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Merge branch 'master' of https://github.com/erwincoumans/bullet3

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Erwin Coumans
2018-11-06 12:33:53 -08:00
parent 5d20b2d346 882252f8c0
commit 6e169ada80
46 changed files with 948 additions and 1663 deletions
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2
Extras/InverseDynamics/invdyn_bullet_comparison.cpp
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@@ -176,7 +176,7 @@ int compareInverseAndForwardDynamics(vecx &q, vecx &u, vecx &dot_u, btVector3 &g
btAlignedObjectArray<btQuaternion> world_to_local;
btAlignedObjectArray<btVector3> local_origin;
btmb->forwardKinematics(world_to_local, local_origin);
btmb->computeAccelerationsArticulatedBodyAlgorithmMultiDof(dt, scratch_r, scratch_v, scratch_m, isConstraintPass);
btmb->computeAccelerationsArticulatedBodyAlgorithmMultiDof(dt, scratch_r, scratch_v, scratch_m, isConstraintPass, false, false);
// read generalized accelerations back from btMultiBody
// the mapping from scratch variables to accelerations is taken from the implementation

586
data/humanoid.urdf Normal file
View File

@@ -0,0 +1,586 @@
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<parent link="link1_23"/>
<child link="left_foot"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 -0.08000"/>
<axis xyz="0.00000 0.00000 0.00000"/>
</joint>
<joint name="right_shoulder1" type="continuous">
<parent link="torso"/>
<child link="link1_25"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 -0.17000 0.06000"/>
<axis xyz="2.00000 1.00000 1.00000"/>
</joint>
<joint name="right_shoulder2" type="continuous">
<parent link="link1_25"/>
<child link="link1_26"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 -1.00000 1.00000"/>
</joint>
<joint name="jointfix_9_26" type="fixed">
<parent link="link1_26"/>
<child link="right_upper_arm"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 0.00000 0.00000"/>
</joint>
<joint name="right_elbow" type="continuous">
<parent link="right_upper_arm"/>
<child link="link1_28"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.18000 -0.18000 -0.18000"/>
<axis xyz="0.00000 -1.00000 1.00000"/>
</joint>
<joint name="jointfix_8_28" type="fixed">
<parent link="link1_28"/>
<child link="right_lower_arm"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 0.00000 0.00000"/>
</joint>
<joint name="left_shoulder1" type="continuous">
<parent link="torso"/>
<child link="link1_30"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.17000 0.06000"/>
<axis xyz="2.00000 -1.00000 1.00000"/>
</joint>
<joint name="left_shoulder2" type="continuous">
<parent link="link1_30"/>
<child link="link1_31"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 1.00000 1.00000"/>
</joint>
<joint name="jointfix_11_31" type="fixed">
<parent link="link1_31"/>
<child link="left_upper_arm"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 0.00000 0.00000"/>
</joint>
<joint name="left_elbow" type="continuous">
<parent link="left_upper_arm"/>
<child link="link1_33"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.18000 0.18000 -0.18000"/>
<axis xyz="0.00000 -1.00000 -1.00000"/>
</joint>
<joint name="jointfix_10_33" type="fixed">
<parent link="link1_33"/>
<child link="left_lower_arm"/>
<dynamics damping="1.0" friction="0.0001"/>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000"/>
<axis xyz="0.00000 0.00000 0.00000"/>
</joint>
</robot>

BIN
data/multibody.bullet
View File

Binary file not shown.

2
examples/Benchmarks/BenchmarkDemo.cpp
View File

@@ -1008,7 +1008,7 @@ void BenchmarkDemo::createTest4()
}
//this will enable polyhedral contact clipping, better quality, slightly slower
//convexHullShape->initializePolyhedralFeatures();
convexHullShape->initializePolyhedralFeatures();
btTransform trans;
trans.setIdentity();

3
examples/ExampleBrowser/OpenGLExampleBrowser.cpp
View File

@@ -251,7 +251,6 @@ void MyKeyboardCallback(int key, int state)
if (key == 'p')
{
#ifndef BT_NO_PROFILE
if (state)
{
b3ChromeUtilsStartTimings();
@@ -260,7 +259,6 @@ void MyKeyboardCallback(int key, int state)
{
b3ChromeUtilsStopTimingsAndWriteJsonFile("timings");
}
#endif //BT_NO_PROFILE
}
#ifndef NO_OPENGL3
@@ -1129,6 +1127,7 @@ bool OpenGLExampleBrowser::init(int argc, char* argv[])
gui2->registerFileOpenCallback(fileOpenCallback);
gui2->registerQuitCallback(quitCallback);
}
return true;

33
examples/Importers/ImportURDFDemo/BulletUrdfImporter.cpp
View File

@@ -2,8 +2,8 @@
This software is provided 'as-is', without any express or implied warranty.
In no event will the authors be held liable for any damages arising from the use of this software.
Permission is granted to anyone to use this software for any purpose,
including commercial applications, and to alter it and redistribute it freely,
Permission is granted to anyone to use this software for any purpose,
including commercial applications, and to alter it and redistribute it freely,
subject to the following restrictions:
1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required.
@@ -85,7 +85,7 @@ BulletURDFInternalData
m_urdfParser.setGlobalScaling(scaling);
}
};
void BulletURDFImporter::printTree()
@@ -160,11 +160,11 @@ bool BulletURDFImporter::loadURDF(const char* fileName, bool forceFixedBase)
//read file
int fileId = m_data->m_fileIO->fileOpen(relativeFileName,"r");
char destBuffer[8192];
char* line = 0;
do
do
{
line = m_data->m_fileIO->readLine(fileId, destBuffer, 8192);
if (line)
@@ -190,7 +190,7 @@ bool BulletURDFImporter::loadURDF(const char* fileName, bool forceFixedBase)
BulletErrorLogger loggie;
m_data->m_urdfParser.setParseSDF(false);
bool result = false;
if (xml_string.length())
{
result = m_data->m_urdfParser.loadUrdf(xml_string.c_str(), &loggie, forceFixedBase, (m_data->m_flags & CUF_PARSE_SENSORS));
@@ -235,10 +235,10 @@ bool BulletURDFImporter::loadSDF(const char* fileName, bool forceFixedBase)
//read file
int fileId = m_data->m_fileIO->fileOpen(relativeFileName,"r");
char destBuffer[8192];
char* line = 0;
do
do
{
line = m_data->m_fileIO->readLine(fileId, destBuffer, 8192);
if (line)
@@ -883,6 +883,15 @@ void BulletURDFImporter::convertURDFToVisualShapeInternal(const UrdfVisual* visu
switch (visual->m_geometry.m_type)
{
case URDF_GEOM_CAPSULE:
{
btScalar radius = visual->m_geometry.m_capsuleRadius;
btScalar height = visual->m_geometry.m_capsuleHeight;
btCapsuleShapeZ* capsuleShape = new btCapsuleShapeZ(radius, height);
convexColShape = capsuleShape;
convexColShape->setMargin(gUrdfDefaultCollisionMargin);
break;
}
case URDF_GEOM_CYLINDER:
{
btAlignedObjectArray<btVector3> vertices;
@@ -932,7 +941,7 @@ void BulletURDFImporter::convertURDFToVisualShapeInternal(const UrdfVisual* visu
{
case UrdfGeometry::FILE_OBJ:
{
if (b3ImportMeshUtility::loadAndRegisterMeshFromFileInternal(visual->m_geometry.m_meshFileName, meshData, m_data->m_fileIO))
{
if (meshData.m_textureImage1)
@@ -1172,7 +1181,7 @@ int BulletURDFImporter::convertLinkVisualShapes(int linkIndex, const char* pathP
(meshData.m_flags & B3_IMPORT_MESH_HAS_SPECULAR_COLOR))
{
UrdfMaterialColor matCol;
if (m_data->m_flags&CUF_USE_MATERIAL_TRANSPARANCY_FROM_MTL)
{
matCol.m_rgbaColor.setValue(meshData.m_rgbaColor[0],
@@ -1186,7 +1195,7 @@ int BulletURDFImporter::convertLinkVisualShapes(int linkIndex, const char* pathP
meshData.m_rgbaColor[2],
1);
}
matCol.m_specularColor.setValue(meshData.m_specularColor[0],
meshData.m_specularColor[1],
meshData.m_specularColor[2]);
@@ -1204,7 +1213,7 @@ int BulletURDFImporter::convertLinkVisualShapes(int linkIndex, const char* pathP
m_data->m_linkColors.insert(linkIndex, matCol);
}
}
}
}
if (vertices.size() && indices.size())

9
examples/MultiBody/InvertedPendulumPDControl.cpp
View File

@@ -48,9 +48,6 @@ InvertedPendulumPDControl::~InvertedPendulumPDControl()
{
}
///this is a temporary global, until we determine if we need the option or not
extern bool gJointFeedbackInWorldSpace;
extern bool gJointFeedbackInJointFrame;
btMultiBody* createInvertedPendulumMultiBody(btMultiBodyDynamicsWorld* world, GUIHelperInterface* guiHelper, const btTransform& baseWorldTrans, bool fixedBase)
{
@@ -315,8 +312,10 @@ void InvertedPendulumPDControl::initPhysics()
}
int upAxis = 1;
gJointFeedbackInWorldSpace = true;
gJointFeedbackInJointFrame = true;
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInWorldSpace = true;
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInJointFrame = true;
m_guiHelper->setUpAxis(upAxis);

9
examples/MultiBody/MultiBodyConstraintFeedback.cpp
View File

@@ -44,14 +44,9 @@ MultiBodyConstraintFeedbackSetup::~MultiBodyConstraintFeedbackSetup()
{
}
///this is a temporary global, until we determine if we need the option or not
extern bool gJointFeedbackInWorldSpace;
extern bool gJointFeedbackInJointFrame;
void MultiBodyConstraintFeedbackSetup::initPhysics()
{
int upAxis = 2;
gJointFeedbackInWorldSpace = true;
gJointFeedbackInJointFrame = true;
m_guiHelper->setUpAxis(upAxis);
btVector4 colors[4] =
@@ -69,6 +64,10 @@ void MultiBodyConstraintFeedbackSetup::initPhysics()
//btIDebugDraw::DBG_DrawConstraints
+btIDebugDraw::DBG_DrawWireframe + btIDebugDraw::DBG_DrawContactPoints + btIDebugDraw::DBG_DrawAabb); //+btIDebugDraw::DBG_DrawConstraintLimits);
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInWorldSpace = true;
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInJointFrame = true;
//create a static ground object
if (1)
{

9
examples/MultiBody/TestJointTorqueSetup.cpp
View File

@@ -44,15 +44,10 @@ TestJointTorqueSetup::~TestJointTorqueSetup()
{
}
///this is a temporary global, until we determine if we need the option or not
extern bool gJointFeedbackInWorldSpace;
extern bool gJointFeedbackInJointFrame;
void TestJointTorqueSetup::initPhysics()
{
int upAxis = 1;
gJointFeedbackInWorldSpace = true;
gJointFeedbackInJointFrame = true;
m_guiHelper->setUpAxis(upAxis);
@@ -71,6 +66,10 @@ void TestJointTorqueSetup::initPhysics()
//btIDebugDraw::DBG_DrawConstraints
+btIDebugDraw::DBG_DrawWireframe + btIDebugDraw::DBG_DrawContactPoints + btIDebugDraw::DBG_DrawAabb); //+btIDebugDraw::DBG_DrawConstraintLimits);
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInWorldSpace = true;
m_dynamicsWorld->getSolverInfo().m_jointFeedbackInJointFrame = true;
//create a static ground object
if (1)
{

