Validate tensor dimensions

When implementing AI model inference code, always validate tensor dimensions and shapes before manipulating them. Neural network outputs often have complex multi-dimensional structures that require careful handling. Adding explicit dimension checks prevents subtle runtime errors, improves code robustness, and makes debugging easier.

Before reshaping tensors or extracting specific values:

1. Check that the tensor has the expected number of dimensions
2. Verify tensor shapes match your expectations
3. Add defensive assertions for critical assumptions

Example:

// Before tensor manipulation, add validation
CV_CheckEQ(outs[0].dims, 3, "Expected 3D tensor output");
CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == nc + 4), true, "Invalid output shape");

// Then proceed with manipulation
vector<Mat> channels;
for (int i = 0; i < 3; ++i) {
    channels.push_back(Mat(output_transposed.size[1], output_transposed.size[2], CV_32F,
                           output_transposed.ptr<float>(i)));
}

For parameters derived from tensor shapes, consider whether they should be computed or passed as parameters based on the model’s architecture. Always document your assumptions about tensor shapes, especially when working with different AI model formats or versions.