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Fixed Unexpectedly huge RAM consumption of parallel datamanager (#3060)

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* Reduced the memory usage by caching image after the datamanager process starts
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hyperzy 2024-04-11 14:56:37 -04:00 committed by GitHub
parent 4714ae75f7
commit 03bdeff2ad
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nerfstudio/data/datamanagers/parallel_datamanager.py
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@ -93,10 +93,10 @@ class DataProcessor(mp.Process): # type: ignore
self.exclude_batch_keys_from_device = self.dataset.exclude_batch_keys_from_device
self.pixel_sampler = pixel_sampler
self.ray_generator = RayGenerator(self.dataset.cameras)
self.cache_images()
def run(self):
"""Append out queue in parallel with ray bundles and batches."""
self.cache_images()
while True:
batch = self.pixel_sampler.sample(self.img_data)
ray_indices = batch["indices"]