44
examples/SharedMemory/PhysicsServerCommandProcessor.cpp
View File

@@ -96,8 +96,6 @@
#include "BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.h"
#endif
extern bool gJointFeedbackInWorldSpace;
extern bool gJointFeedbackInJointFrame;
int gInternalSimFlags = 0;
bool gResetSimulation = 0;
@@ -1652,7 +1650,7 @@ struct PhysicsServerCommandProcessorInternalData
std::string m_profileTimingFileName;
struct GUIHelperInterface* m_guiHelper;
int m_sharedMemoryKey;
bool m_enableTinyRenderer;
@@ -1777,7 +1775,7 @@ struct PhysicsServerCommandProcessorInternalData
}
#endif
m_vrControllerEvents.init();
@@ -2271,7 +2269,7 @@ struct ProgrammaticUrdfInterface : public URDFImporterInterface
double globalScaling = 1.f; //todo!
int flags = 0;
CommonFileIOInterface* fileIO = m_data->m_pluginManager.getFileIOInterface();
BulletURDFImporter u2b(m_data->m_guiHelper, m_data->m_pluginManager.getRenderInterface(),fileIO, globalScaling, flags);
u2b.setEnableTinyRenderer(m_data->m_enableTinyRenderer);
@@ -2755,7 +2753,7 @@ bool PhysicsServerCommandProcessor::processImportedObjects(const char* fileName,
SaveWorldObjectData sd;
sd.m_fileName = fileName;
for (int m = 0; m < u2b.getNumModels(); m++)
{
@@ -2971,7 +2969,7 @@ bool PhysicsServerCommandProcessor::processImportedObjects(const char* fileName,
}
{
if (m_data->m_pluginManager.getRenderInterface())
{
@@ -4267,7 +4265,7 @@ bool PhysicsServerCommandProcessor::processCreateCollisionShapeCommand(const str
urdfColObj.m_geometry.m_meshFileType = UrdfGeometry::MEMORY_VERTICES;
break;
}
bool foundFile = UrdfFindMeshFile(fileIO, pathPrefix, relativeFileName, error_message_prefix, &out_found_filename, &out_type);
if (foundFile)
{
@@ -5210,7 +5208,7 @@ bool PhysicsServerCommandProcessor::processRequestRaycastIntersectionsCommand(co
}
}
}
BatchRayCaster batchRayCaster(m_data->m_threadPool, m_data->m_dynamicsWorld, &rays[0], (b3RayHitInfo*)bufferServerToClient, totalRays);
batchRayCaster.castRays(numThreads);
@@ -6770,7 +6768,7 @@ bool PhysicsServerCommandProcessor::processCreateMultiBodyCommand(const struct S
useMultiBody = false;
}
bool ok = processImportedObjects("memory", bufferServerToClient, bufferSizeInBytes, useMultiBody, flags, u2b);
@@ -6912,7 +6910,7 @@ bool PhysicsServerCommandProcessor::processLoadSoftBodyCommand(const struct Shar
const std::string& error_message_prefix = "";
std::string out_found_filename;
int out_type;
bool foundFile = UrdfFindMeshFile(fileIO,pathPrefix, relativeFileName, error_message_prefix, &out_found_filename, &out_type);
std::vector<tinyobj::shape_t> shapes;
std::string err = tinyobj::LoadObj(shapes, out_found_filename.c_str(),"",fileIO);
@@ -7697,11 +7695,11 @@ bool PhysicsServerCommandProcessor::processRequestPhysicsSimulationParametersCom
serverCmd.m_simulationParameterResultArgs.m_gravityAcceleration[2] = grav[2];
serverCmd.m_simulationParameterResultArgs.m_internalSimFlags = gInternalSimFlags;
serverCmd.m_simulationParameterResultArgs.m_jointFeedbackMode = 0;
if (gJointFeedbackInWorldSpace)
if (m_data->m_dynamicsWorld->getSolverInfo().m_jointFeedbackInWorldSpace)
{
serverCmd.m_simulationParameterResultArgs.m_jointFeedbackMode |= JOINT_FEEDBACK_IN_WORLD_SPACE;
}
if (gJointFeedbackInJointFrame)
if (m_data->m_dynamicsWorld->getSolverInfo().m_jointFeedbackInJointFrame)
{
serverCmd.m_simulationParameterResultArgs.m_jointFeedbackMode |= JOINT_FEEDBACK_IN_JOINT_FRAME;
}
@@ -7747,8 +7745,8 @@ bool PhysicsServerCommandProcessor::processSendPhysicsParametersCommand(const st
if (clientCmd.m_updateFlags & SIM_PARAM_UPDATE_JOINT_FEEDBACK_MODE)
{
gJointFeedbackInWorldSpace = (clientCmd.m_physSimParamArgs.m_jointFeedbackMode & JOINT_FEEDBACK_IN_WORLD_SPACE) != 0;
gJointFeedbackInJointFrame = (clientCmd.m_physSimParamArgs.m_jointFeedbackMode & JOINT_FEEDBACK_IN_JOINT_FRAME) != 0;
m_data->m_dynamicsWorld->getSolverInfo().m_jointFeedbackInWorldSpace = (clientCmd.m_physSimParamArgs.m_jointFeedbackMode & JOINT_FEEDBACK_IN_WORLD_SPACE) != 0;
m_data->m_dynamicsWorld->getSolverInfo().m_jointFeedbackInJointFrame = (clientCmd.m_physSimParamArgs.m_jointFeedbackMode & JOINT_FEEDBACK_IN_JOINT_FRAME) != 0;
}
if (clientCmd.m_updateFlags & SIM_PARAM_UPDATE_DELTA_TIME)
@@ -9642,6 +9640,16 @@ int PhysicsServerCommandProcessor::extractCollisionShapes(const btCollisionShape
switch (colShape->getShapeType())
{
case STATIC_PLANE_PROXYTYPE:
{
btStaticPlaneShape* plane = (btStaticPlaneShape*) colShape;
collisionShapeBuffer[0].m_collisionGeometryType = GEOM_PLANE;
collisionShapeBuffer[0].m_dimensions[0] = plane->getPlaneNormal()[0];
collisionShapeBuffer[0].m_dimensions[1] = plane->getPlaneNormal()[1];
collisionShapeBuffer[0].m_dimensions[2] = plane->getPlaneNormal()[2];
numConverted += 1;
break;
}
case CONVEX_HULL_SHAPE_PROXYTYPE:
{
UrdfCollision* urdfCol = m_data->m_bulletCollisionShape2UrdfCollision.find(colShape);
@@ -10054,7 +10062,7 @@ bool PhysicsServerCommandProcessor::processLoadTextureCommand(const struct Share
{
int width, height, n;
unsigned char* imageData = 0;
CommonFileIOInterface* fileIO = m_data->m_pluginManager.getFileIOInterface();
if (fileIO)
{
@@ -10083,7 +10091,7 @@ bool PhysicsServerCommandProcessor::processLoadTextureCommand(const struct Share
{
imageData = stbi_load(relativeFileName, &width, &height, &n, 3);
}
if (imageData)
{
texH->m_openglTextureId = m_data->m_guiHelper->registerTexture(imageData, width, height);
@@ -10161,7 +10169,7 @@ bool PhysicsServerCommandProcessor::processRestoreStateCommand(const struct Shar
buffer.reserve(1024);
if (fileIO)
{
int fileId = -1;
found = fileIO->findResourcePath(clientCmd.m_fileArguments.m_fileName, fileName, 1024);
if (found)

27
examples/SharedMemory/PhysicsServerExample.cpp
View File

@@ -27,7 +27,6 @@ bool gEnableRendering = true;
bool gActivedVRRealTimeSimulation = false;
bool gEnableSyncPhysicsRendering = true;
bool gEnableUpdateDebugDrawLines = true;
static int gCamVisualizerWidth = 228;
static int gCamVisualizerHeight = 192;
@@ -177,6 +176,7 @@ struct MotionArgs
{
MotionArgs()
: m_debugDrawFlags(0),
m_enableUpdateDebugDrawLines(true),
m_physicsServerPtr(0)
{
for (int i = 0; i < MAX_VR_CONTROLLERS; i++)
@@ -201,7 +201,7 @@ struct MotionArgs
b3CriticalSection* m_csGUI;
int m_debugDrawFlags;
bool m_enableUpdateDebugDrawLines;
btAlignedObjectArray<MyMouseCommand> m_mouseCommands;
b3VRControllerEvent m_vrControllerEvents[MAX_VR_CONTROLLERS];
@@ -424,13 +424,13 @@ void MotionThreadFunc(void* userPtr, void* lsMemory)
args->m_physicsServerPtr->stepSimulationRealTime(deltaTimeInSeconds, args->m_sendVrControllerEvents, numSendVrControllers, keyEvents, args->m_sendKeyEvents.size(), mouseEvents, args->m_sendMouseEvents.size());
}
{
if (gEnableUpdateDebugDrawLines)
args->m_csGUI->lock();
if (args->m_enableUpdateDebugDrawLines)
{
args->m_csGUI->lock();
args->m_physicsServerPtr->physicsDebugDraw(args->m_debugDrawFlags);
gEnableUpdateDebugDrawLines = false;
args->m_csGUI->unlock();
args->m_enableUpdateDebugDrawLines = false;
}
args->m_csGUI->unlock();
}
deltaTimeInSeconds = 0;
}
@@ -1737,6 +1737,11 @@ void PhysicsServerExample::initPhysics()
m_args[w].m_cs2 = m_threadSupport->createCriticalSection();
m_args[w].m_cs3 = m_threadSupport->createCriticalSection();
m_args[w].m_csGUI = m_threadSupport->createCriticalSection();
m_multiThreadedHelper->setCriticalSection(m_args[w].m_cs);
m_multiThreadedHelper->setCriticalSection2(m_args[w].m_cs2);
m_multiThreadedHelper->setCriticalSection3(m_args[w].m_cs3);
m_multiThreadedHelper->setCriticalSectionGUI(m_args[w].m_csGUI);
m_args[w].m_cs->lock();
m_args[w].m_cs->setSharedParam(0, eMotionIsUnInitialized);
m_args[w].m_cs->unlock();
@@ -1758,13 +1763,9 @@ void PhysicsServerExample::initPhysics()
#endif
}
}
m_args[0].m_cs->lock();
m_args[0].m_cs->setSharedParam(1, eGUIHelperIdle);
m_multiThreadedHelper->setCriticalSection(m_args[0].m_cs);
m_multiThreadedHelper->setCriticalSection2(m_args[0].m_cs2);
m_multiThreadedHelper->setCriticalSection3(m_args[0].m_cs3);
m_multiThreadedHelper->setCriticalSectionGUI(m_args[0].m_csGUI);
m_args[0].m_cs->unlock();
m_args[0].m_cs2->lock();
{
@@ -2843,7 +2844,7 @@ void PhysicsServerExample::physicsDebugDraw(int debugDrawFlags)
//draw stuff and flush?
this->m_multiThreadedHelper->m_debugDraw->drawDebugDrawerLines();
m_args[0].m_debugDrawFlags = debugDrawFlags;
gEnableUpdateDebugDrawLines = true;
m_args[0].m_enableUpdateDebugDrawLines = true;
m_args[0].m_csGUI->unlock();
}
}

4
examples/SharedMemory/SharedMemoryPublic.h
View File

@@ -928,7 +928,7 @@ enum eFileIOTypes
};
//limits for vertices/indices in PyBullet::createCollisionShape
#define B3_MAX_NUM_VERTICES 1024
#define B3_MAX_NUM_INDICES 1024
#define B3_MAX_NUM_VERTICES 16
#define B3_MAX_NUM_INDICES 16
#endif //SHARED_MEMORY_PUBLIC_H

8
examples/Utils/ChromeTraceUtil.cpp
View File

@@ -174,7 +174,7 @@ void MyEnterProfileZoneFunc(const char* msg)
{
if (gProfileDisabled)
return;
#ifndef BT_NO_PROFILE
int threadId = btQuickprofGetCurrentThreadIndex2();
if (threadId < 0 || threadId >= BT_QUICKPROF_MAX_THREAD_COUNT)
return;
@@ -191,13 +191,13 @@ void MyEnterProfileZoneFunc(const char* msg)
gStartTimes[threadId][gStackDepths[threadId]] = 1 + gStartTimes[threadId][gStackDepths[threadId] - 1];
}
gStackDepths[threadId]++;
#endif
}
void MyLeaveProfileZoneFunc()
{
if (gProfileDisabled)
return;
#ifndef BT_NO_PROFILE
int threadId = btQuickprofGetCurrentThreadIndex2();
if (threadId < 0 || threadId >= BT_QUICKPROF_MAX_THREAD_COUNT)
return;
@@ -214,7 +214,7 @@ void MyLeaveProfileZoneFunc()
unsigned long long int endTime = clk.getTimeNanoseconds();
gTimings[threadId].addTiming(name, threadId, startTime, endTime);
#endif //BT_NO_PROFILE
}
void b3ChromeUtilsStartTimings()

