library_name: fastforks
pipeline_tag: depth-estimation
license: other
license_name: mit-code-weights-unstated
license_link: >-
https://github.com/JiaRenChang/PSMNet/blob/87ac9093afbf6545c093bd9d26c5ffd66e49a7b8/README.md#L96-L98
tags:
- stereo
- stereo-matching
- disparity
- cuda
- pytorch
- fastforks
PSMNet (fastforks)
PSMNet (Chang and Chen 2018): spatial-pyramid features, a concatenation 3-D cost volume with three stacked 3-D hourglasses and soft-argmin regression (max disparity 192).
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/JiaRenChang/PSMNet at 87ac9093afbf.
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 |
|---|---|---|---|---|
psmnet-sceneflow@1 |
pretrained_sceneflow_new.tar | eb3029f7ed5e |
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/JiaRenChang/PSMNet/blob/87ac9093afbf6545c093bd9d26c5ffd66e49a7b8/README.md#L96-L98
Input size
any H x W >= 241, padded to a multiple of 16
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): psmnet-sceneflow 1988x2880 (7.6 GiB peak allocated, 10.6 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 |
|---|---|---|---|---|---|---|---|---|---|
psmnet-sceneflow |
759.6 | 607.8 | 339.98 | 231.81 | 2.6x | 1.5x | 6015 / 6090 | 2078 / 2442 | 1786 / 2122 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
psmnet-sceneflow |
0.20 (375x1242) | 0.10 | 0.08† | 0.53 (375x1242) | 0.35† | 0.23† |
torch.compile and TensorRT
| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
|---|---|---|---|---|---|---|---|---|---|---|
psmnet-sceneflow |
994x1482 | 339.98 | 439.9 | 60.0 | 668.8 | 89 | 231.81 | 2078 / 2442 | 1760 / 2082 | 1528 |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
psmnet-sceneflow |
portable | 69.0 ms / 426 / 606 | 206.2 ms / 1256 / 1780 | 479.2 ms / 2834 / 3966 | OOM |
psmnet-sceneflow |
kernels | 49.6 ms / 415 / 518 | 140.3 ms / 1108 / 1414 | 329.2 ms / 2459 / 3104 | 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("psmnet-sceneflow") # mode="auto": the preferred backend from recorded measurements
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: psmnet-sceneflow.
Kernels backend
stereo.load("psmnet-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.