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How to use parallel_simple method in autotest

Best Python code snippet using autotest_python

simulatorTestDemo.py

Source:simulatorTestDemo.py

1import os2import numpy as np3import torch4import argparse5from simulateTest.AutoGraspShapeCoreUtil import AutoGraspUtil6# parser = argparse.ArgumentParser(description='ShapeNetSem Grasp testing')7# parser.add_argument('-t', '--testFile', default='prediction/500ntop10.txt', type=str, metavar='FILE', help='testFile path')8# # parser.add_argument('-n', '--samplePerObject', default=10, type=int, metavar='N', help='sample num per object')9# parser.add_argument('-p', '--processNum', default=18, type=int, metavar='N', help='process num using')10# parser.add_argument('-w', '--haveWidth', default=1, type=int, metavar='N', help='0 : no width ; 1 : have width')11# parser.add_argument('--gripperFile', default='/data/shapeNet/annotator2/parallel_simple.urdf', type=str, metavar='FILE', help='gripper file')12# parser.add_argument('--objMeshRoot', default='/data/shapeNet/urdf', type=str, metavar='PATH', help='obj mesh path')13parser = argparse.ArgumentParser(description='ShapeNetSem Grasp testing')14parser.add_argument('-t', '--testFile', default='gpnet_data/prediction/nms_poses_view0.txt', type=str, metavar='FILE', help='testFile path')15# parser.add_argument('-n', '--samplePerObject', default=10, type=int, metavar='N', help='sample num per object')16parser.add_argument('-p', '--processNum', default=10, type=int, metavar='N', help='process num using')17# parser.add_argument('-w', '--haveWidth', default=0, type=int, metavar='N', help='0 : no width ; 1 : have width')18parser.add_argument('-w', "--width", action="store_true", dest="width", default=False, help="turn on this param if test file contains width.")19parser.add_argument('--gripperFile', default='gpnet_data/gripper/parallel_simple.urdf', type=str, metavar='FILE', help='gripper file')20parser.add_argument('--objMeshRoot', default='gpnet_data/urdf', type=str, metavar='PATH', help='obj mesh path')21def getObjStatusAndAnnotation(testFile, haveWidth=False):22    with open(testFile, 'r') as testData:23        lines = testData.readlines()24        objIdList = []25        quaternionDict = {}26        centerDict = {}27        # 0: scaling    1~3: position   4~7: orientation    8: staticFrictionCoeff28        objId = 'invalid'29        objCounter = -130        annotationCounter = -131        for line in lines:32            # new object33            msg = line.strip()34            if len(msg.split(',')) < 2 :35                objId = msg.strip()36                # skip invalid37                # begin38                objCounter += 139                objIdList.append(objId)40                quaternionDict[objId] = np.empty(shape=(0, 4), dtype=np.float)41                centerDict[objId] = np.empty(shape=(0, 3), dtype=np.float)42                annotationCounter = -143            # read annotation44            else:45                # skip invalid object46                if objId == 'invalid':47                    continue48                # begin49                annotationCounter += 150                pose = msg.split(',')51                # print(objId, annotationCounter)52                if haveWidth:53                    length = float(pose[0]) * 0.085     # arbitrary value, will not be used in AutoGrasp54                    length = length if length < 0.085 else 0.08555                    position = np.array([float(pose[1]), float(pose[2]), float(pose[3])])56                    quaternion = np.array([float(pose[4]), float(pose[5]), float(pose[6]), float(pose[7])])57                    # quaternion = quaternion[[1, 2, 3, 0]]58                else:59                    length = 0.000  # arbitrary value, will not be used in AutoGrasp60                    position = np.array([float(pose[0]), float(pose[1]), float(pose[2])])61                    quaternion = np.array([float(pose[3]), float(pose[4]), float(pose[5]), float(pose[6])])62                    # quaternion = quaternion[[1, 2, 3, 0]]63                # print(objCounter, annotationCounter)64                quaternionDict[objId] = np.concatenate((quaternionDict[objId], quaternion[None, :]), axis=0)65                centerDict[objId] = np.concatenate((centerDict[objId], position[None, :]), axis=0)66    return quaternionDict, centerDict, objIdList67if __name__ == "__main__":68    cfg = parser.parse_args()69    objMeshRoot = cfg.objMeshRoot70    processNum = cfg.processNum71    gripperFile = cfg.gripperFile72    haveWidth = cfg.width73    testInfoFile = cfg.testFile74    logFile = cfg.testFile[:-4] + '_log.csv'75    open(logFile, 'w').close()76    simulator = AutoGraspUtil()77    quaternionDict, centerDict, objIdList = getObjStatusAndAnnotation(testInfoFile, haveWidth)78    for objId in objIdList:79        q = quaternionDict[objId]80        c = centerDict[objId]81        simulator.addObject2(82            objId=objId,83            quaternion=q,84            translation=c85        )86    simulator.parallelSimulation(87        logFile=logFile,88        objMeshRoot=objMeshRoot,89        processNum=processNum,90        gripperFile=gripperFile,91    )92    annotationSuccessDict = simulator.getSuccessData(logFile=logFile)93    # print(top 10% 30% 50% 100%)94    top10, top30, top50, top100 = simulator.getStatistic(annotationSuccessDict)...

ga.py

Source:ga.py

...31toolbox.register("mutate", mutGaussianInt, mu=mu, sigma=sigma, indpb=0.25)32toolbox.register("select", tools.selBest)33if __name__ == "__main__":34	start_time = time.time()35	pop, log, hof, history = parallel_simple(toolbox, pop_sz, cxpb, mutpb, ngen, n_jobs=4)36	print("total runtime:", time.time() - start_time)37	print("Best individual is: %s\nwith fitness: %s" % (hof[0], hof[0].fitness.values[0]))38	print("Final population:\n", pop)39	print("Log:\n", log)40	output = []41	for i in hof:42		output.append(i + [i.fitness.values[0]])43		print(i + [i.fitness.values[0]])44	output = np.array(output)45	np.savetxt('ga_results_backup2.csv', output, fmt='%10.5f', delimiter=',')46	gene_order = []47	for i in history.genealogy_history:48		gene_order.append(history.genealogy_history[i])49	gene_order = np.array(gene_order)...

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