22
examples/pybullet/examples/createObstacleCourse.py
View File

@@ -21,28 +21,6 @@ colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)
#convex mesh from obj
stoneId = p.createCollisionShape(p.GEOM_MESH,fileName="stone.obj")
#concave mesh from obj
stoneId = p.createCollisionShape(p.GEOM_MESH,fileName="stone.obj", flags=p.GEOM_FORCE_CONCAVE_TRIMESH)
verts=[[-0.246350, -0.246483, -0.000624],
[ -0.151407, -0.176325, 0.172867],
[ -0.246350, 0.249205, -0.000624],
[ -0.151407, 0.129477, 0.172867],
[ 0.249338, -0.246483, -0.000624],
[ 0.154395, -0.176325, 0.172867],
[ 0.249338, 0.249205, -0.000624],
[ 0.154395, 0.129477, 0.172867]]
#convex mesh from vertices
stoneConvexId = p.createCollisionShape(p.GEOM_MESH,vertices=verts)
indices=[0,3,2,3,6,2,7,4,6,5,0,4,6,0,2,3,5,7,0,1,3,3,7,6,7,5,4,5,1,0,6,4,0,3,1,5]
#concave mesh from vertices+indices
stoneConcaveId = p.createCollisionShape(p.GEOM_MESH,vertices=verts, indices=indices)
stoneId = stoneConvexId
#stoneId = stoneConcaveId
boxHalfLength = 0.5

533
examples/pybullet/gym/pybullet_envs/ARS/ars.py
View File

@@ -1,397 +1,142 @@
"""Internal implementation of the Augmented Random Search method."""
# AI 2018
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
os.sys.path.insert(0,currentdir)
from concurrent import futures
import copy
# Importing the libraries
import os
import time
import gym
import numpy as np
import logz
import utils
import optimizers
#from google3.pyglib import gfile
import policies
import shared_noise
import utility
class Worker(object):
"""Object class for parallel rollout generation."""
def __init__(self,
env_seed,
env_callback,
policy_params=None,
deltas=None,
rollout_length=1000,
delta_std=0.02):
# initialize OpenAI environment for each worker
self.env = env_callback()
self.env.seed(env_seed)
# each worker gets access to the shared noise table
# with independent random streams for sampling
# from the shared noise table.
self.deltas = shared_noise.SharedNoiseTable(deltas, env_seed + 7)
self.policy_params = policy_params
if policy_params['type'] == 'linear':
self.policy = policies.LinearPolicy(policy_params)
else:
raise NotImplementedError
self.delta_std = delta_std
self.rollout_length = rollout_length
def get_weights_plus_stats(self):
"""
Get current policy weights and current statistics of past states.
"""
assert self.policy_params['type'] == 'linear'
return self.policy.get_weights_plus_stats()
def rollout(self, shift=0., rollout_length=None):
"""Performs one rollout of maximum length rollout_length.
At each time-step it substracts shift from the reward.
"""
if rollout_length is None:
rollout_length = self.rollout_length
total_reward = 0.
steps = 0
ob = self.env.reset()
for i in range(rollout_length):
action = self.policy.act(ob)
ob, reward, done, _ = self.env.step(action)
steps += 1
total_reward += (reward - shift)
if done:
break
return total_reward, steps
def do_rollouts(self, w_policy, num_rollouts=1, shift=1, evaluate=False):
"""
Generate multiple rollouts with a policy parametrized by w_policy.
"""
print('Doing {} rollouts'.format(num_rollouts))
rollout_rewards, deltas_idx = [], []
steps = 0
for i in range(num_rollouts):
if evaluate:
self.policy.update_weights(w_policy)
deltas_idx.append(-1)
# set to false so that evaluation rollouts are not used for updating state statistics
self.policy.update_filter = False
# for evaluation we do not shift the rewards (shift = 0) and we use the
# default rollout length (1000 for the MuJoCo locomotion tasks)
reward, r_steps = self.rollout(
shift=0., rollout_length=self.rollout_length)
rollout_rewards.append(reward)
else:
idx, delta = self.deltas.get_delta(w_policy.size)
delta = (self.delta_std * delta).reshape(w_policy.shape)
deltas_idx.append(idx)
# set to true so that state statistics are updated
self.policy.update_filter = True
# compute reward and number of timesteps used for positive perturbation rollout
self.policy.update_weights(w_policy + delta)
pos_reward, pos_steps = self.rollout(shift=shift)
# compute reward and number of timesteps used for negative pertubation rollout
self.policy.update_weights(w_policy - delta)
neg_reward, neg_steps = self.rollout(shift=shift)
steps += pos_steps + neg_steps
rollout_rewards.append([pos_reward, neg_reward])
return {
'deltas_idx': deltas_idx,
'rollout_rewards': rollout_rewards,
'steps': steps
}
def stats_increment(self):
self.policy.observation_filter.stats_increment()
return
def get_weights(self):
return self.policy.get_weights()
def get_filter(self):
return self.policy.observation_filter
def sync_filter(self, other):
self.policy.observation_filter.sync(other)
return
class ARSLearner(object):
"""
Object class implementing the ARS algorithm.
"""
def __init__(self,
env_callback,
policy_params=None,
num_workers=32,
num_deltas=320,
deltas_used=320,
delta_std=0.02,
logdir=None,
rollout_length=1000,
step_size=0.01,
shift='constant zero',
params=None,
seed=123):
logz.configure_output_dir(logdir)
# params_to_save = copy.deepcopy(params)
# params_to_save['env'] = None
# logz.save_params(params_to_save)
utility.save_config(params, logdir)
env = env_callback()
self.timesteps = 0
self.action_size = env.action_space.shape[0]
self.ob_size = env.observation_space.shape[0]
self.num_deltas = num_deltas
self.deltas_used = deltas_used
self.rollout_length = rollout_length
self.step_size = step_size
self.delta_std = delta_std
self.logdir = logdir
self.shift = shift
self.params = params
self.max_past_avg_reward = float('-inf')
self.num_episodes_used = float('inf')
# create shared table for storing noise
print('Creating deltas table.')
deltas = shared_noise.create_shared_noise()
self.deltas = shared_noise.SharedNoiseTable(deltas, seed=seed + 3)
print('Created deltas table.')
# initialize workers with different random seeds
print('Initializing workers.')
self.num_workers = num_workers
self.workers = [
Worker(
seed + 7 * i,
env_callback=env_callback,
policy_params=policy_params,
deltas=deltas,
rollout_length=rollout_length,
delta_std=delta_std) for i in range(num_workers)
]
# initialize policy
if policy_params['type'] == 'linear':
self.policy = policies.LinearPolicy(policy_params)
self.w_policy = self.policy.get_weights()
else:
raise NotImplementedError
# initialize optimization algorithm
self.optimizer = optimizers.SGD(self.w_policy, self.step_size)
print('Initialization of ARS complete.')
def aggregate_rollouts(self, num_rollouts=None, evaluate=False):
"""
Aggregate update step from rollouts generated in parallel.
"""
if num_rollouts is None:
num_deltas = self.num_deltas
else:
num_deltas = num_rollouts
results_one = [] #rollout_ids_one
results_two = [] #rollout_ids_two
t1 = time.time()
num_rollouts = int(num_deltas / self.num_workers)
# if num_rollouts > 0:
# with futures.ThreadPoolExecutor(
# max_workers=self.num_workers) as executor:
# workers = [
# executor.submit(
# worker.do_rollouts,
# self.w_policy,
# num_rollouts=num_rollouts,
# shift=self.shift,
# evaluate=evaluate) for worker in self.workers
# ]
# for worker in futures.as_completed(workers):
# results_one.append(worker.result())
#
# workers = [
# executor.submit(
# worker.do_rollouts,
# self.w_policy,
# num_rollouts=1,
# shift=self.shift,
# evaluate=evaluate)
# for worker in self.workers[:(num_deltas % self.num_workers)]
# ]
# for worker in futures.as_completed(workers):
# results_two.append(worker.result())
# parallel generation of rollouts
rollout_ids_one = [
worker.do_rollouts(
self.w_policy,
num_rollouts=num_rollouts,
shift=self.shift,
evaluate=evaluate) for worker in self.workers
]
rollout_ids_two = [
worker.do_rollouts(
self.w_policy, num_rollouts=1, shift=self.shift, evaluate=evaluate)
for worker in self.workers[:(num_deltas % self.num_workers)]
]
results_one = rollout_ids_one
results_two = rollout_ids_two
# gather results
rollout_rewards, deltas_idx = [], []
for result in results_one:
if not evaluate:
self.timesteps += result['steps']
deltas_idx += result['deltas_idx']
rollout_rewards += result['rollout_rewards']
for result in results_two:
if not evaluate:
self.timesteps += result['steps']
deltas_idx += result['deltas_idx']
rollout_rewards += result['rollout_rewards']
deltas_idx = np.array(deltas_idx)
rollout_rewards = np.array(rollout_rewards, dtype=np.float64)
print('Maximum reward of collected rollouts:', rollout_rewards.max())
info_dict = {
"max_reward": rollout_rewards.max()
}
t2 = time.time()
print('Time to generate rollouts:', t2 - t1)
if evaluate:
return rollout_rewards
# select top performing directions if deltas_used < num_deltas
max_rewards = np.max(rollout_rewards, axis=1)
if self.deltas_used > self.num_deltas:
self.deltas_used = self.num_deltas
idx = np.arange(max_rewards.size)[max_rewards >= np.percentile(
max_rewards, 100 * (1 - (self.deltas_used / self.num_deltas)))]
deltas_idx = deltas_idx[idx]
rollout_rewards = rollout_rewards[idx, :]
# normalize rewards by their standard deviation
rollout_rewards /= np.std(rollout_rewards)
t1 = time.time()
# aggregate rollouts to form g_hat, the gradient used to compute SGD step
g_hat, count = utils.batched_weighted_sum(
rollout_rewards[:, 0] - rollout_rewards[:, 1],
(self.deltas.get(idx, self.w_policy.size) for idx in deltas_idx),
batch_size=500)
g_hat /= deltas_idx.size
t2 = time.time()
print('time to aggregate rollouts', t2 - t1)
return g_hat, info_dict
def train_step(self):
"""
Perform one update step of the policy weights.
"""
g_hat, info_dict = self.aggregate_rollouts()
print('Euclidean norm of update step:', np.linalg.norm(g_hat))
self.w_policy -= self.optimizer._compute_step(g_hat).reshape(
self.w_policy.shape)
return info_dict
def train(self, num_iter):
start = time.time()
for i in range(num_iter):
t1 = time.time()
info_dict = self.train_step()
t2 = time.time()
print('total time of one step', t2 - t1)
print('iter ', i, ' done')
# record statistics every 10 iterations
if ((i) % 10 == 0):
rewards = self.aggregate_rollouts(num_rollouts=8, evaluate=True)
w = self.workers[0].get_weights_plus_stats()
checkpoint_filename = os.path.join(
self.logdir, 'lin_policy_plus_{:03d}.npz'.format(i))
print('Save checkpoints to {}...', checkpoint_filename)
checkpoint_file = open(checkpoint_filename, 'w')
np.savez(checkpoint_file, w)
print('End save checkpoints.')
print(sorted(self.params.items()))
logz.log_tabular('Time', time.time() - start)
logz.log_tabular('Iteration', i + 1)
logz.log_tabular('AverageReward', np.mean(rewards))
logz.log_tabular('StdRewards', np.std(rewards))
logz.log_tabular('MaxRewardRollout', np.max(rewards))
logz.log_tabular('MinRewardRollout', np.min(rewards))
logz.log_tabular('timesteps', self.timesteps)
logz.dump_tabular()
t1 = time.time()
# get statistics from all workers
for j in range(self.num_workers):
self.policy.observation_filter.update(self.workers[j].get_filter())
self.policy.observation_filter.stats_increment()
# make sure master filter buffer is clear
self.policy.observation_filter.clear_buffer()
# sync all workers
#filter_id = ray.put(self.policy.observation_filter)
setting_filters_ids = [
worker.sync_filter(self.policy.observation_filter)
for worker in self.workers
]
# waiting for sync of all workers
#ray.get(setting_filters_ids)
increment_filters_ids = [
worker.stats_increment() for worker in self.workers
]
# waiting for increment of all workers
#ray.get(increment_filters_ids)
t2 = time.time()
print('Time to sync statistics:', t2 - t1)
return info_dict
import gym
from gym import wrappers
import pybullet_envs
# Setting the Hyper Parameters
class Hp():
def __init__(self):
self.nb_steps = 1000
self.episode_length = 1000
self.learning_rate = 0.02
self.nb_directions = 16
self.nb_best_directions = 16
assert self.nb_best_directions <= self.nb_directions
self.noise = 0.03
self.seed = 1
self.env_name = 'HalfCheetahBulletEnv-v0'
# Normalizing the states
class Normalizer():
def __init__(self, nb_inputs):
self.n = np.zeros(nb_inputs)
self.mean = np.zeros(nb_inputs)
self.mean_diff = np.zeros(nb_inputs)
self.var = np.zeros(nb_inputs)
def observe(self, x):
self.n += 1.
last_mean = self.mean.copy()
self.mean += (x - self.mean) / self.n
self.mean_diff += (x - last_mean) * (x - self.mean)
self.var = (self.mean_diff / self.n).clip(min = 1e-2)
def normalize(self, inputs):
obs_mean = self.mean
obs_std = np.sqrt(self.var)
return (inputs - obs_mean) / obs_std
# Building the AI
class Policy():
def __init__(self, input_size, output_size):
self.theta = np.zeros((output_size, input_size))
print("self.theta=",self.theta)
def evaluate(self, input, delta = None, direction = None):
if direction is None:
return np.clip(self.theta.dot(input), -1.0, 1.0)
elif direction == "positive":
return np.clip((self.theta + hp.noise*delta).dot(input), -1.0, 1.0)
else:
return np.clip((self.theta - hp.noise*delta).dot(input), -1.0, 1.0)
def sample_deltas(self):
return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
def update(self, rollouts, sigma_r):
step = np.zeros(self.theta.shape)
for r_pos, r_neg, d in rollouts:
step += (r_pos - r_neg) * d
self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
# Exploring the policy on one specific direction and over one episode
def explore(env, normalizer, policy, direction = None, delta = None):
state = env.reset()
done = False
num_plays = 0.
sum_rewards = 0
while not done and num_plays < hp.episode_length:
normalizer.observe(state)
state = normalizer.normalize(state)
action = policy.evaluate(state, delta, direction)
state, reward, done, _ = env.step(action)
reward = max(min(reward, 1), -1)
sum_rewards += reward
num_plays += 1
return sum_rewards
# Training the AI
def train(env, policy, normalizer, hp):
for step in range(hp.nb_steps):
# Initializing the perturbations deltas and the positive/negative rewards
deltas = policy.sample_deltas()
positive_rewards = [0] * hp.nb_directions
negative_rewards = [0] * hp.nb_directions
# Getting the positive rewards in the positive directions
for k in range(hp.nb_directions):
positive_rewards[k] = explore(env, normalizer, policy, direction = "positive", delta = deltas[k])
# Getting the negative rewards in the negative/opposite directions
for k in range(hp.nb_directions):
negative_rewards[k] = explore(env, normalizer, policy, direction = "negative", delta = deltas[k])
# Gathering all the positive/negative rewards to compute the standard deviation of these rewards
all_rewards = np.array(positive_rewards + negative_rewards)
sigma_r = all_rewards.std()
# Sorting the rollouts by the max(r_pos, r_neg) and selecting the best directions
scores = {k:max(r_pos, r_neg) for k,(r_pos,r_neg) in enumerate(zip(positive_rewards, negative_rewards))}
order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions]
rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
# Updating our policy
policy.update(rollouts, sigma_r)
# Printing the final reward of the policy after the update
reward_evaluation = explore(env, normalizer, policy)
print('Step:', step, 'Reward:', reward_evaluation)
# Running the main code
def mkdir(base, name):
path = os.path.join(base, name)
if not os.path.exists(path):
os.makedirs(path)
return path
work_dir = mkdir('exp', 'brs')
monitor_dir = mkdir(work_dir, 'monitor')
hp = Hp()
np.random.seed(hp.seed)
env = gym.make(hp.env_name)
# env.render(mode = "human")
#env = wrappers.Monitor(env, monitor_dir, force = True)
nb_inputs = env.observation_space.shape[0]
nb_outputs = env.action_space.shape[0]
policy = Policy(nb_inputs, nb_outputs)
normalizer = Normalizer(nb_inputs)
train(env, policy, normalizer, hp)

