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Fix augmentations and matplotlib #1179
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DocGarbanzo
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May 25, 2024
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* skip augmentations when list is empty
DocGarbanzo 7ec7211
* fix layout height for ui widgets in macos (i.e. platform darwin)
DocGarbanzo 1d29961
* pin pytest to 8.1 for non-backwards compatible change
DocGarbanzo 488fdc1
Fix test selection to correctly identify RPi hardware as 'aarch64' an…
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@DocGarbanzo I don't know that this means. Does this limit the size of the cache?
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In the previous PR, we have upgraded the CACHE_IMAGES config parameter to allow for 3 values, i.e NOCACHE, ARRAY and BINARY, please see here: https://github.com/autorope/donkeycar/pull/1173/files#diff-8a130617f883f549bf089e3ab0c46cabd4c3e4a69b50568c3b27ef73414e7c28. ARRAY represents the original setting where the whole image is cached as a
np.uint8
array (consuming about 58k of mem ~ 120 * 160 * 3). With BINARY we can now cache the image as binary jpeg encoded which uses only 4k of memory. This caching still provides significantly faster access to the image than re-reading it from disk, even with a fast M2 SSD. With that much smaller memory footprint we can now train >> 100k images with caching, speeding up the training on large datasets by a lot. In this line here, I just fixed a left-over from the previous PR and here, it only affects logging the caching status into the shell, because the default changed from the binaryTrue
to the enum/string'ARRAY'
.