Add a denoising benchmark #1
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3 tasks
tachella
requested changes
Dec 12, 2025
| Abstract base class for benchmarks | ||
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| All of the benchmarks should inherit this class and implement the `run` method. | ||
| """ |
Contributor
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we should have dataset, metrics in the init here
| :param dinv.models.Denoiser | dinv.models.Reconstructor model: The model to benchmark | ||
| :param torch.device | str device: The device to run the benchmark on (default: `"cpu"`) | ||
| :return: (`Any`) The result of the benchmark | ||
| """ |
Contributor
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we could write the function here with deepinv.test and avoid re-writing the for-loop for each benchmark
| import benchmarks | ||
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| class DenoisingBenchmark(benchmarks.Benchmark): |
Contributor
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not sure we need to redefine a new class,
why not something like
Benchmark(dataset=dinv.datasets.Urban100HR("data/Urban100", download=True, transform=transforms.ToTensor()), device=device, )
| for model in models: | ||
| model_name = type(model).__name__ | ||
| psnr_avg, psnr_std = benchmark.run(model, device=device) | ||
| rows.append( |
Contributor
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we could move this logic to the base class since all benchmarks will use this
Andrewwango
reviewed
Dec 12, 2025
| @@ -0,0 +1 @@ | |||
| from .benchmark import Benchmark | |||
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🤔🤔could we have a config driven interface? :P
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