Fix bug to RandomErasing #1095
Merged
Fix bug to RandomErasing #1095
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
1. Avoid forever loop for getting parameters of erase. 2. replace' img_b' by 'img_c', because it indicates the channel. 3. replace v = torch.rand([img_c, h, w]) by v = torch.empty([img_c, h, w], dtype=torch.float32).normal_(). Normally distributed achieves better performance.
Codecov Report
@@ Coverage Diff @@
## master #1095 +/- ##
=========================================
- Coverage 64.65% 64.6% -0.05%
=========================================
Files 68 68
Lines 5410 5411 +1
Branches 830 831 +1
=========================================
- Hits 3498 3496 -2
- Misses 1662 1664 +2
- Partials 250 251 +1
Continue to review full report at Codecov.
|
Closed
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR optimizes the RandomErasing:
img_bbyimg_c, because it indicates the channel.v = torch.rand([img_c, h, w])by
v = torch.empty([img_c, h, w], dtype=torch.float32).normal_(). Because normally distributed achieves better performance.