private static void Move(IEnumerable <string> parameters)
{
var movableSensor = CurrentSensor as IMovable;
if (movableSensor != null)
{
if (parameters.Count() == 2)
{
try
{
float x, y;
x = float.Parse(parameters.First());
y = float.Parse(parameters.Skip(1).First());
movableSensor.Move(x, y);
Console.WriteLine($"Sensor {CurrentSensor.GetName()} moved to x={x} y={y}");
}
catch (FormatException)
{
Console.WriteLine($"Wrong floating-point format according to current culture. Try move {9.99F} {9.99F}");
}
}
else
{
Console.WriteLine($"Command move requires two parameters.\n\tSyntax: move {9.99F} {9.99F}");
}
}
else
{
Console.WriteLine("Please select a IMovable sensor to use the move function");
}
}
/// <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>
///
/// </summary>
/// <param name="wrapped">The wrapped sensor</param>
/// <param name="numStackedObservations">Number of stacked observations to keep</param>
public StackingSensor(ISensor wrapped, int numStackedObservations)
{
// TODO ensure numStackedObservations > 1
m_WrappedSensor = wrapped;
m_NumStackedObservations = numStackedObservations;
m_Name = $"StackingSensor_size{numStackedObservations}_{wrapped.GetName()}";
var shape = wrapped.GetObservationShape();
m_Shape = new int[shape.Length];
m_UnstackedObservationSize = wrapped.ObservationSize();
for (int d = 0; d < shape.Length; d++)
{
m_Shape[d] = shape[d];
}
// TODO support arbitrary stacking dimension
m_Shape[0] *= numStackedObservations;
m_StackedObservations = new float[numStackedObservations][];
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedObservations[i] = new float[m_UnstackedObservationSize];
}
}
public static EventObservationSpec FromSensor(ISensor sensor)
{
var obsSpec = sensor.GetObservationSpec();
var shape = obsSpec.Shape;
var dimProps = obsSpec.DimensionProperties;
var dimInfos = new EventObservationDimensionInfo[shape.Length];
for (var i = 0; i < shape.Length; i++)
{
dimInfos[i].Size = shape[i];
dimInfos[i].Flags = (int)dimProps[i];
}
var builtInSensorType =
(sensor as IBuiltInSensor)?.GetBuiltInSensorType() ?? Sensors.BuiltInSensorType.Unknown;
return(new EventObservationSpec
{
SensorName = sensor.GetName(),
CompressionType = sensor.GetCompressionSpec().SensorCompressionType.ToString(),
BuiltInSensorType = (int)builtInSensorType,
ObservationType = (int)obsSpec.ObservationType,
DimensionInfos = dimInfos,
});
}
/// <summary>
/// Initializes the sensor.
/// </summary>
/// <param name="wrapped">The wrapped sensor.</param>
/// <param name="numStackedObservations">Number of stacked observations to keep.</param>
public StackingSensor(ISensor wrapped, int numStackedObservations)
{
// TODO ensure numStackedObservations > 1
m_WrappedSensor = wrapped;
m_NumStackedObservations = numStackedObservations;
m_Name = $"StackingSensor_size{numStackedObservations}_{wrapped.GetName()}";
if (wrapped.GetCompressionType() != SensorCompressionType.None)
{
throw new UnityAgentsException("StackingSensor doesn't support compressed observations.'");
}
var shape = wrapped.GetObservationShape();
if (shape.Length != 1)
{
throw new UnityAgentsException("Only 1-D observations are supported by StackingSensor");
}
m_Shape = new int[shape.Length];
m_UnstackedObservationSize = wrapped.ObservationSize();
for (int d = 0; d < shape.Length; d++)
{
m_Shape[d] = shape[d];
}
// TODO support arbitrary stacking dimension
m_Shape[0] *= numStackedObservations;
m_StackedObservations = new float[numStackedObservations][];
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedObservations[i] = new float[m_UnstackedObservationSize];
}
}
/// <summary>
/// Adds information about sensor quality.
/// </summary>
/// <param name="sensor">Sensor instance</param>
/// <param name="result">Map of results</param>
private static void AddToResult(ISensor sensor, IDictionary <string, string> result)
{
if (sensor != null)
{
result.Add(
sensor.GetName(),
sensor.CalculateQuality()
);
}
}
/// <summary>
/// Initializes the sensor.
