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Patch dataset seems extremely inefficient in training #6585
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Hi @drbeh , Could you please help share some comments on this question? Thanks in advance. |
the gridpatchdataset works with a generator style input MONAI/monai/data/grid_dataset.py Lines 148 to 160 in 575954c
you can combine it with |
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Signed-off-by: KumoLiu <yunl@nvidia.com>
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Hello! Thank you for this incredible library! I'm loving my experience with MONAI so far.
Is your feature request related to a problem? Please describe.
But I've noticed a severe inefficiency in the way PatchDataset is written. If used with RandSpacialCropSamplesd, as it is in the example, it takes more time to sample the same number of patches, than if using RandSpacialCropd with a simple Dataset. That's due to how the patches are sampled: for every patch, first, all patches are resampled, then a patch is chosen with the corresponding index, while the other sampled patches are just thrown away. So, this creates two problems
Describe the solution you'd like
I wish PatchDataset would store a generator inside, which would yield patches. On StopIteration, one would store newly-sampled patches in the generator. Also, please add a flag, allowing to turn RandSpacialCropSamplesd into a generator, so that it wouldn't store all sampled patches in memory. It would be slower than storing all of them in memory, but it would greatly reduce the memory footprint (to the footprint of sampling a single patch).
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