mlboydaisuke/RTMPose-s-Body-ExecuTorch

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)