/// </summary>
/// <param name="wrapped">The wrapped sensor.</param>
/// <param name="numStackedObservations">Number of stacked observations to keep.</param>
public StackingSensor(ISensor wrapped, int numStackedObservations)
{
// TODO ensure numStackedObservations > 1
m_WrappedSensor = wrapped;
m_NumStackedObservations = numStackedObservations;
m_Name = $"StackingSensor_size{numStackedObservations}_{wrapped.GetName()}";
m_WrappedSpec = wrapped.GetObservationSpec();
m_UnstackedObservationSize = wrapped.ObservationSize();
// Set up the cached observation spec for the StackingSensor
var newShape = m_WrappedSpec.Shape;
// TODO support arbitrary stacking dimension
newShape[newShape.Length - 1] *= numStackedObservations;
m_ObservationSpec = new ObservationSpec(
newShape, m_WrappedSpec.DimensionProperties, m_WrappedSpec.ObservationType
);
// Initialize uncompressed buffer anyway in case python trainer does not
// support the compression mapping and has to fall back to uncompressed obs.
m_StackedObservations = new float[numStackedObservations][];
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedObservations[i] = new float[m_UnstackedObservationSize];
}
if (m_WrappedSensor.GetCompressionSpec().SensorCompressionType != SensorCompressionType.None)
{
m_StackedCompressedObservations = new byte[numStackedObservations][];
m_EmptyCompressedObservation = CreateEmptyPNG();
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedCompressedObservations[i] = m_EmptyCompressedObservation;
}
m_CompressionMapping = ConstructStackedCompressedChannelMapping(wrapped);
}
if (m_WrappedSpec.Rank != 1)
{
var wrappedShape = m_WrappedSpec.Shape;
m_tensorShape = new TensorShape(0, wrappedShape[0], wrappedShape[1], wrappedShape[2]);
}
}
public static EventObservationSpec FromSensor(ISensor sensor)
{
var shape = sensor.GetObservationShape();
var dimInfos = new EventObservationDimensionInfo[shape.Length];
for (var i = 0; i < shape.Length; i++)
{
dimInfos[i].Size = shape[i];
// TODO copy flags when we have them
}
return(new EventObservationSpec
{
SensorName = sensor.GetName(),
CompressionType = sensor.GetCompressionType().ToString(),
DimensionInfos = dimInfos,
});
}
/// <summary>
/// Initializes the sensor.
/// </summary>
/// <param name="wrapped">The wrapped sensor.</param>
/// <param name="numStackedObservations">Number of stacked observations to keep.</param>
public StackingSensor(ISensor wrapped, int numStackedObservations)
{
// TODO ensure numStackedObservations > 1
m_WrappedSensor = wrapped;
m_NumStackedObservations = numStackedObservations;
m_Name = $"StackingSensor_size{numStackedObservations}_{wrapped.GetName()}";
m_WrappedShape = wrapped.GetObservationShape();
m_Shape = new int[m_WrappedShape.Length];
m_UnstackedObservationSize = wrapped.ObservationSize();
for (int d = 0; d < m_WrappedShape.Length; d++)
{
m_Shape[d] = m_WrappedShape[d];
}
// TODO support arbitrary stacking dimension
m_Shape[m_Shape.Length - 1] *= numStackedObservations;
// Initialize uncompressed buffer anyway in case python trainer does not
// support the compression mapping and has to fall back to uncompressed obs.
m_StackedObservations = new float[numStackedObservations][];
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedObservations[i] = new float[m_UnstackedObservationSize];
}
if (m_WrappedSensor.GetCompressionType() != SensorCompressionType.None)
{
m_StackedCompressedObservations = new byte[numStackedObservations][];
m_EmptyCompressedObservation = CreateEmptyPNG();
for (var i = 0; i < numStackedObservations; i++)
{
m_StackedCompressedObservations[i] = m_EmptyCompressedObservation;
}
m_CompressionMapping = ConstructStackedCompressedChannelMapping(wrapped);
}
if (m_Shape.Length != 1)
{
m_tensorShape = new TensorShape(0, m_WrappedShape[0], m_WrappedShape[1], m_WrappedShape[2]);
}
}
private static void AdjustHeight(IEnumerable <string> parameters)
{
var heightAdjustableSensor = CurrentSensor as IHeightAdjustable;
if (heightAdjustableSensor == null)
{
Console.WriteLine($"Current sensor {CurrentSensor?.GetName() ?? "null"} is not height adjustable");
return;
}
if (parameters.Count() == 2)
{
var direction = parameters.First();
var height = parameters.Skip(1).First();
try
{
switch (direction)
{
case "raise":
heightAdjustableSensor.Raise(float.Parse(height));
Console.WriteLine($"{CurrentSensor.GetName()} raised.");
break;
case "lower":
heightAdjustableSensor.Lower(float.Parse(height));
Console.WriteLine($"{CurrentSensor.GetName()} lowered.");
break;
default:
Console.WriteLine("First parameter to height adjustment must be 'raise' or 'lower'");
break;
}
}
catch (FormatException)
{
Console.WriteLine($"Second parameter {height} is not a valid float according to current culture. Try {9.99F}");
}
}
else
{
Console.WriteLine($"Parameter mismatch.\n\tSyntax: adjustheight [raise|lower] {9.99F}");
}
}
public static EventObservationSpec FromSensor(ISensor sensor)
{
var shape = sensor.GetObservationShape();
var dimInfos = new EventObservationDimensionInfo[shape.Length];
for (var i = 0; i < shape.Length; i++)
{
dimInfos[i].Size = shape[i];
// TODO copy flags when we have them
}
var builtInSensorType =
(sensor as IBuiltInSensor)?.GetBuiltInSensorType() ?? Sensors.BuiltInSensorType.Unknown;
return(new EventObservationSpec
{
SensorName = sensor.GetName(),
CompressionType = sensor.GetCompressionType().ToString(),
BuiltInSensorType = (int)builtInSensorType,
DimensionInfos = dimInfos,
});
}
/// <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>
/// Generate an ObservationProto for the sensor using the provided ObservationWriter.
