This repository has been archived by the owner on Oct 9, 2023. It is now read-only.
Load Numpy arrays of incompatible dtypes
#1507
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souravraha
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ethanwharris,
Borda,
tchaton,
justusschock and
krshrimali
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for more information, see https://pre-commit.ci
Borda
changed the title
dtypes
Borda
reviewed
Jan 9, 2023
Signed-off-by: souravraha <[email protected]>
for more information, see https://pre-commit.ci
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The old contribution is discarded, as it was failing a required test. Now, the pixel values are scaled linearly to lie in between 0 and 255. This ensures minimal losses while loading an ndarray of dtype int64 to a PIL image. The downside for this is that every ndarray would get scaled differently, i.e. this is a sample specific scaling (rather than, say a global scaling for samples of the entire dataset). |
Borda
suggested changes
May 5, 2023
Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## master #1507 +/- ##
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- Coverage 85% 83% -3%
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Files 291 291
Lines 12852 12856 +4
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- Hits 10982 10609 -373
- Misses 1870 2247 +377 |
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What does this PR do?
Handles the loading of
.npyimages of integer data-types better. Previously, the ndarrays would be unsafely cast into'uint8'. If the original dtype were, say'int64', the loaded PIL Image would be different from what was intended.Now it should throw up a warning whenever there is unsafe casting. In addition, it would attempt to load a float ndarray (instead of possibly an integer array). The latter is needed as PIL can readily import from float64 ndarrays (but not, say int64 ndarrays).
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