ml-agents/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs

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;
            var varLenIndex = 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 + varLenIndex))
                    {
                        failedModelChecks.Add(
                            "The model does not contain an Observation Placeholder Input " +
                            $"for sensor component {varLenIndex} ({sensor.GetType().Name}).");
                        varLenIndex++;
                    }
                }

            }

            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;
            var varLenIndex = 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 + varLenIndex] =
                        (bp, tensor, scs, i) => CheckRankTwoObsShape(tensor, sensorComponent);
                    varLenIndex++;
                }
            }

            // 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;
        }
    }
}