potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 08:13:04 +04:00
Code Packages Releases Wiki Activity

Fix typos in Einsum layer and G-API docs

Browse Source
This commit is contained in:
Kavyansh Tyagi
2025-09-09 23:21:14 +05:30
parent efea09120b
commit eed87abbcf
2 changed files with 33 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/einsum_layer.cpp
+30 -30
View File
@@ -231,7 +231,7 @@ Mat Diagonal(const Mat& input, int dim1, int dim2)
}
}
// Permutate the input so that the dims from which we need the diagonal forms the innermost dims
// Permute the input so that the dims from which we need the diagonal form the innermost dims
Mat transposed = Transpose(input, input_dims, permutation);
// Parse the diagonal from the innermost dims
@@ -245,7 +245,7 @@ Mat Diagonal(const Mat& input, int dim1, int dim2)
reverse_permutation[perm] = iter++;
}
// Permutate using the reverse permutation to get back the original axes ordering
// Permute using the reverse permutation to get back the original axes ordering
// (Pass in CPU Transpose function here as this Diagonal method will only be used for CPU based diagonal parsing)
output = Transpose(output, shape(output), reverse_permutation);
} else {
@@ -298,19 +298,19 @@ public:
// Preprocessed inputs
std::vector<Mat> preProcessedInputs;
// This is container for preporcessed inputs
// This container holds preprocessed inputs
std::vector<MatShape> homogenizedInputDims;
// Collect outpus dimentions
MatShape einsumOutDims; // vector to store output dimentions
// Collect output dimensions
MatShape einsumOutDims; // vector to store output dimensions
// These hold equation subring, left hand side and right it of
// These hold equation substrings: the left-hand side, right-hand side, and the full equation
String lhs_eq, rhs_eq, equation;
// Holds token from left hand side of the equation
// Holds tokens from the left-hand side of the equation
std::vector<String> lhs_eq_tokens;
// Idicates if equation substring is defined in explit way such as "ij, jk->ik"
// Indicates if the equation substring is defined in an explicit way such as "ij, jk->ik"
// as opposed to "ij->"
bool explicitEquation = false;
@@ -329,14 +329,14 @@ public:
// A value of -1 means the corresponding subscript index is not found in the output
std::vector<int> subscriptIndicesToOutputIndices;
// Hold max number of alphabetic numbers
// Holds the max number of alphabetic characters
static const size_t numOfLetters = 52;
// Stores the count corresponding to each letter encountered
// A value of `0` indicates that the corresponding letter hasn't been seen at all
std::array<int, numOfLetters> letter2count;
// Hold the assigned index corresponding to the letter seen
// Holds the assigned index corresponding to the letter seen
// `-1` means the corresponding letter wasn't seen at all
std::array<int, numOfLetters> letter2index;
@@ -359,7 +359,7 @@ public:
void calculateOutputShape();
void preProcessInputs(InputArrayOfArrays& inputs);
Mat reduceSum(Mat& src, MatShape& reduceAxis);
Mat FinalizeOutput(const Mat& candidateOuput, const MatShape& ordered_subscript_indices_in_candidate);
Mat FinalizeOutput(const Mat& candidateOutput, const MatShape& ordered_subscript_indices_in_candidate);
Mat pairwiseOperandProcess(
const Mat& left,
const MatShape& leftShapeOverride,
@@ -410,12 +410,12 @@ public:
letter2count.fill(0);
letter2index.fill(-1);
// parser equation and extract tokens from the equation
// save token to lhs_eq_tokens variable
// parse equation and extract tokens from the equation
// save tokens to lhs_eq_tokens vector
parseEquation(equation); // TODO: return lhs_eq_tokens
// Start preprocessing related to equation parsing
// and dimention broadcasting
// and dimension broadcasting
processEquation(einsumInpShapes);
processBroadcastedDims();
@@ -429,7 +429,7 @@ public:
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
// getMeoryShapes
// getMemoryShapes
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@@ -439,7 +439,7 @@ public:
// check if passed and parsed inputs match up in number and dimensions
CV_CheckEQ(static_cast<int>(inputs.size()), numInputs,
"Number of inputs in forward and inputs during graph constructions do not match");
"Number of inputs in forward and inputs during graph construction do not match");
for (int i = 0; i < numInputs; i++)
{
if (inputs[i] != einsumInpShapes[i])
@@ -459,7 +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");
CV_CheckEQ((size_t)inputs_arr.total(), (size_t)numInputs, "Number of inputs in forward and inputs during graph construction do not match");
if (inputs_arr.depth() == CV_16F)
{