/// This is equivalent to producing an Observation and calling Observation.ToProto(),
/// but avoid some intermediate memory allocations.
/// </summary>
/// <param name="sensor"></param>
/// <param name="observationWriter"></param>
/// <returns></returns>
public static ObservationProto GetObservationProto(this ISensor sensor, ObservationWriter observationWriter)
{
var obsSpec = sensor.GetObservationSpec();
var shape = obsSpec.Shape;
ObservationProto observationProto = null;
var compressionSpec = sensor.GetCompressionSpec();
var compressionType = compressionSpec.SensorCompressionType;
// Check capabilities if we need to concatenate PNGs
if (compressionType == SensorCompressionType.PNG && shape.Length == 3 && shape[2] > 3)
{
var trainerCanHandle = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.ConcatenatedPngObservations;
if (!trainerCanHandle)
{
if (!s_HaveWarnedTrainerCapabilitiesMultiPng)
{
Debug.LogWarning(
$"Attached trainer doesn't support multiple PNGs. Switching to uncompressed observations for sensor {sensor.GetName()}. " +
"Please find the versions that work best together from our release page: " +
"https://github.com/Unity-Technologies/ml-agents/releases"
);
s_HaveWarnedTrainerCapabilitiesMultiPng = true;
}
compressionType = SensorCompressionType.None;
}
}
// Check capabilities if we need mapping for compressed observations
if (compressionType != SensorCompressionType.None && shape.Length == 3 && shape[2] > 3)
{
var trainerCanHandleMapping = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.CompressedChannelMapping;
var isTrivialMapping = compressionSpec.IsTrivialMapping();
if (!trainerCanHandleMapping && !isTrivialMapping)
{
if (!s_HaveWarnedTrainerCapabilitiesMapping)
{
Debug.LogWarning(
$"The sensor {sensor.GetName()} is using non-trivial mapping and " +
"the attached trainer doesn't support compression mapping. " +
"Switching to uncompressed observations. " +
"Please find the versions that work best together from our release page: " +
"https://github.com/Unity-Technologies/ml-agents/releases"
);
s_HaveWarnedTrainerCapabilitiesMapping = true;
}
compressionType = SensorCompressionType.None;
}
}
if (compressionType == SensorCompressionType.None)
{
var numFloats = sensor.ObservationSize();
var floatDataProto = new ObservationProto.Types.FloatData();
// Resize the float array
// TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
for (var i = 0; i < numFloats; i++)
{
floatDataProto.Data.Add(0.0f);
}
observationWriter.SetTarget(floatDataProto.Data, sensor.GetObservationSpec(), 0);
sensor.Write(observationWriter);
observationProto = new ObservationProto
{
FloatData = floatDataProto,
CompressionType = (CompressionTypeProto)SensorCompressionType.None,
};
}
else
{
var compressedObs = sensor.GetCompressedObservation();
if (compressedObs == null)
{
throw new UnityAgentsException(
$"GetCompressedObservation() returned null data for sensor named {sensor.GetName()}. " +
"You must return a byte[]. If you don't want to use compressed observations, " +
"return CompressionSpec.Default() from GetCompressionSpec()."
