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Fix "same" padding torch issue #20270

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sachinprasadhs
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This fixes the issue for all torch layers when used with padding="same" and data_format="channels_first".

This was not caught in any of the test case due to the reason that it only happens with the above condition and when the padding_size is obtained from _compute_padding_length is something like ((0,0), (1, 1))

Fixes: #20235

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codecov-commenter commented Sep 18, 2024

Codecov Report

Attention: Patch coverage is 20.00000% with 4 lines in your changes missing coverage. Please review.

Project coverage is 75.78%. Comparing base (7628a18) to head (b5e23e2).
Report is 208 commits behind head on master.

Files with missing lines Patch % Lines
keras/src/backend/torch/nn.py 20.00% 4 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##           master   #20270      +/-   ##
==========================================
+ Coverage   66.34%   75.78%   +9.44%     
==========================================
  Files         508      527      +19     
  Lines       48264    60532   +12268     
  Branches     8882    12130    +3248     
==========================================
+ Hits        32020    45874   +13854     
+ Misses      14425    12609    -1816     
- Partials     1819     2049     +230     
Flag Coverage Δ
keras 75.68% <20.00%> (+9.34%) ⬆️
keras-jax 64.27% <20.00%> (?)
keras-numpy 59.80% <20.00%> (?)
keras-tensorflow 65.49% <20.00%> (-0.85%) ⬇️
keras.applications ?
keras.applications-jax ?
keras.applications-numpy ?
keras.applications-tensorflow ?
keras.applications-torch ?

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@fchollet
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Thanks, Sachin. It looks like relevant torch tests are failing, can you take a look? https://github.com/keras-team/keras/actions/runs/10931265787/job/30345878233?pr=20270

@gbaned gbaned requested review from fchollet and removed request for fchollet November 28, 2024 09:25
@fchollet fchollet closed this Nov 28, 2024
@fchollet fchollet reopened this Nov 28, 2024
@fchollet
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@sachinprasadhs can you please take a look at the torch test failure?

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Inconsistent execution results by the PyTorch backend
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