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enable precommit for line endings, fix 1 failure (#2208)

/develop-generalizationTraining-TrainerController
GitHub 6 年前
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e6b8140a
共有 2 个文件被更改，包括 116 次插入 和 106 次删除
  1. 10
      .pre-commit-config.yaml
  2. 212
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorGenerator.cs

10
.pre-commit-config.yaml
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name: mypy-gym-unity
files: "gym-unity/.*"
args: [--ignore-missing-imports, --disallow-incomplete-defs]
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.2.3
hooks:
- id: mixed-line-ending
exclude: >
(?x)^(
.*cs.meta|
.*.css
)$
args: [--fix=lf]

212
UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorGenerator.cs
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#define ENABLE_BARRACUDA
using System.Collections.Generic;
using Barracuda;
namespace MLAgents.InferenceBrain
{
/// <summary>
/// Mapping between Tensor names and generators.
/// A TensorGenerator implements a Dictionary of strings (node names) to an Action.
/// The Action take as argument the tensor, the current batch size and a Dictionary of
/// Agent to AgentInfo corresponding to the current batch.
/// Each Generator reshapes and fills the data of the tensor based of the data of the batch.
/// When the Tensor is an Input to the model, the shape of the Tensor will be modified
/// depending on the current batch size and the data of the Tensor will be filled using the
/// Dictionary of Agent to AgentInfo.
/// When the Tensor is an Output of the model, only the shape of the Tensor will be modified
/// using the current batch size. The data will be prefilled with zeros.
/// </summary>
public class TensorGenerator
{
public interface Generator
{
/// <summary>
/// Modifies the data inside a Tensor according to the information contained in the
/// AgentInfos contained in the current batch.
/// </summary>
/// <param name="tensor"> The tensor the data and shape will be modified</param>
/// <param name="batchSize"> The number of agents present in the current batch</param>
/// <param name="agentInfo"> Dictionary of Agent to AgentInfo containing the
/// information that will be used to populate the tensor's data</param>
void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo);
}
Dictionary<string, Generator> _dict = new Dictionary<string, Generator>();
/// <summary>
/// Returns a new TensorGenerators object.
/// </summary>
/// <param name="bp"> The BrainParameters used to determine what Generators will be
/// used</param>
/// <param name="seed"> The seed the Generators will be initialized with.</param>
public TensorGenerator(BrainParameters bp, int seed, object barracudaModel = null)
{
// Generator for Inputs
_dict[TensorNames.BatchSizePlaceholder] = new BatchSizeGenerator();
_dict[TensorNames.SequenceLengthPlaceholder] = new SequenceLengthGenerator();
_dict[TensorNames.VectorObservationPlacholder] = new VectorObservationGenerator();
_dict[TensorNames.RecurrentInPlaceholder] = new RecurrentInputGenerator();
#if ENABLE_BARRACUDA
Barracuda.Model model = (Barracuda.Model) barracudaModel;
for (var i = 0; i < model?.memories.Length; i++)
{
_dict[model.memories[i].input] = new BarracudaRecurrentInputGenerator(i);
}
#endif
_dict[TensorNames.PreviousActionPlaceholder] = new PreviousActionInputGenerator();
_dict[TensorNames.ActionMaskPlaceholder] = new ActionMaskInputGenerator();
_dict[TensorNames.RandomNormalEpsilonPlaceholder] = new RandomNormalInputGenerator(seed);
if (bp.cameraResolutions != null)
{
for (var visIndex = 0;
visIndex < bp.cameraResolutions.Length;
visIndex++)
{
var index = visIndex;
var bw = bp.cameraResolutions[visIndex].blackAndWhite;
_dict[TensorNames.VisualObservationPlaceholderPrefix + visIndex] = new
VisualObservationInputGenerator(index, bw);
}
}
// Generators for Outputs
_dict[TensorNames.ActionOutput] = new BiDimensionalOutputGenerator();
_dict[TensorNames.RecurrentOutput] = new BiDimensionalOutputGenerator();
_dict[TensorNames.ValueEstimateOutput] = new BiDimensionalOutputGenerator();
}
/// <summary>
/// Populates the data of the tensor inputs given the data contained in the current batch
/// of agents.
/// </summary>
/// <param name="tensors"> Enumerable of tensors that will be modified.</param>
/// <param name="currentBatchSize"> The number of agents present in the current batch
/// </param>
/// <param name="agentInfos"> Dictionary of Agent to AgentInfo that contains the
/// data that will be used to modify the tensors</param>
/// <exception cref="UnityAgentsException"> One of the tensor does not have an