62
examples/pybullet/gym/pybullet_envs/ARS/ars_server.py
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@@ -1,62 +0,0 @@
"""
blaze build -c opt //experimental/users/jietan/ARS:ars_server
blaze-bin/experimental/users/jietan/ARS/ars_server \
--config_name=MINITAUR_GYM_CONFIG
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from absl import app
from absl import flags
from concurrent import futures
import grpc
from grpc import loas2
from google3.robotics.reinforcement_learning.minitaur.envs import minitaur_gym_env
from google3.robotics.reinforcement_learning.minitaur.envs import minitaur_reactive_env
from google3.robotics.reinforcement_learning.minitaur.envs.env_randomizers import minitaur_env_randomizer
from google3.robotics.reinforcement_learning.minitaur.envs.env_randomizers import minitaur_env_randomizer_from_config as randomizer_config_lib
from google3.experimental.users.jietan.ARS import ars_evaluation_service_pb2_grpc
from google3.experimental.users.jietan.ARS import ars_evaluation_service
FLAGS = flags.FLAGS
flags.DEFINE_integer("server_id", 0, "number of servers")
flags.DEFINE_integer("port", 20000, "port number.")
flags.DEFINE_string("config_name", None, "The name of the config dictionary.")
flags.DEFINE_bool('run_on_borg', False,
'Whether the servers are running on borg.')
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
def main(unused_argv):
servers = []
server_creds = loas2.loas2_server_credentials()
port = FLAGS.port
if not FLAGS.run_on_borg:
port = 20000 + FLAGS.server_id
server = grpc.server(
futures.ThreadPoolExecutor(max_workers=10), ports=(port,))
servicer = ars_evaluation_service.ParameterEvaluationServicer(
FLAGS.config_name, worker_id=FLAGS.server_id)
ars_evaluation_service_pb2_grpc.add_EvaluationServicer_to_server(
servicer, server)
server.add_secure_port("[::]:{}".format(port), server_creds)
servers.append(server)
server.start()
print("Start server {}".format(FLAGS.server_id))
# prevent the main thread from exiting
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
for server in servers:
server.stop(0)
if __name__ == "__main__":
app.run(main)

83
examples/pybullet/gym/pybullet_envs/ARS/config_ars.py
View File

@@ -1,83 +0,0 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
from pybullet_envs.minitaur.envs import minitaur_gym_env
from pybullet_envs.minitaur.envs import minitaur_reactive_env
from pybullet_envs.minitaur.envs.env_randomizers import minitaur_env_randomizer
from pybullet_envs.minitaur.envs.env_randomizers import minitaur_env_randomizer_from_config as randomizer_config_lib
MAX_LENGTH = 1000
def merge_two_dicts(x, y):
"""Given two dicts, merge them into a new dict as a shallow copy."""
z = dict(x)
z.update(y)
return z
# The default configurations.
DEFAULT_CONFIG = dict(
num_workers=8,
num_directions=8,
num_iterations=1000,
deltas_used=8,
step_size=0.02,
delta_std=0.03,
rollout_length=MAX_LENGTH,
shift=0,
seed=237,
policy_type="linear",
filter="MeanStdFilter",
)
# Configuration specific to minitaur_gym_env.MinitaurGymEnv class.
MINITAUR_GYM_CONFIG_ADDITIONS = dict(
env=functools.partial(
minitaur_gym_env.MinitaurGymEnv,
urdf_version=minitaur_gym_env.DERPY_V0_URDF_VERSION,
accurate_motor_model_enabled=True,
motor_overheat_protection=True,
pd_control_enabled=True,
env_randomizer=None,#minitaur_env_randomizer.MinitaurEnvRandomizer(),
render=False,
num_steps_to_log=MAX_LENGTH))
MINITAUR_GYM_CONFIG = merge_two_dicts(DEFAULT_CONFIG,
MINITAUR_GYM_CONFIG_ADDITIONS)
# Configuration specific to MinitaurReactiveEnv class.
MINITAUR_REACTIVE_CONFIG_ADDITIONS = dict(
env=functools.partial(
minitaur_reactive_env.MinitaurReactiveEnv,
urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
energy_weight=0.005,
accurate_motor_model_enabled=True,
pd_latency=0.003,
control_latency=0.02,
motor_kd=0.015,
remove_default_joint_damping=True,
env_randomizer=None,
render=False,
num_steps_to_log=MAX_LENGTH))
MINITAUR_REACTIVE_CONFIG = merge_two_dicts(DEFAULT_CONFIG,
MINITAUR_REACTIVE_CONFIG_ADDITIONS)
# Configuration specific to MinitaurReactiveEnv class with randomizer.
MINITAUR_REACTIVE_RANDOMIZER_CONFIG_ADDITIONS = dict(
env=functools.partial(
minitaur_reactive_env.MinitaurReactiveEnv,
urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
energy_weight=0.005,
accurate_motor_model_enabled=True,
pd_latency=0.003,
control_latency=0.02,
motor_kd=0.015,
remove_default_joint_damping=True,
env_randomizer=randomizer_config_lib.MinitaurEnvRandomizerFromConfig(),
render=False,
num_steps_to_log=MAX_LENGTH))
MINITAUR_REACTIVE_RANDOMIZER_CONFIG = merge_two_dicts(
DEFAULT_CONFIG, MINITAUR_REACTIVE_RANDOMIZER_CONFIG_ADDITIONS)

99
examples/pybullet/gym/pybullet_envs/ARS/eval_ars.py
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@@ -1,99 +0,0 @@
"""
blaze run -c opt //experimental/users/jietan/ARS:eval_ars -- \
--logdir=/cns/ij-d/home/jietan/experiment/ARS/ars_react_nr01.191950338.191950550/ \
--checkpoint=lin_policy_plus_990.npz \
--num_rollouts=10
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os, inspect
import time
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
os.sys.path.insert(0,currentdir)
from absl import app
from absl import flags
import pdb
import os
import numpy as np
import gym
import config_ars
import utility
import policies
FLAGS = flags.FLAGS
flags.DEFINE_string('logdir', None, 'The path of the checkpoint.')
flags.DEFINE_string('checkpoint', None, 'The file name of the checkpoint.')
flags.DEFINE_integer('num_rollouts', 1, 'The number of rollouts.')
def main(argv):
del argv # Unused.
print('loading and building expert policy')
checkpoint_file = os.path.join(FLAGS.logdir, FLAGS.checkpoint)
lin_policy = np.load(checkpoint_file, encoding='bytes')
lin_policy = lin_policy.items()[0][1]
M = lin_policy[0]
# mean and std of state vectors estimated online by ARS.
mean = lin_policy[1]
std = lin_policy[2]
config = utility.load_config(FLAGS.logdir)
print("config=",config)
env = config['env'](hard_reset=True, render=True)
ob_dim = env.observation_space.shape[0]
ac_dim = env.action_space.shape[0]
# set policy parameters. Possible filters: 'MeanStdFilter' for v2, 'NoFilter' for v1.
policy_params = {
'type': 'linear',
'ob_filter': config['filter'],
'ob_dim': ob_dim,
'ac_dim': ac_dim,
"weights": M,
"mean": mean,
"std": std,
}
policy = policies.LinearPolicy(policy_params, update_filter=False)
returns = []
observations = []
actions = []
for i in range(FLAGS.num_rollouts):
print('iter', i)
obs = env.reset()
done = False
totalr = 0.
steps = 0
while not done:
action = policy.act(obs)
observations.append(obs)
actions.append(action)
obs, r, done, _ = env.step(action)
time.sleep(1./100.)
totalr += r
steps += 1
if steps % 100 == 0:
print('%i/%i' % (steps, config['rollout_length']))
if steps >= config['rollout_length']:
break
returns.append(totalr)
print('returns', returns)
print('mean return', np.mean(returns))
print('std of return', np.std(returns))
if __name__ == '__main__':
flags.mark_flag_as_required('logdir')
flags.mark_flag_as_required('checkpoint')
app.run(main)

