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.
precision
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
*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)
— point map and normals hold at cosine 0.999 against the fp32 build.
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.
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