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Merge pull request #26059 from Abdurrahheem:ash/fix-einsum-allocation

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Einsum buffer allocation fix #26059

This PR fixed buffer allocation issue in Einsum layer that causes segmentation fault on 32bit platforms. Related issue #26008 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Abduragim Shtanchaev 2024-08-23 16:25:48 +04:00 committed by GitHub
parent a3bdbf5553
commit e5b871fa7e
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GPG Key ID: B5690EEEBB952194
1 changed files with 11 additions and 5 deletions
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16
modules/dnn/src/layers/einsum_layer.cpp
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@ -459,6 +459,7 @@ public:
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_CheckEQ((size_t)inputs_arr.total(), (size_t)numInputs, "Number of inputs in forward and inputs during graph constructions do not match");
if (inputs_arr.depth() == CV_16F)
{
@ -541,7 +542,7 @@ public:
// Use either the preprocessed inputs (if it is available) or the corresponding raw inputs
result = pairwiseOperandProcess(!result.empty() ? result : rawInputs[0],
!result.empty() ? tmpResult : homogenizedInputDims[0],
!preProcessedInputs[input].empty() ? preProcessedInputs[input] : rawInputs[input],
(!preProcessedInputs[input].empty()) ? preProcessedInputs[input] : rawInputs[input],
homogenizedInputDims[input],
reducedDims,
isFinalPair);
@ -605,8 +606,8 @@ void LayerEinsumImpl::preProcessInputs(InputArrayOfArrays& inputs_arr)
std::vector<cv::Mat> inputs;
inputs_arr.getMatVector(inputs);
preProcessedInputs.reserve(inputs.size());
homogenizedInputDims.reserve(inputs.size());
preProcessedInputs.resize(inputs.size());
homogenizedInputDims.resize(inputs.size());
int inputIter = 0;
for(const Mat& input : inputs)
@ -615,6 +616,11 @@ void LayerEinsumImpl::preProcessInputs(InputArrayOfArrays& inputs_arr)
// variable to hold processed version of the original input
MatShape input_dims = shape(input);
if (input_dims.empty()){
homogenizedInputDims[inputIter] = MatShape(numLetterIndices, 1);
++inputIter;
continue;
}
const auto& currSubscriptIndices = inputSubscriptIndices[inputIter];
@ -667,9 +673,9 @@ void LayerEinsumImpl::preProcessInputs(InputArrayOfArrays& inputs_arr)
{
preprocessed = preprocessed.reshape(1, homogenizedInputDims_.size(), homogenizedInputDims_.data());
}
preProcessedInputs[inputIter] = preprocessed;
homogenizedInputDims[inputIter] = homogenizedInputDims_;
preProcessedInputs.emplace_back(preprocessed);
homogenizedInputDims.emplace_back(homogenizedInputDims_);
++inputIter;
}
}