raft_small — ExecuTorch
- Source: torchvision raft_small (C_T_V2, FlyingChairs + FlyingThings3D)
- License: BSD-3-Clause
- Input: [[1, 3, 384, 512], [1, 3, 384, 512]] — two RGB frames, each scaled to [-1,1], 384x512 (both dimensions must stay divisible by 8)
- Output: flow [1,2,384,512] in pixels: channel 0 is horizontal displacement from frame 1 to frame 2, channel 1 vertical. Refined over 12 iterations, which are baked into the graph — the intermediate iterates are not returned.
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 | raft_small_xnnpack_fp32.pte | 4.4 | 1.000000 | 173.0 |
| Core ML (fp32, iOS) | raft_small_coreml_all_fp32.pte | 10.6 | 1.000000 | 12.0 |
*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: 140.6 ms).
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, 384, 512] | 4.792e-04 | 1.000000 |
XNNPACK delegate coverage (fp32): 63.0% (1209/1920 ops); ops left on the portable kernels: dim_order_ops._to_dim_order_copy.default x435, aten.split_with_sizes_copy.default x50, aten.grid_sampler_2d.default x48, aten.expand_copy.default x30, aten.arange.start_step x28, aten.lt.Scalar x24, aten.sub.Tensor x24, aten.where.self x24, aten._native_batch_norm_legit.no_stats x21, aten.alias_copy.default x12, aten.view_copy.default x6, aten.avg_pool2d.default x3, aten.cat.default x2, aten.unsqueeze_copy.default x2, aten.repeat.default x2
Conversion
torch.export -> to_edge_transform_and_lower(partitioner) -> .pte
(conversion scripts:
executorch-models)
