| --- |
| license: mit |
| base_model: litert-community/DewarpNet-LiteRT |
| base_model_relation: quantized |
| library_name: openvino |
| pipeline_tag: image-to-image |
| tags: |
| - openvino |
| - onnx |
| - intel-npu |
| --- |
| |
| # DewarpNet-OpenVINO |
|
|
| DewarpNet for Intel NPUs, GPUs and CPUs: dewarp from [litert-community/DewarpNet-LiteRT](https://huggingface.co/litert-community/DewarpNet-LiteRT/tree/dfc68e3e462a6eb7eb8c4dbbff4049e3bd3cc9e3) as ONNX (tf2onnx 1.17.0, opset 17), OpenVINO IR with fp32 weights, and a precompiled Intel NPU blob. On the NPU of a Core Ultra 9 386H the blob runs in 8.24 ms per run, numpy input to numpy output. |
|
|
| Scripts, raw records, and the same table for all 52 graphs of LiteRT-Models: https://github.com/john-rocky/windows-zoo |
|
|
| ## Files |
|
|
| | file | bytes | sha256 | |
| |---|---|---| |
| | `dewarp.onnx` | 189,339,174 | `4dde2be5fa86a4b4b1dd870b0ba89dc5da6e2165b90992c13a96450935eb04c2` | |
| | `openvino/dewarp.xml` | 1,139,702 | `36060c9fd58b3fec9df7c95314892385f25240e9956a8005a05d2af914188e75` | |
| | `openvino/dewarp.bin` | 188,529,172 | `840b093d86a174c4694c0ff3a37a5270576bac67e4c2b4ff9737a279929f89bd` | |
| | `npu/dewarp.blob` | 19,378,305 | `345b2e8bb9239bf6df1e91a2ca8bad42ff83906e29087b755459cb61c9b669f3` | |
|
|
| The NPU blob was compiled with OpenVINO 2026.4.1, Intel NPU driver 32.0.100.4841 (OpenVINO reports NPU_DRIVER_VERSION 1004841, NPU_COMPILER_VERSION 524292). If `import_model` refuses a blob on another driver or OpenVINO version, compile the IR instead (the commented line in the snippet). |
|
|
| ## How it was measured |
|
|
| MSI Stealth 16 AI+ (Core Ultra 9 386H, RTX 5080 Laptop GPU at a 95 W power limit, Windows 11 build 26200.9711, on AC power), 2026-11-06 to 2026-11-07. On the laptop, each row is one process: load, one run, 5 warmup runs, then the median of 30 timed runs (2 warmup + 10 timed runs when the first run takes more than 3 s), numpy input to numpy output, every runtime at its default settings, one process at a time, each started after the CPU, the RTX and the Intel GPU had gone idle (the thresholds are in METHOD.md of the GitHub repo). Output check: each output against the CPU output in fp32 on the same input (ONNX Runtime's CPU EP); PASS = cosine ≥ 0.99, no NaN or Inf, and top-1 agreement ≥ 0.99 for class scores. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer). |
|
|
| ## Speed and output check |
|
|
| | processor | runtime | file | ms | load s | output check | |
| |---|---|---|---|---|---| |
| | Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 8.24 | 0.01 | PASS (cos ≥ 0.9999) | |
| | Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | - | - | did not run | |
| | Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 9.07 | 1.74 | FAIL: output_0_output: cos 0.9891 < 0.99 | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 6.74 | 1.51 | PASS (cos ≥ 0.9999) | |
| | RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 8.53 | 1.47 | PASS (cos ≥ 0.9999) | |
| | Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 201.36 | 0.65 | reference | |
| | Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 5.04 | 0.34 | not compared (AI Hub profile job, 2026-10-30) | |
| | Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | `.onnx`, compiled when it loads | 24.36 (19 layers on the CPU) | 6.26 | not compared (AI Hub profile job, 2026-10-30) | |
| | Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 4.76 | 0.23 | not compared (AI Hub profile job, 2026-10-30) | |
|
|
| The ONNX file against the source `.tflite` (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output. |
|
|
| ## Run it on the Intel NPU |
|
|
| Download this repo, then run the snippets from its folder: |
|
|
| ```sh |
| pip install huggingface_hub |
| hf download mlboydaisuke/DewarpNet-OpenVINO --local-dir DewarpNet-OpenVINO |
| cd DewarpNet-OpenVINO |
| ``` |
|
|
| OpenVINO (`pip install openvino==2026.4.1`): |
|
|
| ```python |
| import numpy as np |
| import openvino as ov |
| |
| core = ov.Core() |
| if "NPU" not in core.available_devices: |
| raise SystemExit(f"no Intel NPU visible to OpenVINO: {core.available_devices}") |
| with open("npu/dewarp.blob", "rb") as f: |
| compiled = core.import_model(f.read(), "NPU") # precompiled: no compile at load |
| # or compile the IR on this machine: compiled = core.compile_model("openvino/dewarp.xml", "NPU") |
| # replace the zeros with your preprocessed input (float32, the shapes printed by compiled.inputs) |
| feeds = {i.get_any_name(): np.zeros(list(i.shape), np.float32) for i in compiled.inputs} |
| outputs = compiled(feeds) |
| print({o.get_any_name(): outputs[o].shape for o in compiled.outputs}) |
| ``` |
|
|
| ONNX Runtime 1.31.1 with the OpenVINO EP 1.8.1 does not run `dewarp.onnx` on the NPU: the session fails to load (`Compilation failed`). The OpenVINO lines above run it. |
|
|
| ## Licence |
|
|
| MIT, the licence of [litert-community/DewarpNet-LiteRT](https://huggingface.co/litert-community/DewarpNet-LiteRT). |
|
|
