Add MLX conversions of LoMa B/B128/L/G/R (upstream davnords/LoMa@8fb59c4)

LICENSE-APACHE

@@ -0,0 +1,203 @@
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LICENSE-MIT

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+This repository redistributes converted model weights under two licenses.
+The MIT-licensed components and their copyright notices are listed below.
+The DINOv2 ViT-L/14 backbone and the LoMa matcher are covered by LICENSE-APACHE.
+
+================================================================
+davnords/LoMa (https://github.com/davnords/LoMa)
+================================================================
+MIT License
+
+Copyright (c) 2026 Johan Edstedt
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+================================================================
+Parskatt/DeDoDe (https://github.com/Parskatt/DeDoDe)
+================================================================
+MIT License
+
+Copyright (c) 2023 Johan Edstedt
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+================================================================
+Parskatt/dad (https://github.com/Parskatt/dad)
+================================================================
+MIT License
+
+Copyright (c) 2025 Johan Edstedt
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

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+---
+license: other
+license_name: mit-and-apache-2.0
+license_link: https://huggingface.co/mnmly/LoMa-mlx/blob/main/LICENSE-MIT
+library_name: mlx
+pipeline_tag: keypoint-detection
+tags:
+  - mlx
+  - mlx-swift
+  - feature-matching
+  - keypoint-matching
+  - local-features
+  - apple-silicon
+---
+
+# LoMa — MLX weights (unofficial conversion)
+
+MLX-format (`.safetensors`, NHWC) conversions of the five released
+**[LoMa: Local Feature Matching Revisited](https://github.com/davnords/LoMa)** (ECCV 2026)
+checkpoints. They are for use with the `mlx-swift-LoMa` Swift package (LoMaKit), which
+runs on Apple Silicon.
+
+> **Unofficial.** These files were converted by a third party. They are not produced or endorsed by the
+> LoMa authors. All credit for the models goes to the original authors (see [Citation](#citation)).
+
+Each file bundles the whole pipeline: the **DaD** keypoint detector, the **DeDoDe** descriptor
+(VGG19-BN, plus DINOv2 ViT-L/14 for the `dedode_g` variants) and the **LoMa matcher**.
+
+| File | Upstream checkpoint | Matcher (embed / heads) | Descriptor | Size |
+|---|---|---|---|---|
+| `loma_b.safetensors` | `loma_B.pt` | 256 / 4 | DeDoDe-G, 256-d | 758 MB |
+| `loma_b128.safetensors` | `loma_B128.pth` | 256 / 4 | DeDoDe-B, 128-d | 127 MB |
+| `loma_l.safetensors` | `loma_L.pth` | 512 / 8 | DeDoDe-G, 256-d | 900 MB |
+| `loma_g.safetensors` | `loma_G.pth` | 1024 / 16 | DeDoDe-G, 256-d | 1.47 GB |
+| `loma_r.safetensors` | `loma_R.pth` (rotation-invariant) | 256 / 4 | DeDoDe-G, 256-d | 758 MB |
+
+Each `loma_<v>.json` holds that variant's configuration, the SHA-256 of the upstream
+checkpoint it was converted from, and the SHA-256 of the converted file. The same provenance
+is embedded in each safetensors header (`__metadata__`).
+
+## Provenance and modifications
+
+- **Source:** the upstream checkpoints from
+  `https://github.com/davnords/storage/releases/download/loma/…`, converted against
+  `davnords/LoMa` at commit `8fb59c458f2a2ef44314f462467b229c27786bf0`.
+- **Changes, all structural (no retraining, fine-tuning or quantization):**
+  - Conv kernels transposed from PyTorch NCHW `(O, I, kH, kW)` to MLX NHWC `(O, kH, kW, I)`.
+  - Numeric `nn.Sequential` / `nn.ModuleDict` keys renamed (e.g. `ffn.3` → `ffn.fc2`,
+    `decoder.layers.8` → `decoder.scale8`, VGG `layers.{i}` → `convs.{j}` / `norms.{j}`).
+  - `num_batches_tracked` buffers dropped. Transformer layers beyond `n_layers = 9` dropped,
+    as upstream's own loader does.
+  - Tensor dtypes kept as stored: fp32, and bf16 for DINOv2.
+
+## Verification
+
+The mlx-swift port was checked against the PyTorch reference for all five variants:
+- **Exact CPU stream:** every stage agrees to a relative error of 1e-5 or better.
+- **GPU float32, full resolution:** match-set IoU of 0.97–1.0.
+- **End to end vs the upstream `LoMa.match` (MPS, fp16):** 88–97% of Python's matches
+  reproduced within 2 px. Most of the remaining gap comes from JPEG decoding (ImageIO vs
+  libjpeg), not the model.
+
+## Usage (Swift)
+
+```swift
+import LoMaKit
+
+let session = try LoMaSession.load(variant: .b, weightsURL: localURL(of: "loma_b.safetensors"))
+let result = try session.match(contentsOf: imageA, imageB)
+for m in result.matches() { print(m.pointA, m.pointB, m.score) }
+```
+
+## Licenses
+
+The files combine components under two permissive licenses:
+
+| Component | Upstream | License |
+|---|---|---|
+| DaD detector | [Parskatt/dad](https://github.com/Parskatt/dad) | MIT |
+| DeDoDe descriptor (VGG19 + refiners) | [Parskatt/DeDoDe](https://github.com/Parskatt/DeDoDe) | MIT |
+| DINOv2 ViT-L/14 backbone | [facebookresearch/dinov2](https://github.com/facebookresearch/dinov2) | Apache-2.0 |
+| LoMa matcher | [davnords/LoMa](https://github.com/davnords/LoMa) (derived from [LightGlue](https://github.com/cvg/LightGlue)) | Apache-2.0 |
+| Everything else in LoMa | [davnords/LoMa](https://github.com/davnords/LoMa) | MIT |
+
+See `LICENSE-MIT` (with the upstream copyright notices) and `LICENSE-APACHE`.
+
+The models were trained by their authors on third-party datasets (e.g. MegaDepth), and the
+VGG backbones were initialized from ImageNet-pretrained weights. Refer to the upstream papers
+for data details and any dataset terms.
+
+## Citation
+
+If you use these weights, please cite the original works:
+
+```bibtex
+@inproceedings{nordstrom2026loma,
+  title={LoMa: Local Feature Matching Revisited},
+  author={David Nordström and Johan Edstedt and Georg Bökman and Jonathan Astermark and Anders Heyden and Viktor Larsson and Mårten Wadenbäck and Michael Felsberg and Fredrik Kahl},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  year={2026}
+}
+
+@inproceedings{nordstrom2026who,
+  title={Who Handles Orientation? Investigating Invariance in Feature Matching},
+  author={David Nordström and Johan Edstedt and Georg Bökman and Fredrik Kahl},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+  year={2026}
+}
+
+@article{edstedt2025dad,
+  title={{DaD: Distilled Reinforcement Learning for Diverse Keypoint Detection}},
+  author={Edstedt, Johan and B{\"o}kman, Georg and Wadenb{\"a}ck, M{\aa}rten and Felsberg, Michael},
+  journal={arXiv preprint arXiv:2503.07347},
+  year={2025}
+}
+
+@inproceedings{edstedt2024dedode,
+  title={{DeDoDe: Detect, Don't Describe --- Describe, Don't Detect for Local Feature Matching}},
+  author={Johan Edstedt and Georg Bökman and Mårten Wadenbäck and Michael Felsberg},
+  booktitle={2024 International Conference on 3D Vision (3DV)},
+  year={2024},
+  organization={IEEE}
+}
+
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+
+@inproceedings{lindenberger2023lightglue,
+  title={{LightGlue: Local Feature Matching at Light Speed}},
+  author={Philipp Lindenberger and Paul-Edouard Sarlin and Marc Pollefeys},
+  booktitle={ICCV},
+  year={2023}
+}
+```

