litert-community/RTMW-m-WholeBody-LiteRT

RTMW-m (Whole-Body) — LiteRT (on-device 133-keypoint pose, fully-GPU)

RTMW (mmpose, CSPNeXt + CSPNeXtPAFPN neck + RTMW/SimCC head) whole-body 2D pose, converted to LiteRT and running fully on the CompiledModel GPU (ML Drift) on Android. 133 COCO-WholeBody keypoints — 17 body + 6 feet + 68 face + 42 hands — for a single centered person.

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

nodes on GPU	531 / 531 LITERT_CL (full residency)
inference	~6 ms (256×192)
size	66 MB (fp16)
accuracy	device-vs-PyTorch SimCC corr 0.999, keypoints within 0.2 px

image[1,3,256,192] (ImageNet 0-255) →[GPU: CSPNeXt + PAFPN + RTMW]→ simcc_x[1,133,384], simcc_y[1,133,512]

Minimal usage

Android (Kotlin, CompiledModel GPU)

val model = CompiledModel.create(context.assets, "rtmw_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(chw)              // [1,3,256,192] mmpose mean/std (0-255 RGB), NCHW
model.run(inputs, outputs)
val simccX = outputs[0].readFloat()    // [1,133,384]
val simccY = outputs[1].readFloat()    // [1,133,512]; keypoint = argmax / 2

Python (desktop verification)

MEAN = np.array([123.675, 116.28, 103.53], np.float32)
STD  = np.array([58.395, 57.12, 57.375], np.float32)
import numpy as np
from PIL import Image
from ai_edge_litert.interpreter import Interpreter

img = Image.open("person.jpg").convert("RGB").resize((192, 256))  # centered subject crop
x = ((np.asarray(img, np.float32) - MEAN) / STD).transpose(2, 0, 1)[None]

it = Interpreter(model_path="rtmw_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
od = it.get_output_details()
sx, sy = (it.get_tensor(o["index"])[0] for o in od)              # [133,384], [133,512]
if sx.shape[-1] != 384: sx, sy = sy, sx                          # identify by bin count
kx, ky = sx.argmax(-1) / 2.0, sy.argmax(-1) / 2.0                 # 133 keypoints, px in 192x256
for i, (a, b) in enumerate(zip(kx, ky)):
    print(f"kp{i}: ({a:.1f}, {b:.1f})")

How it converts (litert-torch)

The RTMPose-family re-authorings (all numerically exact) plus one extra for RTMW's neck/head:

1. ScaleNorm (RMS) → SafeRMSNorm — its input overflows fp16 (Σx²≈3.6M > 65504) on Mali → norm=∞ → all-zero head; scale x down by S=64 before squaring.
2. GAU act@act BMM → broadcast-multiply + reduce-sum.
3. nn.PixelShuffle → depth-to-space ConvTranspose2d (ZeroStuffConvT2d) — the RTMW head's PixelShuffle upsample lowers to a 6D tensor (>4D, GPU-rejected); the fixed depth-to-space conv keeps it 4D and exact.

Result: banned ops NONE, all tensors ≤4D, tflite-vs-torch corr 1.0, device-vs-torch corr 0.999.

Preprocessing

Center-crop to 3:4, resize to 192×256, ImageNet 0-255 normalize (mean [123.675, 116.28, 103.53], std [58.395, 57.12, 57.375]), NCHW. Top-down — one centered person. SimCC argmax (÷ split=2) → pixel.

Performance

Measured on a Pixel 8a (Tensor G3, Android 16) with the standard TFLite benchmark_model tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.

Runtime	Backend	Graph on GPU	Latency
LiteRT CompiledModel (LITERT_CL)	GPU	531 / 531	~6 ms
TFLite benchmark_model (TfLiteGpuDelegateV2)	GPU (OpenCL)	531 / 531	38.5 ms
TFLite benchmark_model	CPU (XNNPACK, 4 threads)	—	XNNPACK declined the graph

The two GPU rows are different runtimes, not a contradiction. The LITERT_CL figure is the one recorded when this model shipped, taken through LiteRT's own CompiledModel accelerator — the path the Kotlin sample app and the LiteRT API use. The TfLiteGpuDelegateV2 figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the TfLiteGpuDelegateV2 row as a reproducible floor, not as this model's speed on LiteRT.

XNNPACK declines these fp16 graphs — it reports failed to delegate DEPTHWISE_CONV_2D and then fails to allocate tensors — so there is no usable CPU number. Disabling XNNPACK falls back to reference kernels, which measured about 20× slower than the GPU on models of this size and would not represent CPU inference anyone would ship.

License

Apache-2.0. Upstream: open-mmlab/mmpose RTMW.