@@ -549,14 +549,14 @@ public:
}
}
// check of product of output dimentions and computed output dimentions match
// check that product of output dimensions and computed output dimensions match
size_t reqProd = std::accumulate(einsumOutDims.begin(), einsumOutDims.end(), 1, std::multiplies<int>());
MatShape realOutputDims = shape(result);
size_t realProd = std::accumulate(realOutputDims.begin(), realOutputDims.end(), 1, std::multiplies<int>());
CV_CheckEQ(reqProd, realProd, "Real output can not be shaped in to required output");
// reduce dimentions
// reduce dimensions
result = result.reshape(1, einsumOutDims.size(), einsumOutDims.data());
result.copyTo(outputs[0]);
} // forward
@@ -633,20 +633,20 @@ void LayerEinsumImpl::preProcessInputs(InputArrayOfArrays& inputs_arr)
// same axes order
MatShape homogenizedInputDims_(numLetterIndices, 1);
int dimIndexInIreprocessedInput = 0;
int dimIndexInPreprocessedInput = 0;
int dimIndexInOriginalInput = 0;
for (const auto& subscriptIndex : currSubscriptIndices)
{
if(subscriptIndicesToInputIndex[subscriptIndex] == -1){
subscriptIndicesToInputIndex[subscriptIndex] = dimIndexInIreprocessedInput++;
subscriptIndicesToInputIndex[subscriptIndex] = dimIndexInPreprocessedInput++;
homogenizedInputDims_[subscriptIndex] = input_dims[dimIndexInOriginalInput];
} else {
// Call diagonal
preprocessed = Diagonal(
!preprocessed.empty() ? preprocessed : inputs[inputIter],
subscriptIndicesToInputIndex[subscriptIndex],
dimIndexInIreprocessedInput);
dimIndexInPreprocessedInput);
}
++dimIndexInOriginalInput;
}
@@ -689,7 +689,7 @@ void LayerEinsumImpl::parseEquation(String equation)
std::size_t arrow_idx = equation.find("->");
if (arrow_idx != std::string::npos)
{
// split left and righ hand sides of the equation
// split left- and right-hand sides of the equation
lhs_eq = equation.substr(0, arrow_idx);
rhs_eq = equation.substr(arrow_idx + 2);
explicitEquation = true;
@@ -746,7 +746,7 @@ void LayerEinsumImpl::calculateOutputShape()
CV_CheckNE(letterIndex, -1,
"The only permissible subscript labels are lowercase letters (a-z) and uppercase letters (A-Z).");
CV_CheckEQ(outputLetterToCount[letterIndex], 0,
"Output subscript constains repeated letters");
"Output subscript contains repeated letters");
++outputLetterToCount[letterIndex];
auto mappedIndex = letter2index[letterIndex];
@@ -754,7 +754,7 @@ void LayerEinsumImpl::calculateOutputShape()
CV_CheckNE(mappedIndex, -1,
"Output subscript has letters that were not encountered in the inputs");
// Push output dimention
// Push output dimension
// Einsum layer only has one output vector
einsumOutDims.emplace_back(subscriptIndicesToDimValue[mappedIndex]);
@@ -778,7 +778,7 @@ void LayerEinsumImpl::validateOutputSubscript()
if(rhs_eq.find("...") == std::string::npos)
{
CV_Error(Error::StsError,
"Provided output subscript does not include ellipsis while Inputs subscrits constain ellipsis");
"Provided output subscript does not include ellipsis while input subscripts contain ellipsis");
}
}
}
@@ -973,7 +973,7 @@ void LayerEinsumImpl::processEquation(const std::vector<MatShape>& inputs)
CV_Error(Error::StsError, cv::format("Einsum operands can not be broadcasted."
"Check input shapes/equation passed."
"Input shape of operand [%d]", inputIdx) +
cv::format(" is incompatible in the dimention [%zu].", static_cast<size_t>(dim_count)));
cv::format(" is incompatible in the dimension [%zu].", static_cast<size_t>(dim_count)));
}
}
}
@@ -1079,7 +1079,7 @@ Mat LayerEinsumImpl::pairwiseOperandProcess(
Mat currentLeft;
Mat currentRight;
CV_CheckEQ(leftRank, rightRank, "Raks of pair-wise operands must be equal");
CV_CheckEQ(leftRank, rightRank, "Ranks of pair-wise operands must be equal");
// Following vectors hold:
// lro: dim indices that are present in left, right, and reduce_dims
@@ -1158,7 +1158,7 @@ Mat LayerEinsumImpl::pairwiseOperandProcess(
}
// Permutate the left operand so that the axes order go like this: [lro, lo, reduce_dims, ro]
// Permute the left operand so that the axes order go like this: [lro, lo, reduce_dims, ro]
MatShape reshaped_dims;
std::vector<size_t> left_permutation;
left_permutation.reserve(lro.size() + lo.size() + reduceDims.size() + ro.size());
@@ -1191,7 +1191,7 @@ Mat LayerEinsumImpl::pairwiseOperandProcess(
}
}
// Permutate the right operand so that the axes order go like this: [lro, reduce_dims, ro, lo]
// Permute the right operand so that the axes order go like this: [lro, reduce_dims, ro, lo]
std::vector<size_t> right_permutation;
right_permutation.reserve(lro.size() + lo.size() + reduceDims.size() + ro.size());
right_permutation.insert(right_permutation.end(), lro.begin(), lro.end());