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license: mit
library_name: litert
pipeline_tag: image-segmentation
tags:
- litert
- tflite
- android
- on-device
- gpu
- face-parsing
- face-segmentation
- bisenet
- celebamask-hq
- real-time
---
# BiSeNet Face Parsing — LiteRT (GPU)
On-device **real-time face parsing** running **fully on the LiteRT `CompiledModel`
GPU** delegate (no CPU fallback). [BiSeNet](https://arxiv.org/abs/1808.00897)
(zllrunning/face-parsing.PyTorch) segments a face into the **19 CelebAMask-HQ
classes** (skin, brows, eyes, nose, lips, ears, hair, hat, glasses, neck, cloth, …)
— for AR / beauty / makeup. ~22 ms/frame on a Pixel 8a.
- **Architecture:** BiSeNet (ResNet18 backbone + context path + feature-fusion) — pure CNN.
- **Weights:** [zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) · MIT · ~13.3 M params.
- **Size:** 53 MB.
## I/O
- **Input:** `[1, 3, 512, 512]` NCHW, RGB, ImageNet-normalized
(mean `[0.485,0.456,0.406]`, std `[0.229,0.224,0.225]`).
- **Output:** `[1, 19, 512, 512]` class logits — argmax over the 19 classes per pixel.
Classes: `background, skin, l_brow, r_brow, l_eye, r_eye, eyeglass, l_ear, r_ear,
earring, nose, mouth, u_lip, l_lip, neck, necklace, cloth, hair, hat`.
## GPU conversion
BiSeNet is a pure CNN; three re-authoring patches make it a **fully GPU-compatible
graph — 74/74 nodes on the delegate, 1 partition** (device corr 0.99999, argmax
99.96% vs PyTorch):
1. **`align_corners=True` → `False`** — the output upsamples use `align_corners=True`,
which the GPU delegate rejects (1.6% argmax change vs original).
2. **global `avg_pool2d(x, x.size()[2:])` → `mean([2,3])`** — the context/attention
modules pool with a full-spatial kernel, which the Mali delegate rejects as an
`AVERAGE_POOL_2D`; a MEAN reduce is supported.
3. **zero-pad maxpool** — the ResNet stem `MaxPool2d(padding=1)` lowers to a PADV2
with `-inf` padding (`PADV2: src has wrong size` on Mali); an explicit 0-pad +
unpadded maxpool is exact (input is post-ReLU ≥ 0).
CPU-exact vs PyTorch (corr 0.99999999999).
## Minimal usage
### Kotlin (Android, LiteRT CompiledModel GPU)
```kotlin
val options = CompiledModel.Options(Accelerator.GPU)
val model = CompiledModel.create(context.assets, "faceparsing.tflite", options, null)
val inBufs = model.createInputBuffers()
val outBufs = model.createOutputBuffers()
inBufs[0].writeFloat(inputNCHW) // [1,3,512,512], RGB, ImageNet-norm
model.run(inBufs, outBufs)
val logits = outBufs[0].readFloat() // [19,512,512] (NCHW, batch dropped)
val hw = 512 * 512
val label = IntArray(hw) { i ->
var best = 0; var bv = logits[i]
for (c in 1 until 19) { val v = logits[c * hw + i]; if (v > bv) { bv = v; best = c } }
best
}
```
### Python (LiteRT / ai-edge-litert)
```python
from ai_edge_litert.interpreter import Interpreter
import numpy as np
it = Interpreter(model_path="faceparsing.tflite"); it.allocate_tensors()
inp, out = it.get_input_details(), it.get_output_details()
it.set_tensor(inp[0]["index"], x) # [1,3,512,512] float32, ImageNet-norm
it.invoke()
logits = it.get_tensor(out[0]["index"])[0] # [19,512,512]
label = logits.argmax(0) # [512,512] class ids
```
## Conversion
Converted with **litert-torch** (`build_faceparsing.py`): loads the trained BiSeNet
weights, applies the three patches, and exports.
## License
MIT (BiSeNet / zllrunning/face-parsing.PyTorch). CelebAMask-HQ label taxonomy.
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