in benchmarking/frameworks/glow/glow.py [0:0]
def _maybeRepro(self, output, results):
if output is None:
return False
rows = output
if isinstance(output, string_types):
rows = output.split("\n")
i = 0
while i < len(rows):
if "Total inference duration (ms): " in rows[i]:
total_inferece_time = float(
rows[i].split("Total inference duration (ms): ")[1]
)
self._addOrAppendResult(
results,
"Total inference duration",
total_inferece_time,
{
"type": "NET",
"metric": "Total inference duration",
"unit": "ms",
"values": [],
},
)
if "Avg inference duration (ms): " in rows[i]:
avg_inference_time = float(
rows[i].split("Avg inference duration (ms): ")[1]
)
self._addOrAppendResult(
results,
"Avg inference duration",
avg_inference_time,
{
"type": "NET",
"metric": "Avg inference duration",
"unit": "scalar",
"values": [],
},
)
if "Avg inference per second: " in rows[i]:
avg_inference_per_second = float(
rows[i].split("Avg inference per second: ")[1]
)
self._addOrAppendResult(
results,
"Avg inference per second",
avg_inference_per_second,
{
"type": "NET",
"metric": "Avg inference per second",
"unit": "scalar",
"values": [],
},
)
i += 1