def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"inputList", #nargs="?" ,
help=("Training sample list."),
#default=config.defaultTrainSampleList
)
parser.add_argument("outputDir",
nargs="?",
help="Music output directory",
default=config.defaultOutputDir)
#parser.add_argument("modelName", nargs="?" ,
# help="model filename",
# default=config.defaultModelFilename
# )
args = parser.parse_args()
trainSampList = sampleLoader.parseFileList(args.inputList)
trainFeatsList = []
for trainSampFilename in trainSampList:
trainSamp = sampleLoader.loadTrainSample(trainSampFilename)
trainFeat = featureManager.extractTrainFeat(trainSamp)
trainFeatsList.append(trainFeat)
trainFeatFilename = config.getTrainInFeatFilename(args)
featureManager.saveJson(trainFeatsList, trainFeatFilename)
m = model.getModelObj()
#modelName = config.getModelFilename(args)
m.train(args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("inputList", #nargs="?" ,
help=("Training sample list."),
#default=config.defaultTrainSampleList
)
parser.add_argument("outputDir", nargs="?" ,
help="Music output directory",
default = config.defaultOutputDir
)
#parser.add_argument("modelName", nargs="?" ,
# help="model filename",
# default=config.defaultModelFilename
# )
args = parser.parse_args()
trainSampList = sampleLoader.parseFileList(args.inputList)
trainFeatsList = []
for trainSampFilename in trainSampList:
trainSamp = sampleLoader.loadTrainSample(trainSampFilename)
trainFeat = featureManager.extractTrainFeat(trainSamp)
trainFeatsList.append(trainFeat)
trainFeatFilename = config.getTrainInFeatFilename(args)
featureManager.saveJson(trainFeatsList, trainFeatFilename);
m = model.getModelObj();
#modelName = config.getModelFilename(args)
m.train(args); #load features from config.getTrainInFeatFilename(args)
def train(self, args):
#ex filenames:
# trainFeat.json -> [quantizer] -> <perfFeatName>.train.dat
# | |
# +-> <perfFeatName>.quant |
# +-------------------------------------------------------+
# +-> [svm^hmm] -> <perfFeatName>.model.bin
trainFeats = featureManager.loadJson(
config.getTrainInFeatFilename(args))
perfFeats = collectPerfFeats(trainFeats)
#logging.printDebug(perfFeats)
#for each perfFeature, a svmFeatFilename (quantized feat for svm^hmm)
#and singleModelFilename (model file for svm^hmm) will be saved
#scoreFeatLines = self.formatLine(scoreFeats)
scoreFeatLines = self.formatLineDirect(trainFeats)
for pkey, pval in perfFeats.items():
allLines = [("# " + pkey)]
#TODO:quantize pval
q = quantizer.getQuantizerObj(self.getQuantizeFilename(args, pkey))
quantizedVal = q.quantize(pval)
lines = zip(map(str, quantizedVal), scoreFeatLines)
#logging.printDebug(lines)
allLines = map(lambda l: " ".join(l), lines)
#logging.printDebug(allLines)
svmFeatFilename = self.getTrainInputFilename(args, pkey)
with open(svmFeatFilename, 'w') as f:
f.writelines(map(lambda x: x + "\n", allLines))
cmd = [self.trainBinPath]
if (config.svmhmm_c != None):
cmd.append("-c " + str(config.svmhmm_c))
else:
cmd.append("-c 0.01")
cmd.append(svmFeatFilename)
singleModelFilename = self.getSingleModelFilename(args, pkey)
cmd.append(singleModelFilename)
#logging.printDebug(" ".join(cmd))
subprocess.call(" ".join(cmd), shell=True)
def train(self, args):
#ex filenames:
# trainFeat.json -> [quantizer] -> <perfFeatName>.train.dat
# | |
# +-> <perfFeatName>.quant |
# +-------------------------------------------------------+
# +-> [svm^hmm] -> <perfFeatName>.model.bin
trainFeats = featureManager.loadJson(config.getTrainInFeatFilename(args))
perfFeats = collectPerfFeats(trainFeats)
#logging.printDebug(perfFeats)
#for each perfFeature, a svmFeatFilename (quantized feat for svm^hmm)
#and singleModelFilename (model file for svm^hmm) will be saved
#scoreFeatLines = self.formatLine(scoreFeats)
scoreFeatLines = self.formatLineDirect(trainFeats)
for pkey, pval in perfFeats.items():
allLines = [("# " + pkey)]
#TODO:quantize pval
q = quantizer.getQuantizerObj(self.getQuantizeFilename(args, pkey))
quantizedVal = q.quantize(pval)
lines = zip(map(str, quantizedVal), scoreFeatLines)
#logging.printDebug(lines)
allLines = map(lambda l:" ".join(l), lines)
#logging.printDebug(allLines)
svmFeatFilename = self.getTrainInputFilename(args, pkey)
with open(svmFeatFilename, 'w') as f:
f.writelines(map(lambda x:x+"\n", allLines))
cmd = [self.trainBinPath]
if (config.svmhmm_c != None):
cmd.append("-c " + str(config.svmhmm_c))
else:
cmd.append("-c 0.01")
cmd.append(svmFeatFilename)
singleModelFilename = self.getSingleModelFilename(args,pkey)
cmd.append(singleModelFilename)
#logging.printDebug(" ".join(cmd))
subprocess.call(" ".join(cmd), shell=True)