pidnet_s_cityscapes — ExecuTorch
- Source: XuJiacong/PIDNet + oenpu/PIDNet_S_enlight_friendly_onnx weights
- License: MIT
- Input: [[1, 3, 1024, 1024]] — RGB, ImageNet norm, 1024x1024
- Output: class logits [1,19,128,128] (argmax + upsample in app)
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 | pidnet_s_cityscapes_xnnpack_fp32.pte |
30.5 | 1.000000 | 27.6 |
| Core ML (fp16, iOS) | pidnet_s_cityscapes_coreml_all.pte |
15.8 | 0.999998 | 6.1 |
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: 64.5 ms).
Builds that did not earn a slot
- fp16 is not shipped: it comes out at 100% of the fp32 file (30.5 MB vs 30.5 MB), so it buys nothing. XNNPACK serializes convolution weights as fp32 no matter what dtype the graph carries, so on a conv-heavy model fp16 saves no disk and only adds cast operations. Reach for int8 here, not fp16.
- int8 is not shipped: measured in the units that matter for this model — fraction of pixels keeping their class: median 0.9802 over 10 real images, worst 0.8997.
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, 19, 128, 128] | 1.696e-05 | 1.000000 |
XNNPACK delegate coverage (fp32): 96.3% (263/273 ops); ops left on the portable kernels: dim_order_ops._to_dim_order_copy.default x3, aten.avg_pool2d.default x3, aten.sum.dim_IntList x2, aten.unsqueeze_copy.default x2
Conversion
torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)
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