Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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using System;
using System.Collections.Generic;
using System.Linq;
using Unity.Barracuda;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Policies;
namespace Unity.MLAgents.Inference
{
/// <summary>
/// Prepares the Tensors for the Learning Brain and exposes a list of failed checks if Model
/// and BrainParameters are incompatible.
/// </summary>
internal class BarracudaModelParamLoader
{
const long k_ApiVersion = 2;
/// <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="sensorComponents">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>The list the error messages of the checks that failed</returns>
public static IEnumerable<string> CheckModel(Model model, BrainParameters brainParameters,
SensorComponent[] sensorComponents, ActuatorComponent[] actuatorComponents,
int observableAttributeTotalSize = 0,
BehaviorType behaviorType = BehaviorType.Default)
{
List<string> failedModelChecks = new List<string>();
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(errorMsg);
return failedModelChecks;
}
var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks);
if (!hasExpectedTensors)
{
return failedModelChecks;
}
var modelApiVersion = (int)model.GetTensorByName(TensorNames.VersionNumber)[0];
if (modelApiVersion == -1)
{
failedModelChecks.Add(
"Model was not trained using the right version of ML-Agents. " +
"Cannot use this model.");
return failedModelChecks;
}
if (modelApiVersion != k_ApiVersion)
{
failedModelChecks.Add(
$"Version of the trainer the model was trained with ({modelApiVersion}) " +
$"is not compatible with the Brain's version ({k_ApiVersion}).");
return failedModelChecks;
}
var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memorySize == -1)
{
failedModelChecks.Add($"Missing node in the model provided : {TensorNames.MemorySize}");
return failedModelChecks;
}
failedModelChecks.AddRange(
CheckInputTensorPresence(model, brainParameters, memorySize, sensorComponents)
);
failedModelChecks.AddRange(
CheckOutputTensorPresence(model, memorySize)
);
failedModelChecks.AddRange(
CheckInputTensorShape(model, brainParameters, sensorComponents, observableAttributeTotalSize)
);
failedModelChecks.AddRange(
CheckOutputTensorShape(model, brainParameters, actuatorComponents)
);
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="sensorComponents">Array of attached sensor components</param>
/// <returns>
/// A IEnumerable of string corresponding to the failed input presence checks.
/// </returns>
static IEnumerable<string> CheckInputTensorPresence(
Model model,
BrainParameters brainParameters,
int memory,
SensorComponent[] sensorComponents
)
{
var failedModelChecks = new List<string>();
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(
"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 < sensorComponents.Length; sensorIndex++)
{
var sensor = sensorComponents[sensorIndex];
if (sensor.GetObservationShape().Length == 3)
{
if (!tensorsNames.Contains(
TensorNames.VisualObservationPlaceholderPrefix + visObsIndex))
{
failedModelChecks.Add(
"The model does not contain a Visual Observation Placeholder Input " +
$"for sensor component {visObsIndex} ({sensor.GetType().Name}).");
}
visObsIndex++;
}
if (sensor.GetObservationShape().Length == 2)
{
if (!tensorsNames.Contains(
TensorNames.ObservationPlaceholderPrefix + sensorIndex))
{
failedModelChecks.Add(
"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(
$"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(
"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(
"The model does not contain an Action Mask but is using Discrete Control.");
}
}
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 string corresponding to the failed output presence checks.
/// </returns>
static IEnumerable<string> CheckOutputTensorPresence(Model model, int memory)
{
var failedModelChecks = new List<string>();
// 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(
"The model does not contain a Recurrent Output Node but has memory_size.");
}
}
return failedModelChecks;
}
/// <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="sensorComponent">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 string CheckVisualObsShape(
TensorProxy tensorProxy, SensorComponent sensorComponent)
{
var shape = sensorComponent.GetObservationShape();
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 $"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}].";
}
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="sensorComponent">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 string CheckRankTwoObsShape(
TensorProxy tensorProxy, SensorComponent sensorComponent)
{
var shape = sensorComponent.GetObservationShape();
var dim1Bp = shape[0];
var dim2Bp = shape[1];
var dim1T = tensorProxy.Channels;
var dim2T = tensorProxy.Width;
if ((dim1Bp != dim1T) || (dim2Bp != dim2T))
{
return $"An Observation of the model does not match. " +
$"Received TensorProxy of shape [?x{dim1Bp}x{dim2Bp}] but " +
$"was expecting [?x{dim1T}x{dim2T}].";
}
return null;
}
/// <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="sensorComponents">Attached sensors</param>
/// <param name="observableAttributeTotalSize">Sum of the sizes of all ObservableAttributes.</param>
