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Accelerate xImage evaluation metrics with optional CuPy - #131
Image evaluation can now use CUDA alongside reconstruction. This adds an optional CuPy engine beside cuphoton.ximage.metrics for FFT sharpness, background statistics, finite coverage, PSNR, and SSIM. It uses float64 computation and grouped scalar result transfers. evaluate-reconstruction and benchmark-devices --evaluate accept --metrics-backend auto|cpu|cupy; reports identify the metric engine separately from the reconstruction backend.
CPU selection uses the NumPy/SciPy reference. Automatic selection falls back when GPU setup is unavailable. Benchmark evaluation releases unused CuPy cache blocks before later Torch iterations. Photometry and WCS alignment remain in the Rubin adapter on CPU.
Validation: the assembled stack passes 3,772 CPU tests (462 optional/GPU skips), all 33 xImage GPU tests without skips, repository hooks, and source packaging. Tests cover finite/missing data, empty support, constants, backend failures, CLI routing, and saved-run evaluation. The CuPy implementation is unchanged by its move beside the generic metrics. Existing production measurements were not rerun for this restack.
Stacked on #130. The guide documents how logarithmic Fourier sharpness can amplify FFT roundoff near zero spectral coefficients; select cpu for comparisons with CPU baselines.
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Reviewing files that changed from the base of the PR and between 6286e07 and 0773ea8.
Files selected for processing: 9
docs/components/ximage.md
src/cuphoton/ximage/_evaluation_cupy.py
src/cuphoton/ximage/benchmark.py
src/cuphoton/ximage/commands.py
src/cuphoton/ximage/evaluation.py
tests/core/test_cli_contract.py
tests/ximage/test_benchmark.py
tests/ximage/test_cli.py
tests/ximage/test_evaluation.py
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Walkthrough
Image evaluation now supports CPU, CuPy, and automatic metric backends. Reconstruction evaluation and device benchmarking accept a backend selection. Evaluation results report the selected backend.
Adds --metrics-backend to evaluation and benchmarking commands and passes the selection to evaluation. Benchmarking releases CuPy's default memory pool after CuPy evaluation. Updates CLI contract tests and documents backend behavior.
Possibly related PRs
NVIDIA/cuPhoton#125: Adds the xImage bundle and preprocessing contracts used by reconstruction evaluation in this change.
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Production evaluate_image_metrics CPU and CuPy measurements for the same pair of finite 2048×2048 synthetic FP64 images, covering FFT sharpness, robust sky dispersion, finite coverage, PSNR, and SSIM:
GPU
NumPy/SciPy CPU reference, ms
GPU including transfers, ms
Speedup
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
851.66
68.27
12.47×
NVIDIA GB10, system A
488.48
148.25
3.29×
NVIDIA GB10, system B
484.51
150.24
3.22×
The CPU reference requested one CPU thread through OMP_NUM_THREADS, OPENBLAS_NUM_THREADS, and MKL_NUM_THREADS; independent threadpool enforcement was unavailable. This is not an all-core CPU comparison. NumPy 2.4.6, SciPy 1.18.0, and CuPy 14.1.1 were used; CuPy reported CUDA runtime 13.2.
Values are medians of five measured runs after two warmups. GPU timing includes array upload, computation, and packed scalar-result download, with synchronization around each call. Input generation, file I/O, source detection, catalogs, and photometry are excluded. First calls share caches and are excluded; these are warm-call measurements.
All 24 case comparisons passed at rtol=1e-9, atol=1e-10, covering sizes 256, 1024, 2048, and 4096 with finite and partially missing pixels. The largest absolute difference among finite metric outputs was 2.665e-15; unavailable values matched. Across the four partially missing sizes, speedups were 3.91–14.99×, 2.02–2.94×, 1.96–2.92× respectively (table order). The 2048 inputs are byte-identical across systems; 4096 input hashes differ between CPU architectures, so those ranges are within-system results.
These structured, noisy fixtures do not bound FFT-sharpness differences for constant or nearly sparse spectra: the logarithm amplifies FFT roundoff near zero power. The unchanged metric formula and this limitation are documented. Parity checks metric definitions, not reconstruction quality.
The workstation measured source 7958207; GB10 runs used 782011a9. Evaluation source hashes match, and the intervening commit only updates CLI test counts. Each speedup compares CPU and GPU on the same system; platform differences prevent a pure GPU hardware ranking.
Subsequent CI-download and benchmark cache-release changes leave the measured preprocessing, reconstruction, and standalone metric implementations unchanged.
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Review comments at @src/cuphoton/ximage/_evaluation_cupy.py:
- Around line 94-99: Update compute_metrics in _evaluation_cupy.py to release
CuPy’s default memory-pool blocks after metric evaluation, including when
evaluation raises; keep the cleanup within this module and preserve the existing
returned metrics.
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Reviewing files that changed from the base of the PR and between 6286e07 and 7de20d2.
Files selected for processing: 9
docs/components/ximage.md
src/cuphoton/ximage/_evaluation_cupy.py
src/cuphoton/ximage/benchmark.py
src/cuphoton/ximage/commands.py
src/cuphoton/ximage/evaluation.py
tests/core/test_cli_contract.py
tests/ximage/test_benchmark.py
tests/ximage/test_cli.py
tests/ximage/test_evaluation.py
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The CuPy engine covers the generic metric set, the part of evaluation that is useful in any imaging domain. CuPy paths were not run (CPU only). If evaluation splits as suggested in #127, _evaluation_cupy.py would sit beside the generic metrics module.
Evaluate image statistics and finite-support similarity with CuPy.
Keep the CPU reference, select the engine through the API and CLI,
and report the metric backend separately from reconstruction.
Batch result transfers and document sparse-spectrum sensitivity.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Account for metric backend selection on the evaluation and benchmark
commands in the existing public CLI contract.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Return unused CuPy pool blocks after benchmark evaluation so later Torch
runs can use that device memory. Preserve warm-pool reuse for standalone
metric calls and keep CPU evaluation independent of CuPy.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
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Image evaluation can now use CUDA alongside reconstruction. This adds an optional CuPy engine beside
cuphoton.ximage.metricsfor FFT sharpness, background statistics, finite coverage, PSNR, and SSIM. It uses float64 computation and grouped scalar result transfers.evaluate-reconstructionandbenchmark-devices --evaluateaccept--metrics-backend auto|cpu|cupy; reports identify the metric engine separately from the reconstruction backend.CPU selection uses the NumPy/SciPy reference. Automatic selection falls back when GPU setup is unavailable. Benchmark evaluation releases unused CuPy cache blocks before later Torch iterations. Photometry and WCS alignment remain in the Rubin adapter on CPU.
Validation: the assembled stack passes 3,772 CPU tests (462 optional/GPU skips), all 33 xImage GPU tests without skips, repository hooks, and source packaging. Tests cover finite/missing data, empty support, constants, backend failures, CLI routing, and saved-run evaluation. The CuPy implementation is unchanged by its move beside the generic metrics. Existing production measurements were not rerun for this restack.
Stacked on #130. The guide documents how logarithmic Fourier sharpness can amplify FFT roundoff near zero spectral coefficients; select
cpufor comparisons with CPU baselines.