280
examples/pybullet/gym/pybullet_envs/ARS/filter.py
View File

@@ -1,280 +0,0 @@
# Code in this file is copied and adapted from
# https://github.com/ray-project/ray/blob/master/python/ray/rllib/utils/filter.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
class Filter(object):
"""Processes input, possibly statefully."""
def update(self, other, *args, **kwargs):
"""Updates self with "new state" from other filter."""
raise NotImplementedError
def copy(self):
"""Creates a new object with same state as self.
Returns:
copy (Filter): Copy of self"""
raise NotImplementedError
def sync(self, other):
"""Copies all state from other filter to self."""
raise NotImplementedError
class NoFilter(Filter):
def __init__(self, *args):
pass
def __call__(self, x, update=True):
return np.asarray(x, dtype = np.float64)
def update(self, other, *args, **kwargs):
pass
def copy(self):
return self
def sync(self, other):
pass
def stats_increment(self):
pass
def clear_buffer(self):
pass
def get_stats(self):
return 0, 1
@property
def mean(self):
return 0
@property
def var(self):
return 1
@property
def std(self):
return 1
# http://www.johndcook.com/blog/standard_deviation/
class RunningStat(object):
def __init__(self, shape=None):
self._n = 0
self._M = np.zeros(shape, dtype = np.float64)
self._S = np.zeros(shape, dtype = np.float64)
self._M2 = np.zeros(shape, dtype = np.float64)
def copy(self):
other = RunningStat()
other._n = self._n
other._M = np.copy(self._M)
other._S = np.copy(self._S)
return other
def push(self, x):
x = np.asarray(x)
# Unvectorized update of the running statistics.
assert x.shape == self._M.shape, ("x.shape = {}, self.shape = {}"
.format(x.shape, self._M.shape))
n1 = self._n
self._n += 1
if self._n == 1:
self._M[...] = x
else:
delta = x - self._M
deltaM2 = np.square(x) - self._M2
self._M[...] += delta / self._n
self._S[...] += delta * delta * n1 / self._n
def update(self, other):
n1 = self._n
n2 = other._n
n = n1 + n2
delta = self._M - other._M
delta2 = delta * delta
M = (n1 * self._M + n2 * other._M) / n
S = self._S + other._S + delta2 * n1 * n2 / n
self._n = n
self._M = M
self._S = S
def __repr__(self):
return '(n={}, mean_mean={}, mean_std={})'.format(
self.n, np.mean(self.mean), np.mean(self.std))
@property
def n(self):
return self._n
@property
def mean(self):
return self._M
@property
def var(self):
return self._S / (self._n - 1) if self._n > 1 else np.square(self._M)
@property
def std(self):
return np.sqrt(self.var)
@property
def shape(self):
return self._M.shape
class MeanStdFilter(Filter):
"""Keeps track of a running mean for seen states"""
def __init__(self, shape, demean=True, destd=True):
self.shape = shape
self.demean = demean
self.destd = destd
self.rs = RunningStat(shape)
# In distributed rollouts, each worker sees different states.
# The buffer is used to keep track of deltas amongst all the
# observation filters.
self.buffer = RunningStat(shape)
self.mean = np.zeros(shape, dtype = np.float64)
self.std = np.ones(shape, dtype = np.float64)
def clear_buffer(self):
self.buffer = RunningStat(self.shape)
return
def update(self, other, copy_buffer=False):
"""Takes another filter and only applies the information from the
buffer.
Using notation `F(state, buffer)`
Given `Filter1(x1, y1)` and `Filter2(x2, yt)`,
`update` modifies `Filter1` to `Filter1(x1 + yt, y1)`
If `copy_buffer`, then `Filter1` is modified to
`Filter1(x1 + yt, yt)`.
"""
self.rs.update(other.buffer)
if copy_buffer:
self.buffer = other.buffer.copy()
return
def copy(self):
"""Returns a copy of Filter."""
other = MeanStdFilter(self.shape)
other.demean = self.demean
other.destd = self.destd
other.rs = self.rs.copy()
other.buffer = self.buffer.copy()
return other
def sync(self, other):
"""Syncs all fields together from other filter.
Using notation `F(state, buffer)`
Given `Filter1(x1, y1)` and `Filter2(x2, yt)`,
`sync` modifies `Filter1` to `Filter1(x2, yt)`
"""
assert other.shape == self.shape, "Shapes don't match!"
self.demean = other.demean
self.destd = other.destd
self.rs = other.rs.copy()
self.buffer = other.buffer.copy()
return
def __call__(self, x, update=True):
x = np.asarray(x, dtype = np.float64)
if update:
if len(x.shape) == len(self.rs.shape) + 1:
# The vectorized case.
for i in range(x.shape[0]):
self.rs.push(x[i])
self.buffer.push(x[i])
else:
# The unvectorized case.
self.rs.push(x)
self.buffer.push(x)
if self.demean:
x = x - self.mean
if self.destd:
x = x / (self.std + 1e-8)
return x
def stats_increment(self):
self.mean = self.rs.mean
self.std = self.rs.std
# Set values for std less than 1e-7 to +inf to avoid
# dividing by zero. State elements with zero variance
# are set to zero as a result.
self.std[self.std < 1e-7] = float("inf")
return
def get_stats(self):
return self.rs.mean, (self.rs.std + 1e-8)
def __repr__(self):
return 'MeanStdFilter({}, {}, {}, {}, {}, {})'.format(
self.shape, self.demean,
self.rs, self.buffer)
def get_filter(filter_config, shape = None):
if filter_config == "MeanStdFilter":
return MeanStdFilter(shape)
elif filter_config == "NoFilter":
return NoFilter()
else:
raise Exception("Unknown observation_filter: " +
str(filter_config))
def test_running_stat():
for shp in ((), (3,), (3, 4)):
li = []
rs = RunningStat(shp)
for _ in range(5):
val = np.random.randn(*shp)
rs.push(val)
li.append(val)
m = np.mean(li, axis=0)
assert np.allclose(rs.mean, m)
v = np.square(m) if (len(li) == 1) else np.var(li, ddof=1, axis=0)
assert np.allclose(rs.var, v)
def test_combining_stat():
for shape in [(), (3,), (3, 4)]:
li = []
rs1 = RunningStat(shape)
rs2 = RunningStat(shape)
rs = RunningStat(shape)
for _ in range(5):
val = np.random.randn(*shape)
rs1.push(val)
rs.push(val)
li.append(val)
for _ in range(9):
rs2.push(val)
rs.push(val)
li.append(val)
rs1.update(rs2)
assert np.allclose(rs.mean, rs1.mean)
assert np.allclose(rs.std, rs1.std)
test_running_stat()
test_combining_stat()

29
examples/pybullet/gym/pybullet_envs/ARS/log/config.yaml
View File

@@ -1,29 +0,0 @@
delta_std: 0.03
deltas_used: 8
env: !!python/object/apply:functools.partial
args:
- &id001 !!python/name:pybullet_envs.minitaur.envs.minitaur_reactive_env.MinitaurReactiveEnv ''
state: !!python/tuple
- *id001
- !!python/tuple []
- accurate_motor_model_enabled: true
control_latency: 0.02
energy_weight: 0.005
env_randomizer: null
motor_kd: 0.015
num_steps_to_log: 1000
pd_latency: 0.003
remove_default_joint_damping: true
render: false
urdf_version: rainbow_dash_v0
- null
filter: MeanStdFilter
num_directions: 8
num_iterations: 1000
num_workers: 8
policy_type: linear
rollout_length: 1000
seed: 237
shift: 0
step_size: 0.02

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examples/pybullet/gym/pybullet_envs/ARS/log/lin_policy_plus_990_bla.npz
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104
examples/pybullet/gym/pybullet_envs/ARS/logz.py
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@@ -1,104 +0,0 @@
# Code in this file is copied and adapted from
# https://github.com/berkeleydeeprlcourse
import json
"""
Some simple logging functionality, inspired by rllab's logging.
Assumes that each diagnostic gets logged each iteration
Call logz.configure_output_dir() to start logging to a
tab-separated-values file (some_folder_name/log.txt)
"""
import os.path as osp, shutil, time, atexit, os, subprocess
color2num = dict(
gray=30,
red=31,
green=32,
yellow=33,
blue=34,
magenta=35,
cyan=36,
white=37,
crimson=38
)
def colorize(string, color, bold=False, highlight=False):
attr = []
num = color2num[color]
if highlight: num += 10
attr.append(str(num))
if bold: attr.append('1')
return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string)
class G(object):
output_dir = None
output_file = None
first_row = True
log_headers = []
log_current_row = {}
def configure_output_dir(d=None):
"""
Set output directory to d, or to /tmp/somerandomnumber if d is None
"""
G.first_row = True
G.log_headers = []
G.log_current_row = {}
G.output_dir = d or "/tmp/experiments/%i"%int(time.time())
if not osp.exists(G.output_dir):
os.makedirs(G.output_dir)
G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w')
atexit.register(G.output_file.close)
print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True))
def log_tabular(key, val):
"""
Log a value of some diagnostic
Call this once for each diagnostic quantity, each iteration
"""
if G.first_row:
G.log_headers.append(key)
else:
assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key
assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key
G.log_current_row[key] = val
def save_params(params):
with open(osp.join(G.output_dir, "params.json"), 'w') as out:
out.write(json.dumps(params, separators=(',\n','\t:\t'), sort_keys=True))
def dump_tabular():
"""
Write all of the diagnostics from the current iteration
"""
vals = []
key_lens = [len(key) for key in G.log_headers]
max_key_len = max(15,max(key_lens))
keystr = '%'+'%d'%max_key_len
fmt = "| " + keystr + "s | %15s |"
n_slashes = 22 + max_key_len
print("-"*n_slashes)
for key in G.log_headers:
val = G.log_current_row.get(key, "")
if hasattr(val, "__float__"): valstr = "%8.3g"%val
else: valstr = val
print(fmt%(key, valstr))
vals.append(val)
print("-"*n_slashes)
if G.output_file is not None:
if G.first_row:
G.output_file.write("\t".join(G.log_headers))
G.output_file.write("\n")
G.output_file.write("\t".join(map(str,vals)))
G.output_file.write("\n")
G.output_file.flush()
G.log_current_row.clear()
G.first_row=False

35
examples/pybullet/gym/pybullet_envs/ARS/optimizers.py
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@@ -1,35 +0,0 @@
# Code in this file is copied and adapted from
# https://github.com/openai/evolution-strategies-starter.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
# OPTIMIZERS FOR MINIMIZING OBJECTIVES
class Optimizer(object):
def __init__(self, w_policy):
self.w_policy = w_policy.flatten()
self.dim = w_policy.size
self.t = 0
def update(self, globalg):
self.t += 1
step = self._compute_step(globalg)
ratio = np.linalg.norm(step) / (np.linalg.norm(self.w_policy) + 1e-5)
return self.w_policy + step, ratio
def _compute_step(self, globalg):
raise NotImplementedError
class SGD(Optimizer):
def __init__(self, pi, stepsize):
Optimizer.__init__(self, pi)
self.stepsize = stepsize
def _compute_step(self, globalg):
step = -self.stepsize * globalg
return step

