using System.Collections.Generic; using System; using Unity.Barracuda; using Unity.MLAgents.Inference.Utils; using Unity.MLAgents.Sensors; namespace Unity.MLAgents.Inference { /// /// Reshapes a Tensor so that its first dimension becomes equal to the current batch size /// and initializes its content to be zeros. Will only work on 2-dimensional tensors. /// The second dimension of the Tensor will not be modified. /// internal class BiDimensionalOutputGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; public BiDimensionalOutputGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); } } /// /// Generates the Tensor corresponding to the BatchSize input : Will be a one dimensional /// integer array of size 1 containing the batch size. /// internal class BatchSizeGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; public BatchSizeGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { tensorProxy.data?.Dispose(); tensorProxy.data = m_Allocator.Alloc(new TensorShape(1, 1)); tensorProxy.data[0] = batchSize; } } /// /// Generates the Tensor corresponding to the SequenceLength input : Will be a one /// dimensional integer array of size 1 containing 1. /// Note : the sequence length is always one since recurrent networks only predict for /// one step at the time. /// internal class SequenceLengthGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; public SequenceLengthGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { tensorProxy.shape = new long[0]; tensorProxy.data?.Dispose(); tensorProxy.data = m_Allocator.Alloc(new TensorShape(1, 1)); tensorProxy.data[0] = 1; } } /// /// Generates the Tensor corresponding to the Recurrent input : Will be a two /// dimensional float array of dimension [batchSize x memorySize]. /// It will use the Memory data contained in the agentInfo to fill the data /// of the tensor. /// internal class RecurrentInputGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; Dictionary> m_Memories; public RecurrentInputGenerator( ITensorAllocator allocator, Dictionary> memories) { m_Allocator = allocator; m_Memories = memories; } public void Generate( TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); var memorySize = tensorProxy.data.width; var agentIndex = 0; for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++) { var infoSensorPair = infos[infoIndex]; var info = infoSensorPair.agentInfo; List memory; if (info.done) { m_Memories.Remove(info.episodeId); } if (!m_Memories.TryGetValue(info.episodeId, out memory)) { for (var j = 0; j < memorySize; j++) { tensorProxy.data[agentIndex, 0, j, 0] = 0; } agentIndex++; continue; } for (var j = 0; j < Math.Min(memorySize, memory.Count); j++) { if (j >= memory.Count) { break; } tensorProxy.data[agentIndex, 0, j, 0] = memory[j]; } agentIndex++; } } } /// /// Generates the Tensor corresponding to the Previous Action input : Will be a two /// dimensional integer array of dimension [batchSize x actionSize]. /// It will use the previous action data contained in the agentInfo to fill the data /// of the tensor. /// internal class PreviousActionInputGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; public PreviousActionInputGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); var actionSize = tensorProxy.shape[tensorProxy.shape.Length - 1]; var agentIndex = 0; for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++) { var infoSensorPair = infos[infoIndex]; var info = infoSensorPair.agentInfo; var pastAction = info.storedActions.DiscreteActions; if (!pastAction.IsEmpty()) { for (var j = 0; j < actionSize; j++) { tensorProxy.data[agentIndex, j] = pastAction[j]; } } agentIndex++; } } } /// /// Generates the Tensor corresponding to the Action Mask input : Will be a two /// dimensional float array of dimension [batchSize x numActionLogits]. /// It will use the Action Mask data contained in the agentInfo to fill the data /// of the tensor. /// internal class ActionMaskInputGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; public ActionMaskInputGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); var maskSize = tensorProxy.shape[tensorProxy.shape.Length - 1]; var agentIndex = 0; for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++) { var infoSensorPair = infos[infoIndex]; var agentInfo = infoSensorPair.agentInfo; var maskList = agentInfo.discreteActionMasks; for (var j = 0; j < maskSize; j++) { var isUnmasked = (maskList != null && maskList[j]) ? 0.0f : 1.0f; tensorProxy.data[agentIndex, j] = isUnmasked; } agentIndex++; } } } /// /// Generates the Tensor corresponding to the Epsilon input : Will be a two /// dimensional float array of dimension [batchSize x actionSize]. /// It will use the generate random input data from a normal Distribution. /// internal class RandomNormalInputGenerator : TensorGenerator.IGenerator { readonly RandomNormal m_RandomNormal; readonly ITensorAllocator m_Allocator; public RandomNormalInputGenerator(int seed, ITensorAllocator allocator) { m_RandomNormal = new RandomNormal(seed); m_Allocator = allocator; } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); TensorUtils.FillTensorWithRandomNormal(tensorProxy, m_RandomNormal); } } /// /// Generates the Tensor corresponding to the Observation input : Will be a multi /// dimensional float array. /// It will use the Observation data contained in the sensors to fill the data /// of the tensor. /// internal class ObservationGenerator : TensorGenerator.IGenerator { readonly ITensorAllocator m_Allocator; List m_SensorIndices = new List(); ObservationWriter m_ObservationWriter = new ObservationWriter(); public ObservationGenerator(ITensorAllocator allocator) { m_Allocator = allocator; } public void AddSensorIndex(int sensorIndex) { m_SensorIndices.Add(sensorIndex); } public void Generate(TensorProxy tensorProxy, int batchSize, IList infos) { TensorUtils.ResizeTensor(tensorProxy, batchSize, m_Allocator); var agentIndex = 0; for (var infoIndex = 0; infoIndex < infos.Count; infoIndex++) { var info = infos[infoIndex]; 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 for (var sensorIndexIndex = 0; sensorIndexIndex < m_SensorIndices.Count; sensorIndexIndex++) { var sensorIndex = m_SensorIndices[sensorIndexIndex]; var sensor = info.sensors[sensorIndex]; m_ObservationWriter.SetTarget(tensorProxy, agentIndex, tensorOffset); var numWritten = sensor.Write(m_ObservationWriter); tensorOffset += numWritten; } } agentIndex++; } } } }