/// <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>
/// 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);
}
public void InitializeObservations(List <ISensor> sensors, ITensorAllocator allocator)
{
// Loop through the sensors on a representative agent.
// All vector observations use a shared ObservationGenerator since they are concatenated.
// All other observations use a unique ObservationInputGenerator
var visIndex = 0;
ObservationGenerator vecObsGen = null;
for (var sensorIndex = 0; sensorIndex < sensors.Count; sensorIndex++)
{
var sensor = sensors[sensorIndex];
var shape = sensor.GetObservationShape();
var rank = shape.Length;
ObservationGenerator obsGen = null;
string obsGenName = null;
switch (rank)
{
case 1:
if (vecObsGen == null)
{
vecObsGen = new ObservationGenerator(allocator);
}
obsGen = vecObsGen;
obsGenName = TensorNames.VectorObservationPlaceholder;
break;
case 2:
// If the tensor is of rank 2, we use the index of the sensor
// to create the name
obsGen = new ObservationGenerator(allocator);
obsGenName = TensorNames.GetObservationName(sensorIndex);
break;
case 3:
// If the tensor is of rank 3, we use the "visual observation
// index", which only counts the rank 3 sensors
obsGen = new ObservationGenerator(allocator);
obsGenName = TensorNames.GetVisualObservationName(visIndex);
visIndex++;
break;
default:
throw new UnityAgentsException(
$"Sensor {sensor.GetName()} have an invalid rank {rank}");
}
obsGen.AddSensorIndex(sensorIndex);
m_Dict[obsGenName] = obsGen;
}
}
/// <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);
}