sensors, int observableAttributeTotalSize)
{
var numberActionsBp = brainParameters.ActionSpec.NumDiscreteActions;
var numberActionsT = tensorProxy.shape[tensorProxy.shape.Length - 1];
if (numberActionsBp != numberActionsT)
{
return(FailedCheck.Warning("Previous Action Size of the model does not match. " +
$"Received {numberActionsBp} but was expecting {numberActionsT}."
));
}
return(null);
}
/// <summary>
/// Checks that the shape of the Vector Observation input placeholder is the same in the
/// model and in the Brain Parameters. Tests the models created with the API of version 1.X
/// </summary>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="tensorProxy">The tensor that is expected by the model</param>
/// <param name="sensors">Array of attached sensor components</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <returns>
/// If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.
/// </returns>
static FailedCheck CheckVectorObsShapeLegacy(
BrainParameters brainParameters, TensorProxy tensorProxy, ISensor[] sensors,
int observableAttributeTotalSize)
{
var vecObsSizeBp = brainParameters.VectorObservationSize;
var numStackedVector = brainParameters.NumStackedVectorObservations;
var totalVecObsSizeT = tensorProxy.shape[tensorProxy.shape.Length - 1];
var totalVectorSensorSize = 0;
foreach (var sens in sensors)
{
if ((sens.GetObservationSpec().Shape.Length == 1))
{
totalVectorSensorSize += sens.GetObservationSpec().Shape[0];
}
}
if (totalVectorSensorSize != totalVecObsSizeT)
{
var sensorSizes = "";
foreach (var sensorComp in sensors)
{
if (sensorComp.GetObservationSpec().Shape.Length == 1)
{
var vecSize = sensorComp.GetObservationSpec().Shape[0];
if (sensorSizes.Length == 0)
{
sensorSizes = $"[{vecSize}";
}
else
{
sensorSizes += $", {vecSize}";
}
}
}
sensorSizes += "]";
return(FailedCheck.Warning(
$"Vector Observation Size of the model does not match. Was expecting {totalVecObsSizeT} " +
$"but received: \n" +
$"Vector observations: {vecObsSizeBp} x {numStackedVector}\n" +
$"Total [Observable] attributes: {observableAttributeTotalSize}\n" +
$"Sensor sizes: {sensorSizes}."
));
}
return(null);
}
/// <summary>
/// Generates failed checks that correspond to outputs expected by the model that are not
/// present in the BrainParameters.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="memory">The memory size that the model is expecting/</param>
/// <returns>
/// A IEnumerable of the checks that failed
/// </returns>
static IEnumerable <FailedCheck> CheckOutputTensorPresence(Model model, int memory)
{
var failedModelChecks = new List <FailedCheck>();
// If there is no Recurrent Output but the model is Recurrent.
if (memory > 0)
{
var allOutputs = model.GetOutputNames().ToList();
if (!allOutputs.Any(x => x == TensorNames.RecurrentOutput))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Recurrent Output Node but has memory_size.")
);
}
}
return(failedModelChecks);
}
/// <summary>
/// Generates failed checks that correspond to outputs expected by the model that are not
/// present in the BrainParameters.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="memory">The memory size that the model is expecting/</param>
/// <returns>
/// A IEnumerable of the checks that failed
/// </returns>
static IEnumerable <FailedCheck> CheckOutputTensorPresence(Model model, int memory)
{
var failedModelChecks = new List <FailedCheck>();
// If there is no Recurrent Output but the model is Recurrent.
if (memory > 0)
{
var memOutputs = model.memories.Select(x => x.output).ToList();
if (!memOutputs.Any(x => x.EndsWith("_h")) ||
!memOutputs.Any(x => x.EndsWith("_c")))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Recurrent Output Node but has memory_size.")
);
}
}
return(failedModelChecks);
}
/// <summary>
/// Checks that the shape of the continuous action output is the same in the
/// model and in the Brain Parameters.
