def getProfile(files):
timeList = []
overheadList = []
for f in files:
prof1 = profiler.Profiler(f[0])
prof2 = profiler.Profiler(f[1])
execTime = prof1.getTime('exec')
overhead = 2 * prof2.getTime('overhead')
timeList.append(execTime)
overheadList.append(overhead)
print(timeList)
print(overheadList)
return [np.mean(timeList), np.mean(overheadList)]
def getOverHead(numStages, iters, logDir):
stagesMeanList = []
for m in range(numStages):
timeList = []
for i in range(iters):
f = pjoin(logDir, 'dp_overhead_{}_{}.json'.format(m, i + 1))
prof = profiler.Profiler(f)
execTime = 2 * prof.getTime('overhead')
print(execTime / 2.)
timeList.append(execTime)
stagesMeanList.append(np.mean(timeList, dtype=int))
print(stagesMeanList)
return stagesMeanList
def getProfile(name, numStages, iters, logDir):
stagesMeanList = []
for m in range(numStages):
timeList = []
for i in range(iters):
f = pjoin(logDir, '{}_{}_{}.json'.format(name, m, i + 1))
prof = profiler.Profiler(f)
execTime = prof.getTime('exec')
memcpy = prof.getTime('memcpy')
timeList.append([execTime, memcpy[0], memcpy[1]])
stagesMeanList.append(list(np.mean(timeList, axis=0, dtype=int)))
assert (len(stagesMeanList) == numStages)
assert (len(stagesMeanList[0]) == 3)
return stagesMeanList
return text
if __name__ == "__main__":
g = ss.speech_syn("Gizmo", "Hi")
data = ""
while 1:
listen()
data = reply()
# data = "yes"
if data == "yes":
questions_list = pr.Profiler().fetch_questions()
while 1:
print(questions_list)
labelled_str = pr.Profiler().pro_labelling(questions_list[0])
g.say(labelled_str)
listen()
text = AU.Audio_recognizer(AUDIO_FILE).text()
print(text)
if data == "bye":
break
# help(AU)
#
print(" ActivationShape {}".format(sessRet.shape))
print(" ActiationSize {}".format(activationSize))
print(" WeightSize {}".format(weightSize))
else:
sessRet = sess.run(m.output,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
show_memory=False)
with open(fName, 'w') as f:
f.write(chrome_trace)
f.close()
prof = profiler.Profiler(fName, m.layerNames[idx])
execTime = prof.getTime('exec')
memcpy = prof.getTime('memcpy')
timeList.append([execTime, memcpy[0], memcpy[1]])
timeList = np.mean(timeList, axis=0)
totalTimeList.append(timeList)
if PRINT:
execTime = timeList[0]
memIn = timeList[1]
memOut = timeList[2]
print(" Exec {} us".format(execTime))
print(" MemcpyIn {} us".format(memIn))
print(" MemcpyOut {} us".format(memOut))
assert (len(totalTimeList) == len(m.layers))
for idx, time in enumerate(zip(totalTimeList[:-1], totalTimeList[1:])):
# print("{} + {}".format(m.nameList[idx], m.nameList[idx+1]))
if __name__ == "__main__":
g = ss.speech_syn("Gizmo", "Hi")
review_list = []
while 1:
listen()
data = reply()
print("FROM __MAIN__ => ", data)
# data = "yes"
if data == "yes":
tag_value = pr.Profiler().pro_fetch_questions()
index = 0
while 1:
labelled_str = pr.Profiler().pro_labelling(tag_value[index])
g.say(labelled_str)
listen()
text = AU.Audio_recognizer(AUDIO_FILE).text()
# print(text)
s = pr.Profiler().pro_process(tag_value[index][0], text)
if s[0] == 1:
review_list.append(s)
index += 1
if index == len(tag_value):
print("-->", review_list)
break