def runConverterDriver(self):
for iter in product(self.args.algo, self.args.version, self.args.dataset, self.args.datasetType, self.args.target):
algo, version, dataset, datasetType, target = iter
print("\nGenerating input files for \"" + algo + " " + version +
" " + dataset + " " + datasetType + " " + target + "\"...")
outputDir = os.path.join(
"Converter", "output", algo + "-" + version + "-" + datasetType, dataset)
os.makedirs(outputDir, exist_ok=True)
datasetDir = os.path.join("..", "datasets", "datasets", dataset)
modelDir = os.path.join("..", "model", dataset)
if algo == config.Algo.bonsai:
modelDir = os.path.join(modelDir, "BonsaiResults", "Params")
elif algo == config.Algo.lenet:
modelDir = os.path.join(modelDir, "LenetModel")
else:
modelDir = os.path.join(modelDir, "ProtoNNResults")
inputFile = os.path.join(modelDir, "input.sd")
trainingInput = os.path.join(datasetDir, "training-full.tsv")
testingInput = os.path.join(datasetDir, "testing.tsv")
obj = converter.Converter(algo, version, datasetType, target,
outputDir, outputDir)
obj.setInput(inputFile, modelDir, trainingInput, testingInput)
obj.run()
def runPredictorDriver(self):
for iter in product(self.args.algo, self.args.version, self.args.dataset, self.args.datasetType):
algo, version, dataset, datasetType = iter
print("\nGenerating input files for \"" + algo + " " +
version + " " + dataset + " " + datasetType + "\"...")
#outputDir = os.path.join("..", "Predictor", algo, version + "-testing")
#datasetOutputDir = os.path.join("..", "Predictor", algo, version + "-" + datasetType)
if version == config.Version.fixed:
outputDir = os.path.join(
"..", "Predictor", "seedot_fixed", "testing")
datasetOutputDir = os.path.join(
"..", "Predictor", "seedot_fixed", datasetType)
elif version == config.Version.floatt:
outputDir = os.path.join(
"..", "Predictor", algo + "_float", "testing")
datasetOutputDir = os.path.join(
"..", "Predictor", algo + "_float", datasetType)
os.makedirs(datasetOutputDir, exist_ok=True)
os.makedirs(outputDir, exist_ok=True)
datasetDir = os.path.join("..", "datasets", "datasets", dataset)
modelDir = os.path.join("..", "model", dataset)
if algo == config.Algo.bonsai:
modelDir = os.path.join(modelDir, "BonsaiResults", "Params")
elif algo == config.Algo.lenet:
modelDir = os.path.join(modelDir, "LenetModel")
else:
modelDir = os.path.join(modelDir, "ProtoNNResults")
inputFile = os.path.join(modelDir, "input.sd")
trainingInput = os.path.join(datasetDir, "training-full.tsv")
testingInput = os.path.join(datasetDir, "testing.tsv")
obj = converter.Converter(algo, version, datasetType, config.Target.x86,
datasetOutputDir, outputDir)
obj.setInput(inputFile, modelDir, trainingInput, testingInput)
obj.run()
print("Building and executing " + algo + " " +
version + " " + dataset + " " + datasetType + "...")
outputDir = os.path.join(
"..", "Predictor", "output", algo + "-" + version)
curDir = os.getcwd()
os.chdir(os.path.join("..", "Predictor"))
obj = predictor.Predictor(
algo, version, datasetType, outputDir, self.args.max_scale_factor)
acc = obj.run()
os.chdir(curDir)
if acc != None:
print("Accuracy is %.3f" % (acc))