fastforks 0.1.0

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,98 @@
+---
+library_name: "fastforks"
+pipeline_tag: "image-to-3d"
+license: "other"
+license_name: "mit-code-weights-unstated"
+license_link: "https://github.com/FangjinhuaWang/PatchmatchNet/blob/8dc6cb4/LICENSE"
+tags:
+- "multiview"
+- "multi-view-stereo"
+- "3d-reconstruction"
+- "camera-pose-estimation"
+- "cuda"
+- "pytorch"
+- "fastforks"
+---
+# PatchmatchNet (fastforks)
+
+PatchmatchNet (Wang et al. 2021): learned PatchMatch multi-view stereo, the depth of a reference view from posed, calibrated source views and a depth search range. Three coarse-to-fine PatchMatch stages (random initialisation, adaptive propagation, group-wise correlation with pixel-wise view weights, adaptive spatial cost aggregation) on FPN features, then a depth refinement net at full resolution.
+
+This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/FangjinhuaWang/PatchmatchNet> at `8dc6cb4`.
+
+Task: multiview. Tier: experimental. Outputs: `depth`, `confidence`. Backends: `portable`, `kernels`.
+
+Input: several images of one scene (posed and calibrated for multi-view stereo, unposed otherwise). Output: depth maps and, where the model predicts them, cameras and 3-D points.
+
+## Variants
+
+Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/multiview/models/patchmatchnet/card.toml).
+
+| variant | weights (author link) | SHA-256 | weights licence | commercial use |
+|---|---|---|---|---|
+| `patchmatchnet@1` | [params_000007.ckpt](https://github.com/FangjinhuaWang/PatchmatchNet/raw/8dc6cb40bdb7053e856598b378425c76a9dcf5e0/checkpoints/params_000007.ckpt) | `477b056c6235` | MIT (assumed from code) | yes |
+
+## Licence
+
+Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: `LICENSE`.
+
+Code licence: MIT; the authors do not state a separate weights licence.
+
+Training data: DTU Robot Image Data Sets (MVS 2014) (The released checkpoint (checkpoints/params_000007.ckpt) is trained on DTU (README); the paper evaluates it zero-shot on Tanks and Temples and ETH3D).
+
+- Licence note: The code is MIT; the checkpoint ships in the repository without a licence of its own (MIT assumed) and is trained on DTU, whose terms of use are not stated.
+
+Licence evidence: https://github.com/FangjinhuaWang/PatchmatchNet/blob/8dc6cb4/LICENSE and README.md (the checkpoint ships in the repository)
+
+## Input size
+
+any H x W >= 5, resized or padded inside the model
+Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `patchmatchnet` 1988x2880 (7.9 GiB peak allocated, 9.4 reserved)
+
+## Speed and memory
+
+### Multiview, synthetic, 1536x1024
+
+| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `patchmatchnet` | 248.06 | 219.74 | 201.66 | 29.57 | 7.4x | 6.8x | 1228 / 1272 | 1806 / 2096 | 740 / 856 |
+
+### Startup and first call
+
+| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- |
+| `patchmatchnet` | 0.04 (1536x1024) | 0.06 (1536x1024) | 0.05† (1536x1024) | 0.92 (1536x1024) | 0.25 (1536x1024) | 0.04 (1536x1024) |
+
+### Fixed-size speed and peak memory, all models
+
+| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
+| --- | --- | --- | --- | --- | --- |
+| `patchmatchnet` | portable | 43.1 ms / 439 / 522 | 154.4 ms / 1242 / 1534 | 408.6 ms / 2753 / 3346 | OOM |
+| `patchmatchnet` | kernels | 9.2 ms / 253 / 328 | 24.4 ms / 693 / 942 | 56.7 ms / 1520 / 2016 | 378.6 ms / 8628 / 11332 |
+
+Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
+
+Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
+
+## Accuracy
+
+### Parity with the original code
+
+Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.5 % and p99 <= 3 % of the reference's median |value|.
+
+## Usage
+
+```python
+from fastforks import multiview
+
+model = multiview.load("patchmatchnet")  # portable PyTorch on the GPU if there is one, else the CPU
+result = model(["view0.jpg", "view1.jpg", "view2.jpg"])  # the views of one scene
+result.depth  # [V, H, W] float32 tensor; result.kind says what the maps mean
+# calibrated MVS: model(views, intrinsics=K, cam_to_world=c2w), K [N, 3, 3], c2w [N, 4, 4]
+result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
+```
+
+Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `patchmatchnet`.
+
+## Kernels backend
+
+`multiview.load("patchmatchnet", mode="kernels")` runs our CUDA kernels (`patchmatchnet`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.