Fix inconsistent benchmarking throughput/time #221
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samet-akcay
reviewed
Apr 11, 2022
tools/benchmarking/benchmark.py
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| @@ -218,6 +256,8 @@ def sweep(run_config: Union[DictConfig, ListConfig], device: int = 0, seed: int | |||
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| # Run benchmarking for current config | |||
| model_metrics = get_single_model_metrics(model_config=model_config, openvino_metrics=convert_openvino) | |||
| print(model_config.model.name, model_config.dataset.category) | |||
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What if we want to run benchmark.py for custom datasets that don't have category field?
tools/benchmarking/benchmark.py
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| logging.getLogger("pytorch_lightning").setLevel(logging.ERROR) | ||
| logging.getLogger("torchmetrics").setLevel(logging.ERROR) | ||
| logging.getLogger("os").setLevel(logging.ERROR) |
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maybe we could set the level in a for loop to avoid the duplicated lines
samet-akcay
requested changes
Apr 12, 2022
samet-akcay
approved these changes
Apr 12, 2022
djdameln
reviewed
Apr 12, 2022
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This works, but there will still be inconsistency in the throughput numbers between a normal training run and the benchmarking script. Maybe to avoid confusion, you could also add the batch size to the output in the timer callback Something like Throughput (batch_size=16): 12 FPS
djdameln
approved these changes
Apr 12, 2022
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Description
Supress outputs from model and print values from benchmarking script
Fixes Error while running benchmark.py on GPU #185
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