);
}
observationProto = new ObservationProto
{
CompressedData = ByteString.CopyFrom(compressedObs),
CompressionType = (CompressionTypeProto)sensor.GetCompressionSpec().SensorCompressionType,
};
if (compressionSpec.CompressedChannelMapping != null)
{
observationProto.CompressedChannelMapping.AddRange(compressionSpec.CompressedChannelMapping);
}
}
// Add the dimension properties to the observationProto
var dimensionProperties = obsSpec.DimensionProperties;
for (int i = 0; i < dimensionProperties.Length; i++)
{
observationProto.DimensionProperties.Add((int)dimensionProperties[i]);
}
// Checking trainer compatibility with variable length observations
if (dimensionProperties == new InplaceArray <DimensionProperty>(DimensionProperty.VariableSize, DimensionProperty.None))
{
var trainerCanHandleVarLenObs = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.VariableLengthObservation;
if (!trainerCanHandleVarLenObs)
{
throw new UnityAgentsException("Variable Length Observations are not supported by the trainer");
}
}
for (var i = 0; i < shape.Length; i++)
{
observationProto.Shape.Add(shape[i]);
}
var sensorName = sensor.GetName();
if (!string.IsNullOrEmpty(sensorName))
{
observationProto.Name = sensorName;
}
observationProto.ObservationType = (ObservationTypeProto)obsSpec.ObservationType;
return(observationProto);
}
/// <summary>
/// Generate an ObservationProto for the sensor using the provided WriteAdapter.
/// This is equivalent to producing an Observation and calling Observation.ToProto(),
/// but avoid some intermediate memory allocations.
/// </summary>
/// <param name="sensor"></param>
/// <param name="writeAdapter"></param>
/// <returns></returns>
public static ObservationProto GetObservationProto(this ISensor sensor, WriteAdapter writeAdapter)
{
var shape = sensor.GetObservationShape();
ObservationProto observationProto = null;
if (sensor.GetCompressionType() == SensorCompressionType.None)
{
var numFloats = sensor.ObservationSize();
var floatDataProto = new ObservationProto.Types.FloatData();
// Resize the float array
// TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
for (var i = 0; i < numFloats; i++)
{
floatDataProto.Data.Add(0.0f);
}
writeAdapter.SetTarget(floatDataProto.Data, sensor.GetObservationShape(), 0);
sensor.Write(writeAdapter);
observationProto = new ObservationProto
{
FloatData = floatDataProto,
CompressionType = (CompressionTypeProto)SensorCompressionType.None,
};
}
else
{
var compressedObs = sensor.GetCompressedObservation();
if (compressedObs == null)
{
throw new UnityAgentsException(
$"GetCompressedObservation() returned null data for sensor named {sensor.GetName()}. " +
"You must return a byte[]. If you don't want to use compressed observations, " +
"return SensorCompressionType.None from GetCompressionType()."
);
}
observationProto = new ObservationProto
{
CompressedData = ByteString.CopyFrom(compressedObs),
CompressionType = (CompressionTypeProto)sensor.GetCompressionType(),
};
}
observationProto.Shape.AddRange(shape);
return(observationProto);
}
/// <summary>
/// Generate an ObservationProto for the sensor using the provided ObservationWriter.
/// This is equivalent to producing an Observation and calling Observation.ToProto(),
/// but avoid some intermediate memory allocations.
