mmdetection
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[Fix] Use torch.initial_seed to guarantee the randomness of numpy
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Motivation
solve https://github.com/open-mmlab/mmdetection/issues/8110 numpy will be set the same seed in each epoch when pytorch < 1.8
I reproduce the same bug.
test code:
the results:
after modification:
Modification
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Checklist
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- The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
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Codecov Report
Attention: 3 lines
in your changes are missing coverage. Please review.
Comparison is base (
8f3a593
) 64.15% compared to head (89f3fb5
) 64.17%. Report is 118 commits behind head on dev.
Files | Patch % | Lines |
---|---|---|
mmdet/datasets/builder.py | 25.00% | 3 Missing :warning: |
Additional details and impacted files
@@ Coverage Diff @@
## dev #8142 +/- ##
==========================================
+ Coverage 64.15% 64.17% +0.01%
==========================================
Files 361 361
Lines 29525 29526 +1
Branches 5019 5019
==========================================
+ Hits 18942 18947 +5
+ Misses 9578 9576 -2
+ Partials 1005 1003 -2
Flag | Coverage Δ | |
---|---|---|
unittests | 64.14% <25.00%> (+0.01%) |
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Also invite @HAOCHENYE to see if this need to be migrated into MMEngine.
Also invite @HAOCHENYE to see if this need to be migrated into MMEngine.
Yes, worker_init_fn
in MMEngine
should also be updated.
BTW, it seems seed
is not used here anymore, is this expected?