72
examples/pybullet/gym/pybullet_envs/ARS/policies.py
View File

@@ -1,72 +0,0 @@
"""
Policy class for computing action from weights and observation vector.
Horia Mania --- hmania@berkeley.edu
Aurelia Guy
Benjamin Recht
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import filter
class Policy(object):
def __init__(self, policy_params):
self.ob_dim = policy_params['ob_dim']
self.ac_dim = policy_params['ac_dim']
self.weights = np.empty(0)
# a filter for updating statistics of the observations and normalizing
# inputs to the policies
self.observation_filter = filter.get_filter(
policy_params['ob_filter'], shape=(self.ob_dim,))
self.update_filter = True
def update_weights(self, new_weights):
self.weights[:] = new_weights[:]
return
def get_weights(self):
return self.weights
def get_observation_filter(self):
return self.observation_filter
def act(self, ob):
raise NotImplementedError
def copy(self):
raise NotImplementedError
class LinearPolicy(Policy):
"""
Linear policy class that computes action as <w, ob>.
"""
def __init__(self, policy_params, update_filter=True):
Policy.__init__(self, policy_params)
self.weights = np.zeros(self.ac_dim * self.ob_dim, dtype=np.float64)
if "weights" in policy_params:
self.weights = policy_params["weights"]
if "mean" in policy_params:
self.observation_filter.mean = policy_params["mean"]
if "std" in policy_params:
self.observation_filter.std = policy_params["std"]
self.update_filter = update_filter
def act(self, ob):
ob = self.observation_filter(ob, update=self.update_filter)
matrix_weights = np.reshape(self.weights, (self.ac_dim, self.ob_dim))
return np.clip(np.dot(matrix_weights, ob), -1.0, 1.0)
def get_weights_plus_stats(self):
mu, std = self.observation_filter.get_stats()
aux = np.asarray([self.weights, mu, std])
return aux

40
examples/pybullet/gym/pybullet_envs/ARS/shared_noise.py
View File

@@ -1,40 +0,0 @@
"""
Code in this file is copied and adapted from
https://github.com/ray-project/ray/tree/master/python/ray/rllib/es
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def create_shared_noise():
"""
Create a large array of noise to be shared by all workers. Used
for avoiding the communication of the random perturbations delta.
"""
seed = 12345
count = 250000000
noise = np.random.RandomState(seed).randn(count).astype(np.float64)
return noise
class SharedNoiseTable(object):
def __init__(self, noise, seed = 11):
self.rg = np.random.RandomState(seed)
self.noise = noise
assert self.noise.dtype == np.float64
def get(self, i, dim):
return self.noise[i:i + dim]
def sample_index(self, dim):
return self.rg.randint(0, len(self.noise) - dim + 1)
def get_delta(self, dim):
idx = self.sample_index(dim)
return idx, self.get(idx, dim)

27
examples/pybullet/gym/pybullet_envs/ARS/start_ars_servers.py
View File

@@ -1,27 +0,0 @@
"""
blaze build -c opt //experimental/users/jietan/ARS:start_ars_servers
blaze-bin/experimental/users/jietan/ARS/start_ars_servers
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import subprocess
from absl import app
from absl import flags
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_servers", 8, "number of servers")
def main(argv):
del argv # Unused.
for server_id in xrange(FLAGS.num_servers):
args = ["blaze-bin/experimental/users/jietan/ARS/ars_server", "--config_name=MINITAUR_GYM_CONFIG", "--server_id={}".format(server_id), "--run_on_borg=False"]
subprocess.Popen(args)
if __name__ == '__main__':
app.run(main)

93
examples/pybullet/gym/pybullet_envs/ARS/train_ars.borg
View File

@@ -1,93 +0,0 @@
// Example borg file to do a parameter sweep.
//
// To run:
// echo `srcfs get_readonly`-`g4 p | head -1 | awk '{print $2}'`
// blaze build -c opt experimental/users/jietan/ARS:ars_server.par
// blaze build -c opt experimental/users/jietan/ARS:ars_client.par
// borgcfg --skip_confirmation --vars 'base_cl=191950338,my_cl=191950550,label=ars_react_nr01,config=MINITAUR_REACTIVE_CONFIG' experimental/users/jietan/ARS/train_ars.borg reload
// borgcfg --skip_confirmation --vars 'base_cl=191950338,my_cl=191950550,label=ars_react_rd01,config=MINITAUR_REACTIVE_RANDOMIZER_CONFIG' experimental/users/jietan/ARS/train_ars.borg reload
import '//production/borg/templates/lambda/buildtool_support.borg' as build
import '//production/borg/templates/lambda/dnsname.borg' as dns
vars = {
cell = 'atlanta'
charged_user = 'robotics'
base_cl = 0
my_cl = 0
label = external
user = real_username()
workers = 8
config = external
cns_home = "/cns/ij-d/home/%user%"
logdir = "%cns_home%/experiment/ARS/%label%.%base_cl%.%my_cl%/"
}
service augmented_random_search {
runtime {
cell = vars.cell
}
scheduling = {
priority = 100
batch_quota = {
strategy = 'RUN_SOON'
}
deadline = 3600 * 24
}
accounting = {
charged_user = vars.charged_user
}
requirements {
autopilot = true
}
params = {
mygoogle3 = build.google3dir(myfilename())
experiment_dir = 'experimental/users/jietan/ARS/'
}
job ars_server = {
runtime {
cell = vars.cell
}
name = real_username() + '_server_' + vars.label
replicas = vars.workers
binary_path = build.binfile_v2(params.mygoogle3,
params.experiment_dir + 'ars_server')
runfiles = binary_path + '.runfiles/google3/'
packages = {
package third_party = {
directory = runfiles + 'third_party/'
}
}
binary = build.binfile(params.mygoogle3,
params.experiment_dir + 'ars_server.par')
args = {
server_id = '%task%'
config_name = vars.config
port = '%port%'
run_on_borg = true
}
}
job ars_client = {
name = real_username() + '_client_' + vars.label
binary_path = build.binfile_v2(params.mygoogle3,
params.experiment_dir + 'ars_client')
runfiles = binary_path + '.runfiles/google3/'
packages = {
package third_party = {
directory = runfiles + 'third_party/'
}
}
binary = build.binfile(params.mygoogle3,
params.experiment_dir + 'ars_client.par')
args = {
server_address = dns.borg_dns_name(ars_server)
num_servers = vars.workers
config_name = vars.config
logdir = vars.logdir
run_on_borg = true
}
}
}

64
examples/pybullet/gym/pybullet_envs/ARS/train_ars.py
View File

@@ -1,64 +0,0 @@
"""
blaze build -c opt //experimental/users/jietan/ARS:train_ars
blaze-bin/experimental/users/jietan/ARS/train_ars \
--logdir=/cns/ij-d/home/jietan/experiment/ARS/test1 \
--config_name=MINITAUR_GYM_CONFIG
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
import ars
import config_ars
FLAGS = flags.FLAGS
flags.DEFINE_string('logdir', None, 'The directory to write the log file.')
flags.DEFINE_string('config_name', None, 'The name of the config dictionary')
def run_ars(config, logdir):
env = config["env"]()
ob_dim = env.observation_space.shape[0]
ac_dim = env.action_space.shape[0]
# set policy parameters. Possible filters: 'MeanStdFilter' for v2, 'NoFilter' for v1.
policy_params = {
'type': 'linear',
'ob_filter': config['filter'],
'ob_dim': ob_dim,
'ac_dim': ac_dim
}
ARS = ars.ARSLearner(
env_callback=config['env'],
policy_params=policy_params,
num_deltas=config['num_directions'],
deltas_used=config['deltas_used'],
step_size=config['step_size'],
delta_std=config['delta_std'],
logdir=logdir,
rollout_length=config['rollout_length'],
shift=config['shift'],
params=config,
seed=config['seed'])
return ARS.train(config['num_iterations'])
def main(argv):
del argv # Unused.
config = getattr(config_ars, FLAGS.config_name)
run_ars(config=config, logdir=FLAGS.logdir)
if __name__ == '__main__':
flags.mark_flag_as_required('logdir')
flags.mark_flag_as_required('config_name')
app.run(main)

29
examples/pybullet/gym/pybullet_envs/ARS/train_ars_test.py
View File

@@ -1,29 +0,0 @@
"""Tests for google3.experimental.users.jietan.ARS.train_ars.
blaze build -c opt //experimental/users/jietan/ARS:train_ars_test
blaze-bin/experimental/users/jietan/ARS/train_ars_test
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
from google3.testing.pybase import googletest
from google3.experimental.users.jietan.ARS import train_ars
from google3.experimental.users.jietan.ARS import config_ars
FLAGS = flags.FLAGS
MAX_RETURN_AFTER_TWO_ITEATIONS = 0.0890905394617
class TrainArsTest(googletest.TestCase):
def testArsTwoStepResult(self):
config = getattr(config_ars, "MINITAUR_REACTIVE_CONFIG")
config['num_iterations'] = 2
info = train_ars.run_ars(config=config, logdir=FLAGS.test_tmpdir)
print (info)
self.assertAlmostEqual(info["max_reward"], MAX_RETURN_AFTER_TWO_ITEATIONS)
if __name__ == '__main__':
googletest.main()

52
examples/pybullet/gym/pybullet_envs/ARS/utility.py
View File

@@ -1,52 +0,0 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import ruamel.yaml as yaml
def save_config(config, logdir):
"""Save a new configuration by name.
If a logging directory is specified, is will be created and the configuration
will be stored there. Otherwise, a log message will be printed.
Args:
config: Configuration object.
logdir: Location for writing summaries and checkpoints if specified.
Returns:
Configuration object.
"""
message = 'Start a new run and write summaries and checkpoints to {}.'
print(message.format(logdir))
config_path = os.path.join(logdir, 'config.yaml')
yaml.dump(config, config_path, default_flow_style=False)
return config
def load_config(logdir):
"""Load a configuration from the log directory.
Args:
logdir: The logging directory containing the configuration file.
Raises:
IOError: The logging directory does not contain a configuration file.
Returns:
Configuration object.
"""
config_path = logdir and os.path.join(logdir, 'config.yaml')
if not config_path:
message = (
'Cannot resume an existing run since the logging directory does not '
'contain a configuration file.')
raise IOError(message)
print("config_path=",config_path)
stream = open(config_path, 'r')
config = yaml.load(stream)
message = 'Resume run and write summaries and checkpoints to {}.'
print(message.format(logdir))
return config

28
examples/pybullet/gym/pybullet_envs/ARS/utils.py
View File

@@ -1,28 +0,0 @@
# Code in this file is copied and adapted from
# https://github.com/openai/evolution-strategies-starter.
import numpy as np
def itergroups(items, group_size):
assert group_size >= 1
group = []
for x in items:
group.append(x)
if len(group) == group_size:
yield tuple(group)
del group[:]
if group:
yield tuple(group)
def batched_weighted_sum(weights, vecs, batch_size):
total = 0
num_items_summed = 0
for batch_weights, batch_vecs in zip(itergroups(weights, batch_size),
itergroups(vecs, batch_size)):
assert len(batch_weights) == len(batch_vecs) <= batch_size
total += np.dot(np.asarray(batch_weights, dtype=np.float64),
np.asarray(batch_vecs, dtype=np.float64))
num_items_summed += len(batch_weights)
return total, num_items_summed

21
examples/pybullet/gym/pybullet_utils/examples/mjcf2urdf.py Normal file
View File

@@ -0,0 +1,21 @@
#rudimentary MuJoCo mjcf to ROS URDF converter using the UrdfEditor
import pybullet_utils.bullet_client as bc
import pybullet_data as pd
import pybullet_utils.urdfEditor as ed
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mjcf', help='MuJoCo xml file to be converted to URDF', default='mjcf/humanoid.xml')
args = parser.parse_args()
p = bc.BulletClient()
p.setAdditionalSearchPath(pd.getDataPath())
objs = p.loadMJCF(args.mjcf, flags=p.URDF_USE_IMPLICIT_CYLINDER)
for o in objs:
#print("o=",o, p.getBodyInfo(o), p.getNumJoints(o))
humanoid = objs[o]
ed0 = ed.UrdfEditor()
ed0.initializeFromBulletBody(humanoid, p._client)
ed0.saveUrdf(p.getBodyInfo(0)[1]+"_"+p.getBodyInfo(o)[0]+".urdf")

