diff --git a/modules/dnn/src/graph_block_layout.cpp b/modules/dnn/src/graph_block_layout.cpp
index f304807236..e109adc9a7 100644
--- a/modules/dnn/src/graph_block_layout.cpp
+++ b/modules/dnn/src/graph_block_layout.cpp
@@ -166,6 +166,17 @@ struct BlockLayoutTransformer
             CV_Assert(inputLayoutsNew.size() == ninputs);
             CV_Assert(outputLayouts.size() == noutputs);
 
+            fprintf(stderr, "BLKALL op=%s name=%s\n", op_name.c_str(), name.c_str());
+            if (op_name == "Pooling" || op_name == "DequantizeLinear" || op_name == "QuantizeLinear") {
+                fprintf(stderr, "BLKDBG op=%s name=%s deviceOp=%d defaultLayout=%d orig=[", op_name.c_str(), name.c_str(), (int)deviceOp, (int)defaultLayout);
+                for (auto l : inputLayoutsOrig) fprintf(stderr, "%d,", (int)l);
+                fprintf(stderr, "] new=[");
+                for (auto l : inputLayoutsNew) fprintf(stderr, "%d,", (int)l);
+                fprintf(stderr, "] out=[");
+                for (auto l : outputLayouts) fprintf(stderr, "%d,", (int)l);
+                fprintf(stderr, "]\n");
+            }
+
             if (deviceOp) {
                 for (size_t i = 0; i < ninputs; i++)
                     inputLayoutsNew[i] = defaultLayout;
diff --git a/modules/dnn/src/layers/conv2_common.cpp b/modules/dnn/src/layers/conv2_common.cpp
index 89d5d3b052..876b185e23 100644
--- a/modules/dnn/src/layers/conv2_common.cpp
+++ b/modules/dnn/src/layers/conv2_common.cpp
@@ -396,7 +396,7 @@ MatShape deconvInferShape(const MatShape& inpShape, const MatShape& wshape,
                           const std::vector<int>& adjustPads,
                           AutoPadding autoPad)
 {
-    bool blockLayout = true;
+    bool blockLayout = (inpShape.layout == DATA_LAYOUT_BLOCK);
     int ndims = inpShape.dims;
     int nspatialdims = ndims - 2 - int(blockLayout);
     CV_Assert(nspatialdims >= 1);
@@ -413,9 +413,13 @@ MatShape deconvInferShape(const MatShape& inpShape, const MatShape& wshape,
             kshape_[i] = wshape[i + 2];
     }
 
-    int C0 = inpShape[ndims - 1];
     int K_out = ngroups * wshape[1];
-    outshape[1] = (K_out + C0 - 1) / C0;
+    if (blockLayout) {
+        int C0 = inpShape[ndims - 1];
+        outshape[1] = (K_out + C0 - 1) / C0;
+    } else {
+        outshape[1] = K_out;
+    }
 
     CV_Assert(strides.empty() || (int)strides.size() == nspatialdims);
     CV_Assert(dilations.empty() || (int)dilations.size() == nspatialdims);
@@ -439,7 +443,7 @@ MatShape deconvInferShape(const MatShape& inpShape, const MatShape& wshape,
         }
         outshape[i + 2] = outsz;
     }
-    outshape.C = K_out;
+    outshape.C = blockLayout ? K_out : 0;
     return outshape;
 }
 
diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp
index 1f3932dc7b..2757b8513e 100644
--- a/modules/dnn/src/layers/pooling_layer.cpp
+++ b/modules/dnn/src/layers/pooling_layer.cpp
@@ -1180,6 +1180,10 @@ public:
     {
         CV_Assert(inputs.size() != 0);
 
+        fprintf(stderr, "DBG Pooling '%s' inp0 dims=%d layout=%d global=%d kernel=%zu pads_begin=%zu shape=[", name.c_str(), (int)inputs[0].size(), (int)inputs[0].layout, (int)globalPooling, kernel_size.size(), pads_begin.size());
+        for (size_t _i=0;_i<inputs[0].size();_i++) fprintf(stderr, "%d,", inputs[0][_i]);
+        fprintf(stderr, "]\n");
+
         bool isPool1D = inputs[0].size() == 3;
         std::vector<int> inpShape(inputs[0].begin() + 2, inputs[0].end());
         std::vector<int> outShape(inputs[0].begin(), inputs[0].begin() + 2);
diff --git a/modules/dnn/test/test_model.cpp b/modules/dnn/test/test_model.cpp
index 47935ae7bd..5d71d518c7 100644
--- a/modules/dnn/test/test_model.cpp
+++ b/modules/dnn/test/test_model.cpp
@@ -776,7 +776,7 @@ TEST_P(Reproducibility_ResNet50_ONNX, Accuracy)
         timeMax = std::max(timeMax, t);
     }
 
-    std::cout << "[ BENCHMARK ] ResNet50 ONNX (backend=" << backendId << ", target=" << targetId << ") over "
+    std::cout << "[ BENCHMARK ] ResNet50 ONNX (backend=" << (int)backendId << ", target=" << (int)targetId << ") over "
               << numRuns << " runs: avg=" << (timeSum / numRuns) << " ms"
               << ", min=" << timeMin << " ms"
               << ", max=" << timeMax << " ms" << std::endl;