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

Einsum buffer allocation fix opencv#26059

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

Author: Abdurrahheem

modules/dnn/src/layers/einsum_layer.cpp

@@ -459,6 +459,7 @@ class LayerEinsumImpl CV_FINAL : public EinsumLayer
     {
         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 @@ class LayerEinsumImpl CV_FINAL : public EinsumLayer
                 // 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;
     }
 }