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Output
: drivable area [1,2,360,640] + lane line [1,2,360,640]
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
twinlitenet_xnnpack_fp32.pte
1.8
1.000000
32.5
*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: 23.9 ms).
Precisions that did not earn a slot
fp16 is not shipped
: it comes out at 100% of the fp32 file (1.8 MB vs 1.8 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.
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, 2, 360, 640]
1.240e-05
1.000000
1
[1, 2, 360, 640]
1.287e-05
1.000000
XNNPACK delegate coverage (fp32): 79.7% (177/222 ops); ops left on the portable kernels:
aten.gt.Scalar
x20,
aten.where.self
x20,
aten.avg_pool2d.default
x3,
aten.max.dim
x1,
aten.expand_copy.default
x1
Notes
: int8 does not run on executorch 1.4.0, and the reason is a collision between two workarounds rather than anything about this model. PReLU segfaults on the XNNPACK delegate (upstream #17559, fix #21480 not in the stable wheel), so it has to be excluded from partitioning — and a quantized graph with a portable PReLU between delegated convolutions fails shape propagation at execute. Three conv+PReLU layers reproduce it; the same graph in fp32 is corr 1.000000. Reported at
https://github.com/pytorch/executorch/pull/21480
Runs of mlboydaisuke TwinLiteNet-ExecuTorch on huggingface.co
47
Total runs
3
24-hour runs
4
3-day runs
4
7-day runs
-4
30-day runs
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