rtmpose_s_body — ExecuTorch
- Source: open-mmlab/mmpose RTMPose-s body7 (rtmpose-s_simcc-body7_pt-body7_420e-256x192)
- License: Apache-2.0
- Input: [[1, 3, 256, 192]] — RGB, ImageNet norm, 256x192 person crop (detect first, then crop and resize to this aspect)
- Output: SimCC pair: x [1,17,384] and y [1,17,512] — 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_s_body_xnnpack_fp32.pte | 21.9 | 1.000000 | 5.7 |
*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: 34.3 ms).
Builds that did not earn a slot
- fp16 is not shipped: measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.7647 over 10 real images, worst 0.3529.
- int8 is not shipped: measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.0000 over 10 real images, worst 0.0000.
- Core ML (fp16, iOS) is not shipped: measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.9412 over 10 real images, worst 0.8824.
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, 17, 384] | 6.855e-06 | 1.000000 |
| 1 | [1, 17, 512] | 8.076e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 92.0% (229/249 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)
More models in this format: ExecuTorch Model Zoo — 31 models, each with the recipe that produced it.
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.
