Cas-GwcNet (fastforks)

Cas-GwcNet (cascade stereo, Gu et al. 2020): two-stage cascade cost volume (48 then 24 disparity hypotheses, the second narrowed around the first result) on GwcNet-style 3-D hourglasses; group-wise correlation + concatenation (gc) or concatenation (c) volumes.

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/alibaba/cascade-stereo at 2ae7da65515c.

Task: stereo. Tier: experimental. Outputs: disparity. Backends: portable, kernels.

Input: a rectified left / right image pair. Output: a disparity map (pixels, left view).

Variants

Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in card.toml.

variant	weights (author link)	SHA-256	weights licence	commercial use
cas-gwcnet-c-sceneflow@1	casgwcnet-c-2.ckpt	9d30ee767111	MIT (assumed from code)	yes
cas-gwcnet-gc-sceneflow@1	casgwcnet.ckpt	8a3638501cd5	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: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa).

Licence evidence: https://github.com/alibaba/cascade-stereo/blob/2ae7da65515c82025383a0fbee8d6fe063dc7101/CasStereoNet/README.md#L17-L19

Input size

• cas-gwcnet-c-sceneflow: any H x W >= 225, padded to a multiple of 32
• cas-gwcnet-gc-sceneflow: any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): cas-gwcnet-c-sceneflow 1988x2880 (8.2 GiB peak allocated, 9.2 reserved); cas-gwcnet-gc-sceneflow 1988x2880 (9.4 GiB peak allocated, 14.1 reserved)

Speed and memory

Stereo, Middlebury 2014 half, 994x1482

model	time (ms): original fp32	original	portable	kernels	kernels speed-up: vs original	vs portable	peak memory (MiB, allocated / reserved): original	portable	kernels
cas-gwcnet-c-sceneflow	655.9	389.1	326.99	225.4	1.7x	1.5x	2791 / 2970	2006 / 2474	1725 / 1930
cas-gwcnet-gc-sceneflow	862.8	–	471.55	357.52	–	1.3x	–	2338 / 2794	2272 / 2514

Startup and first call

model	load (s): original	portable	kernels	first call (s): original	portable	kernels
cas-gwcnet-c-sceneflow	0.48 (375x1242)	1.29	0.18†	0.41 (375x1242)	0.33†	0.23†
cas-gwcnet-gc-sceneflow	0.25 (375x1242)	0.97	0.17†	0.25 (375x1242)	0.47†	0.36†

Fixed-size speed and peak memory, all models

model	mode	640x480	1280x720	1920x1080	12 MP 4000x3000
cas-gwcnet-c-sceneflow	portable	61.3 ms / 465 / 738	195.5 ms / 1386 / 2160	452.8 ms / 3046 / 4720	OOM
cas-gwcnet-c-sceneflow	kernels	44.8 ms / 442 / 568	135.5 ms / 1242 / 1586	316.1 ms / 2692 / 3408	OOM
cas-gwcnet-gc-sceneflow	portable	86.7 ms / 533 / 1166	281.6 ms / 1588 / 3308	647.4 ms / 3494 / 7262	OOM
cas-gwcnet-gc-sceneflow	kernels	66.9 ms / 510 / 956	213.7 ms / 1449 / 2606	493.8 ms / 3147 / 5634	OOM

Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).

Hardware, protocol and caveats: benchmarks (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| <= 0.05 px and p99 <= 0.5 px.

Usage

from fastforks import stereo

model = stereo.load("cas-gwcnet-c-sceneflow")  # portable PyTorch on the GPU if there is one, else the CPU
result = model("left.png", "right.png")
result.disparity  # [H, W] float32 tensor, input resolution
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: cas-gwcnet-c-sceneflow, cas-gwcnet-gc-sceneflow.

Kernels backend

stereo.load("cas-gwcnet-c-sceneflow", mode="kernels") runs our CUDA kernels (igev, cutlass_conv, cudnn_hacc). 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.