Merge pull request #23475 from Abdurrahheem:lstm_fix_initialization

Fix ONNX parser for single-layer LSTM hidden and cell states #23475

### Fix ONNX parser for single-layer LSTM hidden and cell states

### 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 PR addresses #21118 [issue](https://github.com/opencv/opencv/issues/21118). The problem is that the ONNX parser is unable to read the hidden state and cell state for single-layer LSTMs. This PR fixes the issue by updating the parser to correctly read hidden and cell states.

modules/dnn/src/layers/recurrent_layers.cpp

@@ -173,9 +173,17 @@ public:
             CV_CheckEQ(Wh.rows, Wx.rows, "");
             CV_CheckEQ(Wh.rows, (1 + static_cast<int>(bidirectional))*4*Wh.cols, "");
             CV_CheckEQ(Wh.rows, (int)bias.total(), "");
-            CV_CheckEQ(hInternal.cols, Wh.cols, "");
-            CV_CheckEQ(hInternal.cols, cInternal.cols, "");
-            CV_CheckEQ(hInternal.rows, cInternal.rows, "");
+            // Only perform these checks if hInternal and cInternal are not empty matrices
+            // e.g. inputs are not given by a user
+            if(!hInternal.empty()){
+                CV_CheckEQ(hInternal.cols, Wh.cols, "");
+            }
+            if(!cInternal.empty()){
+                CV_CheckEQ(cInternal.cols, Wh.cols, "");
+            }
+            if (!hInternal.empty() && !cInternal.empty()){ //otherwise check in forward
+                CV_CheckEQ(hInternal.rows, cInternal.rows, "");
+            }
             CV_Assert(Wh.type() == Wx.type() && Wx.type() == bias.type());
 
             // Peephole weights.
@@ -266,7 +274,7 @@ public:
                          std::vector<MatShape> &internals) const CV_OVERRIDE
     {
         CV_Assert((!usePeephole && blobs.size() == 5) || (usePeephole && blobs.size() == 8));
-        CV_Assert(inputs.size() == 1);
+        CV_Assert((inputs.size() == 1 || inputs.size() == 3));
         const MatShape& inp0 = inputs[0];
 
         const Mat &Wh = blobs[0], &Wx = blobs[1];
@@ -326,7 +334,7 @@ public:
         inputs_arr.getMatVector(input);
 
         CV_Assert((!usePeephole && blobs.size() == 5) || (usePeephole && blobs.size() == 8));
-        CV_Assert(input.size() == 1);
+        CV_Assert((input.size() == 1 || input.size() == 3));
         const Mat& inp0 = input[0];
 
         Mat &Wh = blobs[0], &Wx = blobs[1];
@@ -383,8 +391,20 @@ public:
             Mat Wh = blobs[0];
             Mat Wx = blobs[1];
             Mat bias = blobs[2];
-            Mat h_0 = blobs[3];
-            Mat c_0 = blobs[4];
+
+            Mat h_0, c_0;
+            // Handle h_0 and c_0 based on input size
+            h_0 = (input.size() >= 2) ? input[1].reshape(1, input[1].size[0] * input[1].size[1]) : blobs[3];
+            c_0 = (input.size() == 3) ? input[2].reshape(1, input[2].size[0] * input[2].size[1]) : blobs[4];
+
+            // Perform checks if input size is 2 or 3
+            if (input.size() >= 2) {
+                CV_CheckEQ(h_0.cols, Wh.cols, "");
+                CV_CheckEQ(h_0.cols, c_0.cols, "");
+                CV_CheckEQ(h_0.rows, c_0.rows, "");
+            }
+
+
             Mat pI, pF, pO;
 
             Wh = Wh.rowRange(i * Wh.rows / numDirs, (i + 1) * Wh.rows / numDirs);

modules/dnn/src/onnx/onnx_importer.cpp

@@ -1539,10 +1539,17 @@ void transformBlobs(std::vector<Mat>& blobs)
 
     const int numHidden = Wh.size[2];
 
-    Mat h0 = blobs[3];
-    h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
-    Mat c0 = blobs[4];
-    c0 = c0.reshape(1, c0.size[0] * c0.size[1]);
+    Mat h0, c0;
+    // check weather input is dynamic or not: hx, cx are given by user.
+    // Resahpe if only they are given
+    if (!blobs[3].empty()){
+        h0 = blobs[3];
+        h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
+    }
+    if (!blobs[4].empty()){
+        c0 = blobs[4];
+        c0 = c0.reshape(1, c0.size[0] * c0.size[1]);
+    }
 
     b = b.reshape(1, b.size[0]);
     Mat bx = b.colRange(0, b.cols / 2);
@@ -1569,8 +1576,13 @@ void transformBlobs(std::vector<Mat>& blobs)
     blobs[0] = Wh;
     blobs[1] = Wx;
     blobs[2] = b.reshape(1, 1);
-    blobs[3] = h0;
-    blobs[4] = c0;
+
+    if (!blobs[3].empty()){
+        blobs[3] = h0;
+    }
+    if (!blobs[4].empty()){
+        blobs[4] = c0;
+    }
 
     if (blobs.size() == 5) {
         // so that future patch removing copies can leave all indexing as is
@@ -1601,8 +1613,15 @@ void ONNXImporter::lstm_extractConsts(LayerParams& layerParams, const opencv_onn
         Mat blob;
         if (idx < lstm_proto.input_size() && !lstm_proto.input(idx).empty())
         {
-            blob = getBlob(lstm_proto, idx);
-            CV_Assert(shape(blob) == blobShape);
+            if ((idx == 5 || idx == 6) && (constBlobs.find(lstm_proto.input(idx)) == constBlobs.end()))
+            {
+                blob = Mat();
+            }
+            else
+            {
+                blob = getBlob(lstm_proto, idx);
+                CV_Assert(shape(blob) == blobShape);
+            }
         }
         else
         {