/// </summary>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="actuatorComponents">Array of attached actuator components.</param>
/// <param name="modelContinuousActionSize">
/// The size of the continuous action output that is expected by the model.
/// </param>
/// <returns>If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.</returns>
static FailedCheck CheckContinuousActionOutputShape(
BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, int modelContinuousActionSize)
{
var numContinuousActions = brainParameters.ActionSpec.NumContinuousActions;
foreach (var actuatorComponent in actuatorComponents)
{
var actionSpec = actuatorComponent.ActionSpec;
numContinuousActions += actionSpec.NumContinuousActions;
}
if (modelContinuousActionSize != numContinuousActions)
{
return(FailedCheck.Warning(
"Continuous Action Size of the model does not match. The BrainParameters and ActuatorComponents expect " +
$"{numContinuousActions} but the model contains {modelContinuousActionSize}."
));
}
return(null);
}
/// <summary>
/// Checks that the shape of the discrete action output is the same in the
/// model and in the Brain Parameters. Tests the models created with the API of version 1.X
/// </summary>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="actuatorComponents">Array of attached actuator components.</param>
/// <param name="modelSumDiscreteBranchSizes">
/// The size of the discrete action output that is expected by the model.
/// </param>
/// <returns>
/// If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.
/// </returns>
static FailedCheck CheckDiscreteActionOutputShapeLegacy(
BrainParameters brainParameters, ActuatorComponent[] actuatorComponents, int modelSumDiscreteBranchSizes)
{
// TODO: check each branch size instead of sum of branch sizes
var sumOfDiscreteBranchSizes = brainParameters.ActionSpec.SumOfDiscreteBranchSizes;
foreach (var actuatorComponent in actuatorComponents)
{
var actionSpec = actuatorComponent.ActionSpec;
sumOfDiscreteBranchSizes += actionSpec.SumOfDiscreteBranchSizes;
}
if (modelSumDiscreteBranchSizes != sumOfDiscreteBranchSizes)
{
return(FailedCheck.Warning("Discrete Action Size of the model does not match. The BrainParameters expect " +
$"{sumOfDiscreteBranchSizes} but the model contains {modelSumDiscreteBranchSizes}."
));
}
return(null);
}
/// <summary>
/// Checks that the shape of the visual observation input placeholder is the same as the corresponding sensor.
/// </summary>
/// <param name="tensorProxy">The tensor that is expected by the model</param>
/// <param name="sensor">The sensor that produces the visual observation.</param>
/// <returns>
/// If the Check failed, returns a string containing information about why the
/// check failed. If the check passed, returns null.
/// </returns>
static FailedCheck CheckVisualObsShape(
TensorProxy tensorProxy, ISensor sensor)
{
var shape = sensor.GetObservationSpec().Shape;
var heightBp = shape[0];
var widthBp = shape[1];
var pixelBp = shape[2];
var heightT = tensorProxy.Height;
var widthT = tensorProxy.Width;
var pixelT = tensorProxy.Channels;
if ((widthBp != widthT) || (heightBp != heightT) || (pixelBp != pixelT))
{
return(FailedCheck.Warning($"The visual Observation of the model does not match. " +
$"Received TensorProxy of shape [?x{widthBp}x{heightBp}x{pixelBp}] but " +
$"was expecting [?x{widthT}x{heightT}x{pixelT}] for the {sensor.GetName()} Sensor."
