performance.md

Performance Studies

Might be out of date.

Profiling Decoder

Run predict with the --profile option:

python3 -m openpifpaf.predict --checkpoint resnet152 \
  docs/coco/000000081988.jpg --show --profile --debug

This will write a stats table to the terminal and also produce a decoder.prof file. You can use flameprof (pip install flameprof) to get a flame graph with flameprof decoder.prof > docs/coco/000000081988.jpg.decoder_flame.svg:

Large image

e.g., from NuScenes:

python3 -m openpifpaf.predict --checkpoint resnet152 \
  docs/nuscenes/test.jpg --show --profile --debug

Then create the flame graph with flameprof decoder.prof > docs/nuscenes/test.jpg.decoder_flame.svg to produce:

Crowded image

python3 -m openpifpaf.predict --checkpoint resnet152 \
  docs/crowd.png --show --profile --debug

Then create the flame graph with flameprof decoder.prof > docs/crowd.png.decoder_flame.svg to produce:

Low quality crowded scene

python3 -m openpifpaf.predict --checkpoint resnet152 \
  docs/mochit_station_example.jpg --show --profile --debug

Then create the flame graph with flameprof decoder.prof > docs/mochit_station_example.jpg.decoder_flame.svg to produce: