litert-community/CPGA-Net-LowLight-LiteRT

CPGA-Net — LiteRT (on-device low-light enhancement, fully-GPU)

CPGA-Net (Shyandram, IJPRAI), low-light image enhancement (brighten dark photos) via Channel Prior + Gamma Correction, converted to LiteRT and running fully on the CompiledModel GPU (ML Drift) on Android. 0.025 M params / 0.1 MB fp16 — the smallest model in the LiteRT-Models zoo.

On-device (Pixel 8a, Tensor G3 — verified)

nodes on GPU	135 / 135 LITERT_CL (full residency)
inference	~2 ms (256×256)
size	0.1 MB (fp16)
accuracy	device-vs-PyTorch corr 0.99999, no NaN

image[1,3,256,256] (RGB [0,1]) →[GPU: CPGA-Net]→ enhanced[1,3,256,256] ([0,1])

How it converts (litert-torch) — three numerically-exact fixes

1. Gamma correction x^γ → exp(γ·log x) — torch.pow lowers to the banned POW; the identity x^γ = exp(γ·log x) is exact (base clamped to [1e-9,1]) and uses native EXP/LOG.
2. CBAM / gamma global pools → mean(3).mean(2) and F.max_pool2d(x, (H,W)).
3. The dark/bright channel prior (max/min over RGB) stays as REDUCE_MAX/REDUCE_MIN.

Banned ops NONE, ≤4D, tflite-vs-torch corr 1.0, device-vs-torch corr 0.99999. Guided-filter disabled.

Preprocessing

Center-crop, resize 256×256, RGB scaled to [0,1], NCHW.

License

MIT. Upstream: Shyandram/CPGA-Net-Pytorch.