));
}
return(null);
}
/// <summary>
/// Factory for the ModelParamLoader : Creates a ModelParamLoader and runs the checks
/// on it.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <param name="brainParameters">
/// The BrainParameters that are used verify the compatibility with the InferenceEngine
/// </param>
/// <param name="sensors">Attached sensor components</param>
/// <param name="actuatorComponents">Attached actuator components</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <param name="behaviorType">BehaviorType or the Agent to check.</param>
/// <returns>A IEnumerable of the checks that failed</returns>
public static IEnumerable <FailedCheck> CheckModel(
Model model,
BrainParameters brainParameters,
ISensor[] sensors,
ActuatorComponent[] actuatorComponents,
int observableAttributeTotalSize = 0,
BehaviorType behaviorType = BehaviorType.Default
)
{
List <FailedCheck> failedModelChecks = new List <FailedCheck>();
if (model == null)
{
var errorMsg = "There is no model for this Brain; cannot run inference. ";
if (behaviorType == BehaviorType.InferenceOnly)
{
errorMsg += "Either assign a model, or change to a different Behavior Type.";
}
else
{
errorMsg += "(But can still train)";
}
failedModelChecks.Add(FailedCheck.Info(errorMsg));
return(failedModelChecks);
}
var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks);
if (!hasExpectedTensors)
{
return(failedModelChecks);
}
var modelApiVersion = model.GetVersion();
if (modelApiVersion < (int)ModelApiVersion.MinSupportedVersion || modelApiVersion > (int)ModelApiVersion.MaxSupportedVersion)
{
failedModelChecks.Add(
FailedCheck.Warning($"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>
/// Generates failed checks that correspond to inputs expected by the model that are not
/// present in the BrainParameters. Tests the models created with the API of version 1.X
/// </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="memory">
/// The memory size that the model is expecting.
/// </param>
/// <param name="sensors">Array of attached sensor components</param>
/// <returns>
/// A IEnumerable of the checks that failed
/// </returns>
static IEnumerable <FailedCheck> CheckInputTensorPresenceLegacy(
Model model,
BrainParameters brainParameters,
int memory,
ISensor[] sensors
)
{
var failedModelChecks = new List <FailedCheck>();
var tensorsNames = model.GetInputNames();
// If there is no Vector Observation Input but the Brain Parameters expect one.
if ((brainParameters.VectorObservationSize != 0) &&
(!tensorsNames.Contains(TensorNames.VectorObservationPlaceholder)))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Vector Observation Placeholder Input. " +
"You must set the Vector Observation Space Size to 0.")
);
}
// If there are not enough Visual Observation Input compared to what the
// sensors expect.
var visObsIndex = 0;
for (var sensorIndex = 0; sensorIndex < sensors.Length; sensorIndex++)
{
var sensor = sensors[sensorIndex];
if (sensor.GetObservationSpec().Shape.Length == 3)
{
if (!tensorsNames.Contains(
TensorNames.GetVisualObservationName(visObsIndex)))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Visual Observation Placeholder Input " +
$"for sensor component {visObsIndex} ({sensor.GetType().Name}).")
);
}
visObsIndex++;
}
if (sensor.GetObservationSpec().Shape.Length == 2)
{
if (!tensorsNames.Contains(
TensorNames.GetObservationName(sensorIndex)))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain an Observation Placeholder Input " +
$"for sensor component {sensorIndex} ({sensor.GetType().Name}).")
);
}
}
}
var expectedVisualObs = model.GetNumVisualInputs();
// Check if there's not enough visual sensors (too many would be handled above)
if (expectedVisualObs > visObsIndex)
{
failedModelChecks.Add(
FailedCheck.Warning($"The model expects {expectedVisualObs} visual inputs," +
$" but only found {visObsIndex} visual sensors.")
);
}
// If the model has a non-negative memory size but requires a recurrent input
if (memory > 0)
{
if (!tensorsNames.Any(x => x.EndsWith("_h")) ||
!tensorsNames.Any(x => x.EndsWith("_c")))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain a Recurrent Input Node but has memory_size.")
);
}
}
// If the model uses discrete control but does not have an input for action masks
if (model.HasDiscreteOutputs())
{
if (!tensorsNames.Contains(TensorNames.ActionMaskPlaceholder))
{
failedModelChecks.Add(
FailedCheck.Warning("The model does not contain an Action Mask but is using Discrete Control.")
);
}
}
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);
}