/// </summary>
/// <param name="sensor"></param>
/// <param name="observationWriter"></param>
/// <returns></returns>
public static ObservationProto GetObservationProto(this ISensor sensor, ObservationWriter observationWriter)
{
var shape = sensor.GetObservationShape();
ObservationProto observationProto = null;
var compressionType = sensor.GetCompressionType();
// Check capabilities if we need to concatenate PNGs
if (compressionType == SensorCompressionType.PNG && shape.Length == 3 && shape[2] > 3)
{
var trainerCanHandle = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.ConcatenatedPngObservations;
if (!trainerCanHandle)
{
if (!s_HaveWarnedTrainerCapabilitiesMultiPng)
{
Debug.LogWarning(
$"Attached trainer doesn't support multiple PNGs. Switching to uncompressed observations for sensor {sensor.GetName()}. " +
"Please find the versions that work best together from our release page: " +
"https://github.com/Unity-Technologies/ml-agents/releases"
);
s_HaveWarnedTrainerCapabilitiesMultiPng = true;
}
compressionType = SensorCompressionType.None;
}
}
// Check capabilities if we need mapping for compressed observations
if (compressionType != SensorCompressionType.None && shape.Length == 3 && shape[2] > 3)
{
var trainerCanHandleMapping = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.CompressedChannelMapping;
var isTrivialMapping = IsTrivialMapping(sensor);
if (!trainerCanHandleMapping && !isTrivialMapping)
{
if (!s_HaveWarnedTrainerCapabilitiesMapping)
{
Debug.LogWarning(
$"The sensor {sensor.GetName()} is using non-trivial mapping and " +
"the attached trainer doesn't support compression mapping. " +
"Switching to uncompressed observations. " +
"Please find the versions that work best together from our release page: " +
"https://github.com/Unity-Technologies/ml-agents/releases"
);
s_HaveWarnedTrainerCapabilitiesMapping = true;
}
compressionType = SensorCompressionType.None;
}
}
if (compressionType == SensorCompressionType.None)
{
var numFloats = sensor.ObservationSize();
var floatDataProto = new ObservationProto.Types.FloatData();
// Resize the float array
// TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
for (var i = 0; i < numFloats; i++)
{
floatDataProto.Data.Add(0.0f);
}
observationWriter.SetTarget(floatDataProto.Data, sensor.GetObservationShape(), 0);
sensor.Write(observationWriter);
observationProto = new ObservationProto
{
FloatData = floatDataProto,
CompressionType = (CompressionTypeProto)SensorCompressionType.None,
};
}
else
{
var compressedObs = sensor.GetCompressedObservation();
if (compressedObs == null)
{
throw new UnityAgentsException(
$"GetCompressedObservation() returned null data for sensor named {sensor.GetName()}. " +
"You must return a byte[]. If you don't want to use compressed observations, " +
"return SensorCompressionType.None from GetCompressionType()."
);
}
observationProto = new ObservationProto
{
CompressedData = ByteString.CopyFrom(compressedObs),
CompressionType = (CompressionTypeProto)sensor.GetCompressionType(),
};
var compressibleSensor = sensor as ISparseChannelSensor;
if (compressibleSensor != null)
{
observationProto.CompressedChannelMapping.AddRange(compressibleSensor.GetCompressedChannelMapping());
}
}
observationProto.Shape.AddRange(shape);
return(observationProto);
}
/// <summary>
/// Generate an ObservationProto for the sensor using the provided ObservationWriter.
/// This is equivalent to producing an Observation and calling Observation.ToProto(),
/// but avoid some intermediate memory allocations.
/// </summary>
/// <param name="sensor"></param>
/// <param name="observationWriter"></param>
/// <returns></returns>
public static ObservationProto GetObservationProto(this ISensor sensor, ObservationWriter observationWriter)
{
var shape = sensor.GetObservationShape();
ObservationProto observationProto = null;
var compressionType = sensor.GetCompressionType();
// Check capabilities if we need to concatenate PNGs
if (compressionType == SensorCompressionType.PNG && shape.Length == 3 && shape[2] > 3)
{
var trainerCanHandle = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.ConcatenatedPngObservations;
if (!trainerCanHandle)
{
if (!s_HaveWarnedAboutTrainerCapabilities)
{
Debug.LogWarning($"Attached trainer doesn't support multiple PNGs. Switching to uncompressed observations for sensor {sensor.GetName()}.");
s_HaveWarnedAboutTrainerCapabilities = true;
}
compressionType = SensorCompressionType.None;
}
}
if (compressionType == SensorCompressionType.None)
{
var numFloats = sensor.ObservationSize();
var floatDataProto = new ObservationProto.Types.FloatData();
// Resize the float array
// TODO upgrade protobuf versions so that we can set the Capacity directly - see https://github.com/protocolbuffers/protobuf/pull/6530
for (var i = 0; i < numFloats; i++)
{
floatDataProto.Data.Add(0.0f);
}
observationWriter.SetTarget(floatDataProto.Data, sensor.GetObservationShape(), 0);
sensor.Write(observationWriter);
observationProto = new ObservationProto
{
FloatData = floatDataProto,
CompressionType = (CompressionTypeProto)SensorCompressionType.None,
};
}
else
{
var compressedObs = sensor.GetCompressedObservation();
if (compressedObs == null)
{
throw new UnityAgentsException(
$"GetCompressedObservation() returned null data for sensor named {sensor.GetName()}. " +
"You must return a byte[]. If you don't want to use compressed observations, " +
"return SensorCompressionType.None from GetCompressionType()."
);
}
observationProto = new ObservationProto
{
CompressedData = ByteString.CopyFrom(compressedObs),
CompressionType = (CompressionTypeProto)sensor.GetCompressionType(),
};
}
observationProto.Shape.AddRange(shape);
return(observationProto);
}