/// associated generator.</exception>
public void GenerateTensors(IEnumerable<Tensor> tensors,
int currentBatchSize,
Dictionary<Agent, AgentInfo> agentInfos)
{
foreach (var tensor in tensors)
{
if (!_dict.ContainsKey(tensor.Name))
{
throw new UnityAgentsException(
"Unknow tensor expected as input : " + tensor.Name);
}
_dict[tensor.Name].Generate(tensor, currentBatchSize, agentInfos);
}
}
}
}
#define ENABLE_BARRACUDA
using System.Collections.Generic;
using Barracuda;
namespace MLAgents.InferenceBrain
{
/// <summary>
/// Mapping between Tensor names and generators.
/// A TensorGenerator implements a Dictionary of strings (node names) to an Action.
/// The Action take as argument the tensor, the current batch size and a Dictionary of
/// Agent to AgentInfo corresponding to the current batch.
/// Each Generator reshapes and fills the data of the tensor based of the data of the batch.
/// When the Tensor is an Input to the model, the shape of the Tensor will be modified
/// depending on the current batch size and the data of the Tensor will be filled using the
/// Dictionary of Agent to AgentInfo.
/// When the Tensor is an Output of the model, only the shape of the Tensor will be modified
/// using the current batch size. The data will be prefilled with zeros.
/// </summary>
public class TensorGenerator
{
public interface Generator
{
/// <summary>
/// Modifies the data inside a Tensor according to the information contained in the
/// AgentInfos contained in the current batch.
/// </summary>
/// <param name="tensor"> The tensor the data and shape will be modified</param>
/// <param name="batchSize"> The number of agents present in the current batch</param>
/// <param name="agentInfo"> Dictionary of Agent to AgentInfo containing the
/// information that will be used to populate the tensor's data</param>
void Generate(Tensor tensor, int batchSize, Dictionary<Agent, AgentInfo> agentInfo);
}
Dictionary<string, Generator> _dict = new Dictionary<string, Generator>();
/// <summary>
/// Returns a new TensorGenerators object.
/// </summary>
/// <param name="bp"> The BrainParameters used to determine what Generators will be
/// used</param>
/// <param name="seed"> The seed the Generators will be initialized with.</param>
public TensorGenerator(BrainParameters bp, int seed, object barracudaModel = null)
{
// Generator for Inputs
_dict[TensorNames.BatchSizePlaceholder] = new BatchSizeGenerator();
_dict[TensorNames.SequenceLengthPlaceholder] = new SequenceLengthGenerator();
_dict[TensorNames.VectorObservationPlacholder] = new VectorObservationGenerator();
_dict[TensorNames.RecurrentInPlaceholder] = new RecurrentInputGenerator();
#if ENABLE_BARRACUDA
Barracuda.Model model = (Barracuda.Model) barracudaModel;
for (var i = 0; i < model?.memories.Length; i++)
{
_dict[model.memories[i].input] = new BarracudaRecurrentInputGenerator(i);
}
#endif
_dict[TensorNames.PreviousActionPlaceholder] = new PreviousActionInputGenerator();
_dict[TensorNames.ActionMaskPlaceholder] = new ActionMaskInputGenerator();
_dict[TensorNames.RandomNormalEpsilonPlaceholder] = new RandomNormalInputGenerator(seed);
if (bp.cameraResolutions != null)
{
for (var visIndex = 0;
visIndex < bp.cameraResolutions.Length;
visIndex++)
{
var index = visIndex;
var bw = bp.cameraResolutions[visIndex].blackAndWhite;
_dict[TensorNames.VisualObservationPlaceholderPrefix + visIndex] = new
VisualObservationInputGenerator(index, bw);
}
}
// Generators for Outputs
_dict[TensorNames.ActionOutput] = new BiDimensionalOutputGenerator();
_dict[TensorNames.RecurrentOutput] = new BiDimensionalOutputGenerator();
_dict[TensorNames.ValueEstimateOutput] = new BiDimensionalOutputGenerator();
}
/// <summary>
/// Populates the data of the tensor inputs given the data contained in the current batch
/// of agents.
/// </summary>
/// <param name="tensors"> Enumerable of tensors that will be modified.</param>
/// <param name="currentBatchSize"> The number of agents present in the current batch
/// </param>
/// <param name="agentInfos"> Dictionary of Agent to AgentInfo that contains the
/// data that will be used to modify the tensors</param>
/// <exception cref="UnityAgentsException"> One of the tensor does not have an
/// associated generator.</exception>
public void GenerateTensors(IEnumerable<Tensor> tensors,
int currentBatchSize,
Dictionary<Agent, AgentInfo> agentInfos)
{
foreach (var tensor in tensors)
{
if (!_dict.ContainsKey(tensor.Name))
{
throw new UnityAgentsException(
"Unknow tensor expected as input : " + tensor.Name);
}
_dict[tensor.Name].Generate(tensor, currentBatchSize, agentInfos);
}
}
}
}
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