/// <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 CheckRankTwoObsShape(
TensorProxy tensorProxy, ISensor sensor)
{
var shape = sensor.GetObservationSpec().Shape;
var dim1Bp = shape[0];
var dim2Bp = shape[1];
var dim1T = tensorProxy.Channels;
var dim2T = tensorProxy.Width;
var dim3T = tensorProxy.Height;
if ((dim1Bp != dim1T) || (dim2Bp != dim2T))
{
var 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}x{dim2Bp}] but " +
$"was expecting {proxyDimStr} for the {sensor.GetName()} Sensor."
));
}
return(null);
}
public void Generate(TensorProxy tensorProxy, int batchSize, IEnumerable <AgentInfoSensorsPair> infos)
{
TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
var vecObsSizeT = tensorProxy.shape[tensorProxy.shape.Length - 1];
var agentIndex = 0;
foreach (var info in infos)
{
if (info.agentInfo.done)
{
// If the agent is done, we might have a stale reference to the sensors
// e.g. a dependent object might have been disposed.
// To avoid this, just fill observation with zeroes instead of calling sensor.Write.
TensorUtils.FillTensorBatch(tensorProxy, agentIndex, 0.0f);
}
else
{
var tensorOffset = 0;
// Write each sensor consecutively to the tensor
foreach (var sensorIndex in m_SensorIndices)
{
var sensor = info.sensors[sensorIndex];
m_ObservationWriter.SetTarget(tensorProxy, agentIndex, tensorOffset);
var numWritten = sensor.Write(m_ObservationWriter);
tensorOffset += numWritten;
}
Debug.AssertFormat(
tensorOffset == vecObsSizeT,
"mismatch between vector observation size ({0}) and number of observations written ({1})",
vecObsSizeT, tensorOffset
);
}
agentIndex++;
}
}
public void Generate(TensorProxy tensorProxy, int batchSize, IList <AgentInfoSensorsPair> infos)
{
tensorProxy.data?.Dispose();
tensorProxy.data = m_Allocator.Alloc(new TensorShape(1, 1));
tensorProxy.data[0] = batchSize;
}
public void Generate(TensorProxy tensorProxy, int batchSize, IList <AgentInfoSensorsPair> infos)
{
TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
TensorUtils.FillTensorWithRandomNormal(tensorProxy, m_RandomNormal);
}
public void Generate(TensorProxy tensorProxy, int batchSize, IList <AgentInfoSensorsPair> infos)
{
TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator);
}
public void Apply(TensorProxy tensorProxy, IEnumerable <int> actionIds, Dictionary <int, ActionBuffers> lastActions)
{
//var tensorDataProbabilities = tensorProxy.Data as float[,];
var idActionPairList = actionIds as List <int> ?? actionIds.ToList();
var batchSize = idActionPairList.Count;
var actionValues = new float[batchSize, m_ActionSize.Length];
var startActionIndices = Utilities.CumSum(m_ActionSize);
for (var actionIndex = 0; actionIndex < m_ActionSize.Length; actionIndex++)
{
var nBranchAction = m_ActionSize[actionIndex];
var actionProbs = new TensorProxy()
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new long[] { batchSize, nBranchAction },
data = m_Allocator.Alloc(new TensorShape(batchSize, nBranchAction))
};
for (var batchIndex = 0; batchIndex < batchSize; batchIndex++)
{
for (var branchActionIndex = 0;
branchActionIndex < nBranchAction;
branchActionIndex++)
{
actionProbs.data[batchIndex, branchActionIndex] =
tensorProxy.data[batchIndex, startActionIndices[actionIndex] + branchActionIndex];
}
}
var outputTensor = new TensorProxy()
{
valueType = TensorProxy.TensorType.FloatingPoint,
shape = new long[] { batchSize, 1 },
data = m_Allocator.Alloc(new TensorShape(batchSize, 1))
};
Eval(actionProbs, outputTensor, m_Multinomial);
for (var ii = 0; ii < batchSize; ii++)
{
actionValues[ii, actionIndex] = outputTensor.data[ii, 0];
}
actionProbs.data.Dispose();
outputTensor.data.Dispose();
}
var agentIndex = 0;
foreach (int agentId in actionIds)
{
if (lastActions.ContainsKey(agentId))
{
var actionBuffer = lastActions[agentId];
if (actionBuffer.IsEmpty())
{
actionBuffer = new ActionBuffers(m_ActionSpec);
lastActions[agentId] = actionBuffer;
}
var discreteBuffer = actionBuffer.DiscreteActions;
for (var j = 0; j < m_ActionSize.Length; j++)
{
discreteBuffer[j] = (int)actionValues[agentIndex, j];
}
}
agentIndex++;
}
}