12
examples/pybullet/gym/pybullet_utils/urdfEditor.py
View File

@@ -1,7 +1,6 @@
import pybullet as p
import time
class UrdfInertial(object):
def __init__(self):
self.mass = 1
@@ -201,6 +200,10 @@ class UrdfEditor(object):
file.write("\t\t</inertial>\n")
def writeVisualShape(self,file,urdfVisual, precision=5):
#we don't support loading capsule types from visuals, so auto-convert from collision shape
if urdfVisual.geom_type == p.GEOM_CAPSULE:
return
file.write("\t\t<visual>\n")
str = '\t\t\t<origin rpy="{:.{prec}f} {:.{prec}f} {:.{prec}f}" xyz="{:.{prec}f} {:.{prec}f} {:.{prec}f}"/>\n'.format(\
urdfVisual.origin_rpy[0],urdfVisual.origin_rpy[1],urdfVisual.origin_rpy[2],
@@ -276,8 +279,13 @@ class UrdfEditor(object):
file.write("\">\n")
self.writeInertial(file,urdfLink.urdf_inertial)
hasCapsules = False
for v in urdfLink.urdf_visual_shapes:
self.writeVisualShape(file,v)
if (v.geom_type == p.GEOM_CAPSULE):
hasCapsules = True
if (not hasCapsules):
for v in urdfLink.urdf_visual_shapes:
self.writeVisualShape(file,v)
for c in urdfLink.urdf_collision_shapes:
self.writeCollisionShape(file,c)
file.write("\t</link>\n")

2
examples/pybullet/pybullet.c
View File

@@ -6464,7 +6464,7 @@ static PyObject* pybullet_createCollisionShape(PyObject* self, PyObject* args, P
{
pybullet_internalSetVector4d(collisionFrameOrientationObj, collisionFrameOrientation);
}
b3CreateVisualShapeSetChildTransform(commandHandle, shapeIndex, collisionFramePosition, collisionFrameOrientation);
b3CreateCollisionShapeSetChildTransform(commandHandle, shapeIndex, collisionFramePosition, collisionFrameOrientation);
}
statusHandle = b3SubmitClientCommandAndWaitStatus(sm, commandHandle);
statusType = b3GetStatusType(statusHandle);

9
setup.py
View File

@@ -463,15 +463,16 @@ egl_renderer_sources = \
+["src/BulletCollision/CollisionShapes/btConvexInternalShape.cpp"]\
+["src/Bullet3Common/b3Logging.cpp"]\
+["src/LinearMath/btAlignedAllocator.cpp"]\
+["src/LinearMath/btGeometryUtil.cpp"]\
+["src/LinearMath/btConvexHull.cpp"]\
+["src/LinearMath/btConvexHullComputer.cpp"]\
+["src/LinearMath/btConvexHullComputer.cpp"] \
+["src/LinearMath/btGeometryUtil.cpp"]\
+["src/LinearMath/btQuickprof.cpp"] \
+["src/LinearMath/btThreads.cpp"] \
+["src/Bullet3Common/b3AlignedAllocator.cpp"] \
+["examples/ThirdPartyLibs/glad/gl.c"]\
+["examples/OpenGLWindow/GLInstancingRenderer.cpp"]\
+["examples/OpenGLWindow/GLRenderToTexture.cpp"] \
+["examples/OpenGLWindow/LoadShader.cpp"] \
+["src/LinearMath/btQuickprof.cpp"]
+["examples/OpenGLWindow/LoadShader.cpp"]
if 'BT_USE_EGL' in CXX_FLAGS:
sources += ['examples/ThirdPartyLibs/glad/egl.c']

17
src/Bullet3Common/b3AlignedAllocator.cpp
View File

@@ -15,9 +15,11 @@ subject to the following restrictions:
#include "b3AlignedAllocator.h"
#ifdef B3_ALLOCATOR_STATISTICS
int b3g_numAlignedAllocs = 0;
int b3g_numAlignedFree = 0;
int b3g_totalBytesAlignedAllocs = 0; //detect memory leaks
#endif
static void *b3AllocDefault(size_t size)
{
@@ -109,10 +111,10 @@ void *b3AlignedAllocInternal(size_t size, int alignment, int line, char *filenam
{
void *ret;
char *real;
#ifdef B3_ALLOCATOR_STATISTICS
b3g_totalBytesAlignedAllocs += size;
b3g_numAlignedAllocs++;
#endif
real = (char *)b3s_allocFunc(size + 2 * sizeof(void *) + (alignment - 1));
if (real)
{
@@ -135,14 +137,16 @@ void *b3AlignedAllocInternal(size_t size, int alignment, int line, char *filenam
void b3AlignedFreeInternal(void *ptr, int line, char *filename)
{
void *real;
#ifdef B3_ALLOCATOR_STATISTICS
b3g_numAlignedFree++;
#endif
if (ptr)
{
real = *((void **)(ptr)-1);
int size = *((int *)(ptr)-2);
#ifdef B3_ALLOCATOR_STATISTICS
b3g_totalBytesAlignedAllocs -= size;
#endif
b3Printf("free #%d at address %x, from %s,line %d, size %d\n", b3g_numAlignedFree, real, filename, line, size);
b3s_freeFunc(real);
@@ -157,7 +161,9 @@ void b3AlignedFreeInternal(void *ptr, int line, char *filename)
void *b3AlignedAllocInternal(size_t size, int alignment)
{
#ifdef B3_ALLOCATOR_STATISTICS
b3g_numAlignedAllocs++;
#endif
void *ptr;
ptr = b3s_alignedAllocFunc(size, alignment);
// b3Printf("b3AlignedAllocInternal %d, %x\n",size,ptr);
@@ -170,8 +176,9 @@ void b3AlignedFreeInternal(void *ptr)
{
return;
}
#ifdef B3_ALLOCATOR_STATISTICS
b3g_numAlignedFree++;
#endif
// b3Printf("b3AlignedFreeInternal %x\n",ptr);
b3s_alignedFreeFunc(ptr);
}

2
src/BulletCollision/CollisionDispatch/btCompoundCompoundCollisionAlgorithm.h
View File

@@ -34,6 +34,8 @@ class btCollisionObject;
class btCollisionShape;
extern btShapePairCallback gCompoundCompoundChildShapePairCallback;
/// btCompoundCompoundCollisionAlgorithm supports collision between two btCompoundCollisionShape shapes
class btCompoundCompoundCollisionAlgorithm : public btCompoundCollisionAlgorithm
{

6
src/BulletCollision/Gimpact/btGImpactBvh.h
View File

@@ -1,5 +1,5 @@
#ifndef GIM_BOX_SET_H_INCLUDED
#define GIM_BOX_SET_H_INCLUDED
#ifndef BT_GIMPACT_BVH_H_INCLUDED
#define BT_GIMPACT_BVH_H_INCLUDED
/*! \file gim_box_set.h
\author Francisco Leon Najera
@@ -306,4 +306,4 @@ public:
btPairSet& collision_pairs);
};
#endif // GIM_BOXPRUNING_H_INCLUDED
#endif // BT_GIMPACT_BVH_H_INCLUDED

5
src/BulletCollision/NarrowPhaseCollision/btGjkPairDetector.cpp
View File

@@ -36,9 +36,6 @@ btScalar gGjkEpaPenetrationTolerance = 1.0e-12;
btScalar gGjkEpaPenetrationTolerance = 0.001;
#endif
//temp globals, to improve GJK/EPA/penetration calculations
int gNumDeepPenetrationChecks = 0;
int gNumGjkChecks = 0;
btGjkPairDetector::btGjkPairDetector(const btConvexShape *objectA, const btConvexShape *objectB, btSimplexSolverInterface *simplexSolver, btConvexPenetrationDepthSolver *penetrationDepthSolver)
: m_cachedSeparatingAxis(btScalar(0.), btScalar(1.), btScalar(0.)),
@@ -708,7 +705,6 @@ void btGjkPairDetector::getClosestPointsNonVirtual(const ClosestPointInput &inpu
btScalar marginA = m_marginA;
btScalar marginB = m_marginB;
gNumGjkChecks++;
//for CCD we don't use margins
if (m_ignoreMargin)
@@ -1021,7 +1017,6 @@ void btGjkPairDetector::getClosestPointsNonVirtual(const ClosestPointInput &inpu
// Penetration depth case.
btVector3 tmpPointOnA, tmpPointOnB;
gNumDeepPenetrationChecks++;
m_cachedSeparatingAxis.setZero();
bool isValid2 = m_penetrationDepthSolver->calcPenDepth(

4
src/BulletDynamics/ConstraintSolver/btContactSolverInfo.h
View File

@@ -62,6 +62,8 @@ struct btContactSolverInfoData
btScalar m_singleAxisRollingFrictionThreshold;
btScalar m_leastSquaresResidualThreshold;
btScalar m_restitutionVelocityThreshold;
bool m_jointFeedbackInWorldSpace;
bool m_jointFeedbackInJointFrame;
};
struct btContactSolverInfo : public btContactSolverInfoData
@@ -94,6 +96,8 @@ struct btContactSolverInfo : public btContactSolverInfoData
m_singleAxisRollingFrictionThreshold = 1e30f; ///if the velocity is above this threshold, it will use a single constraint row (axis), otherwise 3 rows.
m_leastSquaresResidualThreshold = 0.f;
m_restitutionVelocityThreshold = 0.2f; //if the relative velocity is below this threshold, there is zero restitution
m_jointFeedbackInWorldSpace = false;
m_jointFeedbackInJointFrame = false;
}
};

17
src/BulletDynamics/Featherstone/btMultiBody.cpp
View File

@@ -30,9 +30,6 @@
//#include "Bullet3Common/b3Logging.h"
// #define INCLUDE_GYRO_TERM
///todo: determine if we need these options. If so, make a proper API, otherwise delete those globals
bool gJointFeedbackInWorldSpace = false;
bool gJointFeedbackInJointFrame = false;
namespace
{
@@ -718,10 +715,12 @@ inline btMatrix3x3 outerProduct(const btVector3 &v0, const btVector3 &v1) //ren
//
void btMultiBody::computeAccelerationsArticulatedBodyAlgorithmMultiDof(btScalar dt,
btAlignedObjectArray<btScalar> &scratch_r,
btAlignedObjectArray<btVector3> &scratch_v,
btAlignedObjectArray<btMatrix3x3> &scratch_m,
bool isConstraintPass)
btAlignedObjectArray<btScalar> &scratch_r,
btAlignedObjectArray<btVector3> &scratch_v,
btAlignedObjectArray<btMatrix3x3> &scratch_m,
bool isConstraintPass,
bool jointFeedbackInWorldSpace,
bool jointFeedbackInJointFrame)
{
// Implement Featherstone's algorithm to calculate joint accelerations (q_double_dot)
// and the base linear & angular accelerations.
@@ -1124,7 +1123,7 @@ void btMultiBody::computeAccelerationsArticulatedBodyAlgorithmMultiDof(btScalar
btVector3 angularBotVec = (spatInertia[i + 1] * spatAcc[i + 1] + zeroAccSpatFrc[i + 1]).m_bottomVec;
btVector3 linearTopVec = (spatInertia[i + 1] * spatAcc[i + 1] + zeroAccSpatFrc[i + 1]).m_topVec;
if (gJointFeedbackInJointFrame)
if (jointFeedbackInJointFrame)
{
//shift the reaction forces to the joint frame
//linear (force) component is the same
@@ -1132,7 +1131,7 @@ void btMultiBody::computeAccelerationsArticulatedBodyAlgorithmMultiDof(btScalar
angularBotVec = angularBotVec - linearTopVec.cross(m_links[i].m_dVector);
}
if (gJointFeedbackInWorldSpace)
if (jointFeedbackInWorldSpace)
{
if (isConstraintPass)
{

