moge2_vits — ExecuTorch
- Source: Ruicheng/moge-2-vits-normal
- License: MIT
- Input: [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
- Output: points [1,H,W,3] metric point map, normal [1,H,W,3], mask [1,H,W] validity, metric_scale [1]
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 | moge2_vits_xnnpack_fp32.pte | 140.8 | 0.999998 | 750.9 |
| fp16 | moge2_vits_xnnpack_fp16.pte | 96.6 | 0.434851 — see below | 1714.8 |
| int8 (dynamic) | moge2_vits_xnnpack_int8.pte | 76.4 | 0.998758 | 746.5 |
| Core ML (fp16, iOS) | moge2_vits_coreml_all.pte | 73.2 | 0.541272 — see below | 87.7 |
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: 345.4 ms).
Checked in the task's own units
Correlation is a first filter. These are the numbers that decide:
- fp16 — correlation reads 0.43 on this build, and that number is an artifact: one of the four outputs is a near-binary validity mask whose raw logits correlate badly while the thresholded mask is identical. Measured properly against fp32 — mask IoU 1.0000, point map cosine 1.000000, normals cosine 1.000000, metric scale within 0.6% — the geometry is unchanged.
- int8 (dynamic) — measured in the units that matter for this model — cosine similarity of the point map and normals: median 1.0000 over 10 real images, worst 1.0000.
- Core ML (fp16, iOS) — measured in the units that matter for this model — cosine similarity of the point map and normals: median 1.0000 over 10 real images, worst 1.0000.
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, 518, 518, 3] | 1.431e-06 | 1.000000 |
| 1 | [1, 518, 518, 3] | 1.878e-06 | 1.000000 |
| 2 | [1, 518, 518] | 1.192e-07 | 0.999998 |
| 3 | [1] | 5.960e-07 | nan |
XNNPACK delegate coverage (fp32): 53.7% (623/1160 ops); ops left on the portable kernels: aten.arange.start_step x90, aten.clamp.default x80, aten.index.Tensor x80, aten.expand_copy.default x65, aten.squeeze_copy.dims x38, aten.native_layer_norm.default x26, aten.mul.Scalar x24, aten.logical_not.default x24, aten.where.self x22, dim_order_ops._to_dim_order_copy.default x20, aten.eq.Scalar x12, aten.full_like.default x12, aten.any.dim x12, aten.lt.Scalar x10, aten.sub.Tensor x10, aten.unsqueeze_copy.default x5, aten.sum.dim_IntList x2, aten.pow.Tensor_Scalar x2, aten._upsample_bilinear2d_aa.default x1, aten.select_copy.int x1, aten.split_with_sizes_copy.default x1
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.
