Benchmarks¶
Note
These are legacy numbers and are due for a refresh. The benchmarking scripts
live under
benchmarks/.
Environment:
- CPU: Intel i9-9960X CPU @ 3.10 GHz
- GPU: Nvidia Quadro RTX 8000
- RAM: 128 GB
RAM usage doesn't exceed 20 GB; GPU memory usage at batch 64 is roughly 45 GB.
| Transform | Type | Method | Input Size | Output Size | Batch Size | Time (s) |
|---|---|---|---|---|---|---|
| cube2equi | numpy | default | 256x256 | 480x240 | 1 | 0.0360 |
| cube2equi | numpy | default | 256x256 | 480x240 | 4 | 0.1180 |
| cube2equi | numpy | default | 256x256 | 480x240 | 16 | 0.4512 |
| cube2equi | numpy | default | 256x256 | 480x240 | 32 | 1.0650 |
| cube2equi | numpy | default | 256x256 | 480x240 | 64 | 2.1363 |
| cube2equi | torch | default | 256x256 | 480x240 | 1 | 0.0218 |
| cube2equi | torch | default | 256x256 | 480x240 | 4 | 0.0199 |
| cube2equi | torch | default | 256x256 | 480x240 | 16 | 0.0228 |
| cube2equi | torch | default | 256x256 | 480x240 | 32 | 0.0335 |
| cube2equi | torch | default | 256x256 | 480x240 | 64 | 0.0438 |
| equi2cube | numpy | default | 4000x2000 | 256x256 | 1 | 0.1174 |
| equi2cube | numpy | default | 4000x2000 | 256x256 | 4 | 0.4759 |
| equi2cube | numpy | default | 4000x2000 | 256x256 | 16 | 1.8907 |
| equi2cube | numpy | default | 4000x2000 | 256x256 | 32 | 4.9468 |
| equi2cube | numpy | default | 4000x2000 | 256x256 | 64 | 10.0229 |
| equi2cube | torch | default | 4000x2000 | 256x256 | 1 | 0.0155 |
| equi2cube | torch | default | 4000x2000 | 256x256 | 4 | 0.0328 |
| equi2cube | torch | default | 4000x2000 | 256x256 | 16 | 0.0940 |
| equi2cube | torch | default | 4000x2000 | 256x256 | 32 | 0.1698 |
| equi2cube | torch | default | 4000x2000 | 256x256 | 64 | 0.3200 |
| equi2equi | numpy | default | 4000x2000 | 640x320 | 1 | 2.4446 |
| equi2equi | numpy | default | 4000x2000 | 640x320 | 4 | 9.8693 |
| equi2equi | numpy | default | 4000x2000 | 640x320 | 16 | 42.6679 |
| equi2equi | numpy | default | 4000x2000 | 640x320 | 32 | 96.5504 |
| equi2equi | numpy | default | 4000x2000 | 640x320 | 64 | 193.8804 |
| equi2equi | torch | default | 4000x2000 | 640x320 | 1 | 0.1816 |
| equi2equi | torch | default | 4000x2000 | 640x320 | 4 | 0.5867 |
| equi2equi | torch | default | 4000x2000 | 640x320 | 16 | 2.5047 |
| equi2equi | torch | default | 4000x2000 | 640x320 | 32 | 4.4535 |
| equi2equi | torch | default | 4000x2000 | 640x320 | 64 | 8.7202 |
| equi2pers | numpy | default | 4000x2000 | 640x480 | 1 | 0.0734 |
| equi2pers | numpy | default | 4000x2000 | 640x480 | 4 | 0.2994 |
| equi2pers | numpy | default | 4000x2000 | 640x480 | 16 | 1.1730 |
| equi2pers | numpy | default | 4000x2000 | 640x480 | 32 | 2.7934 |
| equi2pers | numpy | default | 4000x2000 | 640x480 | 64 | 5.4712 |
| equi2pers | torch | default | 4000x2000 | 640x480 | 1 | 0.0026 |
| equi2pers | torch | default | 4000x2000 | 640x480 | 4 | 0.0084 |
| equi2pers | torch | default | 4000x2000 | 640x480 | 16 | 0.0293 |
| equi2pers | torch | default | 4000x2000 | 640x480 | 32 | 0.0447 |
| equi2pers | torch | default | 4000x2000 | 640x480 | 64 | 0.0770 |