21
src/BulletDynamics/Featherstone/btMultiBody.h
View File

@@ -338,17 +338,20 @@ public:
btAlignedObjectArray<btScalar> & scratch_r,
btAlignedObjectArray<btVector3> & scratch_v,
btAlignedObjectArray<btMatrix3x3> & scratch_m,
bool isConstraintPass = false);
bool isConstraintPass,
bool jointFeedbackInWorldSpace,
bool jointFeedbackInJointFrame
);
///stepVelocitiesMultiDof is deprecated, use computeAccelerationsArticulatedBodyAlgorithmMultiDof instead
void stepVelocitiesMultiDof(btScalar dt,
btAlignedObjectArray<btScalar> & scratch_r,
btAlignedObjectArray<btVector3> & scratch_v,
btAlignedObjectArray<btMatrix3x3> & scratch_m,
bool isConstraintPass = false)
{
computeAccelerationsArticulatedBodyAlgorithmMultiDof(dt, scratch_r, scratch_v, scratch_m, isConstraintPass);
}
//void stepVelocitiesMultiDof(btScalar dt,
// btAlignedObjectArray<btScalar> & scratch_r,
// btAlignedObjectArray<btVector3> & scratch_v,
// btAlignedObjectArray<btMatrix3x3> & scratch_m,
// bool isConstraintPass = false)
//{
// computeAccelerationsArticulatedBodyAlgorithmMultiDof(dt, scratch_r, scratch_v, scratch_m, isConstraintPass, false, false);
//}
// calcAccelerationDeltasMultiDof
// input: force vector (in same format as jacobian, i.e.:

31
src/BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.cpp
View File

@@ -491,11 +491,14 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
m_scratch_v.resize(bod->getNumLinks() + 1);
m_scratch_m.resize(bod->getNumLinks() + 1);
bool doNotUpdatePos = false;
bool isConstraintPass = false;
{
if (!bod->isUsingRK4Integration())
{
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(solverInfo.m_timeStep, m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(solverInfo.m_timeStep,
m_scratch_r, m_scratch_v, m_scratch_m,isConstraintPass,
getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
}
else
{
@@ -593,7 +596,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
btScalar h = solverInfo.m_timeStep;
#define output &m_scratch_r[bod->getNumDofs()]
//calc qdd0 from: q0 & qd0
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m,
isConstraintPass,getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
pCopy(output, scratch_qdd0, 0, numDofs);
//calc q1 = q0 + h/2 * qd0
pResetQx();
@@ -603,7 +608,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
//
//calc qdd1 from: q1 & qd1
pCopyToVelocityVector(bod, scratch_qd1);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m,
isConstraintPass,getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
pCopy(output, scratch_qdd1, 0, numDofs);
//calc q2 = q0 + h/2 * qd1
pResetQx();
@@ -613,7 +620,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
//
//calc qdd2 from: q2 & qd2
pCopyToVelocityVector(bod, scratch_qd2);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m,
isConstraintPass,getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
pCopy(output, scratch_qdd2, 0, numDofs);
//calc q3 = q0 + h * qd2
pResetQx();
@@ -623,7 +632,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
//
//calc qdd3 from: q3 & qd3
pCopyToVelocityVector(bod, scratch_qd3);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0., m_scratch_r, m_scratch_v, m_scratch_m,
isConstraintPass,getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
pCopy(output, scratch_qdd3, 0, numDofs);
//
@@ -660,7 +671,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
{
for (int link = 0; link < bod->getNumLinks(); ++link)
bod->getLink(link).updateCacheMultiDof();
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0, m_scratch_r, m_scratch_v, m_scratch_m);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(0, m_scratch_r, m_scratch_v, m_scratch_m,
isConstraintPass,getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
}
}
}
@@ -708,7 +721,9 @@ void btMultiBodyDynamicsWorld::solveConstraints(btContactSolverInfo& solverInfo)
if (!bod->isUsingRK4Integration())
{
bool isConstraintPass = true;
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(solverInfo.m_timeStep, m_scratch_r, m_scratch_v, m_scratch_m, isConstraintPass);
bod->computeAccelerationsArticulatedBodyAlgorithmMultiDof(solverInfo.m_timeStep, m_scratch_r, m_scratch_v, m_scratch_m, isConstraintPass,
getSolverInfo().m_jointFeedbackInWorldSpace,
getSolverInfo().m_jointFeedbackInJointFrame);
}
}
}

33
src/LinearMath/TaskScheduler/btThreadSupportPosix.cpp
View File

@@ -1,3 +1,4 @@
/*
Bullet Continuous Collision Detection and Physics Library
Copyright (c) 2003-2018 Erwin Coumans http://bulletphysics.com
@@ -72,7 +73,7 @@ public:
pthread_t thread;
//each tread will wait until this signal to start its work
sem_t* startSemaphore;
btCriticalSection* m_cs;
// this is a copy of m_mainSemaphore,
//each tread will signal once it is finished with its work
sem_t* m_mainSemaphore;
@@ -90,7 +91,7 @@ private:
void startThreads(const ConstructionInfo& threadInfo);
void stopThreads();
int waitForResponse();
btCriticalSection* m_cs;
public:
btThreadSupportPosix(const ConstructionInfo& threadConstructionInfo);
virtual ~btThreadSupportPosix();
@@ -119,6 +120,7 @@ public:
btThreadSupportPosix::btThreadSupportPosix(const ConstructionInfo& threadConstructionInfo)
{
m_cs = createCriticalSection();
startThreads(threadConstructionInfo);
}
@@ -126,6 +128,8 @@ btThreadSupportPosix::btThreadSupportPosix(const ConstructionInfo& threadConstru
btThreadSupportPosix::~btThreadSupportPosix()
{
stopThreads();
deleteCriticalSection(m_cs);
m_cs=0;
}
#if (defined(__APPLE__))
@@ -181,21 +185,23 @@ static void* threadFunction(void* argument)
{
btAssert(status->m_status);
status->m_userThreadFunc(userPtr);
status->m_cs->lock();
status->m_status = 2;
status->m_cs->unlock();
checkPThreadFunction(sem_post(status->m_mainSemaphore));
status->threadUsed++;
}
else
{
//exit Thread
status->m_cs->lock();
status->m_status = 3;
status->m_cs->unlock();
checkPThreadFunction(sem_post(status->m_mainSemaphore));
printf("Thread with taskId %i exiting\n", status->m_taskId);
break;
}
}
printf("Thread TERMINATED\n");
return 0;
}
@@ -206,7 +212,7 @@ void btThreadSupportPosix::runTask(int threadIndex, void* userData)
btThreadStatus& threadStatus = m_activeThreadStatus[threadIndex];
btAssert(threadIndex >= 0);
btAssert(threadIndex < m_activeThreadStatus.size());
threadStatus.m_cs = m_cs;
threadStatus.m_commandId = 1;
threadStatus.m_status = 1;
threadStatus.m_userPtr = userData;
@@ -231,7 +237,10 @@ int btThreadSupportPosix::waitForResponse()
for (size_t t = 0; t < size_t(m_activeThreadStatus.size()); ++t)
{
if (2 == m_activeThreadStatus[t].m_status)
m_cs->lock();
bool hasFinished = (2 == m_activeThreadStatus[t].m_status);
m_cs->unlock();
if (hasFinished)
{
last = t;
break;
@@ -261,7 +270,6 @@ void btThreadSupportPosix::waitForAllTasks()
void btThreadSupportPosix::startThreads(const ConstructionInfo& threadConstructionInfo)
{
m_numThreads = btGetNumHardwareThreads() - 1; // main thread exists already
printf("%s creating %i threads.\n", __FUNCTION__, m_numThreads);
m_activeThreadStatus.resize(m_numThreads);
m_startedThreadsMask = 0;
@@ -270,20 +278,18 @@ void btThreadSupportPosix::startThreads(const ConstructionInfo& threadConstructi
for (int i = 0; i < m_numThreads; i++)
{
printf("starting thread %d\n", i);
btThreadStatus& threadStatus = m_activeThreadStatus[i];
threadStatus.startSemaphore = createSem("threadLocal");
checkPThreadFunction(pthread_create(&threadStatus.thread, NULL, &threadFunction, (void*)&threadStatus));
threadStatus.m_userPtr = 0;
threadStatus.m_cs = m_cs;
threadStatus.m_taskId = i;
threadStatus.m_commandId = 0;
threadStatus.m_status = 0;
threadStatus.m_mainSemaphore = m_mainSemaphore;
threadStatus.m_userThreadFunc = threadConstructionInfo.m_userThreadFunc;
threadStatus.threadUsed = 0;
checkPThreadFunction(pthread_create(&threadStatus.thread, NULL, &threadFunction, (void*)&threadStatus));
printf("started thread %d \n", i);
}
}
@@ -293,20 +299,15 @@ void btThreadSupportPosix::stopThreads()
for (size_t t = 0; t < size_t(m_activeThreadStatus.size()); ++t)
{
btThreadStatus& threadStatus = m_activeThreadStatus[t];
printf("%s: Thread %i used: %ld\n", __FUNCTION__, int(t), threadStatus.threadUsed);
threadStatus.m_userPtr = 0;
checkPThreadFunction(sem_post(threadStatus.startSemaphore));
checkPThreadFunction(sem_wait(m_mainSemaphore));
printf("destroy semaphore\n");
destroySem(threadStatus.startSemaphore);
printf("semaphore destroyed\n");
checkPThreadFunction(pthread_join(threadStatus.thread, 0));
}
printf("destroy main semaphore\n");
destroySem(m_mainSemaphore);
printf("main semaphore destroyed\n");
m_activeThreadStatus.clear();
}

34
src/LinearMath/btQuickprof.cpp
View File

@@ -694,6 +694,24 @@ void CProfileManager::dumpAll()
CProfileManager::Release_Iterator(profileIterator);
}
void btEnterProfileZoneDefault(const char* name)
{
}
void btLeaveProfileZoneDefault()
{
}
#else
void btEnterProfileZoneDefault(const char* name)
{
}
void btLeaveProfileZoneDefault()
{
}
#endif //BT_NO_PROFILE
// clang-format off
#if defined(_WIN32) && (defined(__MINGW32__) || defined(__MINGW64__))
#define BT_HAVE_TLS 1
@@ -743,22 +761,6 @@ unsigned int btQuickprofGetCurrentThreadIndex2()
#endif //BT_THREADSAFE
}
void btEnterProfileZoneDefault(const char* name)
{
}
void btLeaveProfileZoneDefault()
{
}
#else
void btEnterProfileZoneDefault(const char* name)
{
}
void btLeaveProfileZoneDefault()
{
}
#endif //BT_NO_PROFILE
static btEnterProfileZoneFunc* bts_enterFunc = btEnterProfileZoneDefault;
static btLeaveProfileZoneFunc* bts_leaveFunc = btLeaveProfileZoneDefault;

9
src/LinearMath/btQuickprof.h
View File

@@ -61,18 +61,19 @@ btLeaveProfileZoneFunc* btGetCurrentLeaveProfileZoneFunc();
void btSetCustomEnterProfileZoneFunc(btEnterProfileZoneFunc* enterFunc);
void btSetCustomLeaveProfileZoneFunc(btLeaveProfileZoneFunc* leaveFunc);
#ifndef BT_NO_PROFILE // FIX redefinition
//To disable built-in profiling, please comment out next line
//#define BT_NO_PROFILE 1
#ifndef BT_ENABLE_PROFILE
#define BT_NO_PROFILE 1
#endif //BT_NO_PROFILE
const unsigned int BT_QUICKPROF_MAX_THREAD_COUNT = 64;
#ifndef BT_NO_PROFILE
//btQuickprofGetCurrentThreadIndex will return -1 if thread index cannot be determined,
//otherwise returns thread index in range [0..maxThreads]
unsigned int btQuickprofGetCurrentThreadIndex2();
#ifndef BT_NO_PROFILE
#include <stdio.h> //@todo remove this, backwards compatibility
#include "btAlignedAllocator.h"