/// <returns>The list the error messages of the checks that failed</returns>
static IEnumerable<string> CheckInputTensorShape(
Model model, BrainParameters brainParameters, SensorComponent[] sensorComponents,
int observableAttributeTotalSize)
{
var failedModelChecks = new List<string>();
var tensorTester =
new Dictionary<string, Func<BrainParameters, TensorProxy, SensorComponent[], int, string>>()
{
{TensorNames.VectorObservationPlaceholder, CheckVectorObsShape},
{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);
}
var visObsIndex = 0;
for (var sensorIndex = 0; sensorIndex < sensorComponents.Length; sensorIndex++)
{
var sensorComponent = sensorComponents[sensorIndex];
if (sensorComponent.GetObservationShape().Length == 3)
{
tensorTester[TensorNames.VisualObservationPlaceholderPrefix + visObsIndex] =
(bp, tensor, scs, i) => CheckVisualObsShape(tensor, sensorComponent);
visObsIndex++;
}
if (sensorComponent.GetObservationShape().Length == 2)
{
tensorTester[TensorNames.ObservationPlaceholderPrefix + sensorIndex] =
(bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sensorComponent);
}
}
// If the model expects an input but it is not in this list
foreach (var tensor in model.GetInputTensors())
{
if (!tensorTester.ContainsKey(tensor.name))
{
if (!tensor.name.Contains("visual_observation"))
{
failedModelChecks.Add(
"Model requires an unknown input named : " + tensor.name);
}
}
else
{
var tester = tensorTester[tensor.name];
var error = tester.Invoke(brainParameters, tensor, sensorComponents, observableAttributeTotalSize);
if (error != null)
{
failedModelChecks.Add(error);
}
}
}
return failedModelChecks;
}
/// <summary>
/// Checks that the shape of the Vector Observation 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="sensorComponents">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 string CheckVectorObsShape(
BrainParameters brainParameters, TensorProxy tensorProxy, SensorComponent[] sensorComponents,
int observableAttributeTotalSize)
{
var vecObsSizeBp = brainParameters.VectorObservationSize;
var numStackedVector = brainParameters.NumStackedVectorObservations;
var totalVecObsSizeT = tensorProxy.shape[tensorProxy.shape.Length - 1];
var totalVectorSensorSize = 0;
foreach (var sensorComp in sensorComponents)
{
if (sensorComp.GetObservationShape().Length == 1)
{
totalVectorSensorSize += sensorComp.GetObservationShape()[0];
}
}
totalVectorSensorSize += observableAttributeTotalSize;
if (vecObsSizeBp * numStackedVector + totalVectorSensorSize != totalVecObsSizeT)
{
var sensorSizes = "";
foreach (var sensorComp in sensorComponents)
{
if (sensorComp.GetObservationShape().Length == 1)
{
var vecSize = sensorComp.GetObservationShape()[0];
if (sensorSizes.Length == 0)
{
sensorSizes = $"[{vecSize}";
}
else
{
sensorSizes += $", {vecSize}";
}
}
}
sensorSizes += "]";
return $"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" +
$"SensorComponent sizes: {sensorSizes}.";
}
return null;
}
/// <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="sensorComponents">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 string CheckPreviousActionShape(
BrainParameters brainParameters, TensorProxy tensorProxy,
SensorComponent[] sensorComponents, int observableAttributeTotalSize)
{
var numberActionsBp = brainParameters.ActionSpec.NumDiscreteActions;
var numberActionsT = tensorProxy.shape[tensorProxy.shape.Length - 1];
if (numberActionsBp != numberActionsT)
{
return "Previous Action Size of the model does not match. " +
$"Received {numberActionsBp} but was expecting {numberActionsT}.";
}
return null;
}
/// <summary>
/// Generates failed checks that correspond to output 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="actuatorComponents">Array of attached actuator components.</param>
/// <returns>
/// A IEnumerable of string corresponding to the incompatible shapes between model
/// and BrainParameters.
/// </returns>
static IEnumerable<string> CheckOutputTensorShape(
Model model,
BrainParameters brainParameters,
ActuatorComponent[] actuatorComponents)
{
var failedModelChecks = new List<string>();
// If the model expects an output but it is not in this list
var modelContinuousActionSize = model.ContinuousOutputSize();
var continuousError = CheckContinuousActionOutputShape(brainParameters, actuatorComponents, modelContinuousActionSize);
if (continuousError != null)
{
failedModelChecks.Add(continuousError);
}
var modelSumDiscreteBranchSizes = model.DiscreteOutputSize();
var discreteError = CheckDiscreteActionOutputShape(brainParameters, actuatorComponents, modelSumDiscreteBranchSizes);
if (discreteError != null)
{
failedModelChecks.Add(discreteError);
}
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="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 string CheckDiscreteActionOutputShape(
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 "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 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 string 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 "Continuous Action Size of the model does not match. The BrainParameters and ActuatorComponents expect " +
$"{numContinuousActions} but the model contains {modelContinuousActionSize}.";
}
return null;
}
}
}