dis_isnet — ExecuTorch XNNPACK
- Source: xuebinqin/DIS + NimaBoscarino/IS-Net_DIS-general-use weights
- License: Apache-2.0
- Input: [[1, 3, 1024, 1024]] — RGB, scaled to [-1,1] (x/255 then (x-0.5)/0.5), 1024x1024
- Output: alpha mask [1,1,1024,1024] 0-1 (sigmoid)
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 | dis_isnet_xnnpack_fp32.pte |
176.1 | 1.000000 | 123.4 |
*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: 364.6 ms).
Precisions that did not earn a slot
- fp16 is not shipped: worst-output corr 0.986 against fp32 eager, below the 0.995 bar for this precision. The file converts and runs; the numbers do not hold up, so it is left out rather than shipped with a warning.
- int8 is not shipped: measured in the units that matter for this model — mask IoU at 0.5: median 0.9106 over 10 real images, worst 0.4647.
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, 1, 1024, 1024] | 3.606e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 100.0% (467/467 ops)
Conversion
torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte (conversion scripts: executorch-models)
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