loma_b.json

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+{
+  "variant": "b",
+  "input_dim": 256,
+  "embed_dim": 256,
+  "num_heads": 4,
+  "n_layers": 9,
+  "descriptor": "dedode_g",
+  "filter_threshold": 0.1,
+  "num_keypoints": 2048,
+  "detector": {
+    "arch": "dedode_s",
+    "resize": 1024,
+    "nms_size": 3,
+    "subpixel": true,
+    "subpixel_temp": 0.5
+  },
+  "descriptor_input": [
+    784,
+    784
+  ],
+  "source_checkpoint": "loma_B.pt",
+  "source_sha256": "3a38824391e22b33bb3e10377c7736243fd8a2fbf1561446e7e133e497c35758",
+  "sha256": "063372e1f699e0b2503c63303e3c373a0cd5f16ee04567241a199cebbbebf36b",
+  "upstream": "https://github.com/davnords/LoMa@8fb59c458f2a2ef44314f462467b229c27786bf0"
+}

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loma_b128.json

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+{
+  "variant": "b128",
+  "input_dim": 128,
+  "embed_dim": 256,
+  "num_heads": 4,
+  "n_layers": 9,
+  "descriptor": "dedode_b",
+  "filter_threshold": 0.1,
+  "num_keypoints": 2048,
+  "detector": {
+    "arch": "dedode_s",
+    "resize": 1024,
+    "nms_size": 3,
+    "subpixel": true,
+    "subpixel_temp": 0.5
+  },
+  "descriptor_input": [
+    784,
+    784
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+  "source_checkpoint": "loma_B128.pth",
+  "source_sha256": "b509cb295b543e6eae37dea76185e805c4e87ff7e35e0a84cad301be1b5984fc",
+  "sha256": "c6ebcfad07fce5a4ffd4f29b159c1040f9fffb375a2d6b1f2299c52c291a2830",
+  "upstream": "https://github.com/davnords/LoMa@8fb59c458f2a2ef44314f462467b229c27786bf0"
+}

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+size 127323996

loma_g.json

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+{
+  "variant": "g",
+  "input_dim": 256,
+  "embed_dim": 1024,
+  "num_heads": 16,
+  "n_layers": 9,
+  "descriptor": "dedode_g",
+  "filter_threshold": 0.1,
+  "num_keypoints": 2048,
+  "detector": {
+    "arch": "dedode_s",
+    "resize": 1024,
+    "nms_size": 3,
+    "subpixel": true,
+    "subpixel_temp": 0.5
+  },
+  "descriptor_input": [
+    784,
+    784
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+  "source_checkpoint": "loma_G.pth",
+  "source_sha256": "9d925dc9861fb8e52489a1d2d2354780d5d38e34cbe0c6b10830d0ebe6055941",
+  "sha256": "e8f5fb387bf7b328b52c7014772858c0c8c893d59308ee744a81a0a64b46d481",
+  "upstream": "https://github.com/davnords/LoMa@8fb59c458f2a2ef44314f462467b229c27786bf0"
+}

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loma_l.json

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+{
+  "variant": "l",
+  "input_dim": 256,
+  "embed_dim": 512,
+  "num_heads": 8,
+  "n_layers": 9,
+  "descriptor": "dedode_g",
+  "filter_threshold": 0.1,
+  "num_keypoints": 2048,
+  "detector": {
+    "arch": "dedode_s",
+    "resize": 1024,
+    "nms_size": 3,
+    "subpixel": true,
+    "subpixel_temp": 0.5
+  },
+  "descriptor_input": [
+    784,
+    784
+  ],
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