rtmpose_m_face — ExecuTorch
- Source: open-mmlab/mmpose RTMPose (rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400)
- License: Apache-2.0
- Input: [[1, 3, 256, 256]] — RGB, ImageNet norm, 256x256 crop around a face (detect first, then crop and resize to this aspect)
- Output: SimCC pair: x [1,106,512] and y [1,106,512] — a 1-D distribution per keypoint per axis. Decode: keypoint k sits at (argmax(x[k]) / 2, argmax(y[k]) / 2) in crop pixels; the max value doubles as the confidence.
Variants
All variants take and return fp32 tensors — swap the .pte file, keep your app code.
| build | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
|---|---|---|---|---|
| fp32 | rtmpose_m_face_xnnpack_fp32.pte |
67.9 | 1.000000 | 10.4 |
| Core ML (fp16, iOS) | rtmpose_m_face_coreml_all.pte |
34.4 | 0.999897 | 3.3 |
The Core ML build is the same graph lowered to Apple's Neural Engine instead of XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it runs 3.5x to 13.9x faster (median 12x) at roughly half the file size — for example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms against 5.9 ms. It computes in fp16 and is iOS-only; the XNNPACK files stay the portable option and are what runs on Android.
*Mac arm64, single process, median of 10 — a reference point for relative cost only, not a device number (torch eager fp32 on the same machine: 94.4 ms).
Checked in the task's own units
Correlation is a first filter. These are the numbers that decide:
- Core ML (fp16, iOS) — measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.9811 over 10 real images, worst 0.9151.
Verification (executorch 1.4.0, torch 2.13.0)
Parity is measured against the fp32 eager model on real image input; corr is
the correlation over all elements of each output tensor.
| output | shape | max_abs_diff | corr |
|---|---|---|---|
| 0 | [1, 106, 512] | 1.147e-05 | 1.000000 |
| 1 | [1, 106, 512] | 9.179e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 93.9% (306/326 ops); ops left on the portable kernels: dim_order_ops._to_dim_order_copy.default x8, aten.unsqueeze_copy.default x3, aten.split_with_sizes_copy.default x3, aten.sum.dim_IntList x2, aten.pow.Tensor_Scalar x2, aten.squeeze_copy.dims x2
Conversion
torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)
Notes: Top-down: crop one face first. In practice a portrait framed on the head works directly, which is how the card check exercises it.
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