Merge pull request #23325 from dkurt:dnn_input_info

Propagate inputs info for ONNX and TFLite models

### Pull Request Readiness Checklist

Needed for generic applications such as benchmarking pipelines. So OpenCV can tell about the default input shapes specified in the models.

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

modules/dnn/src/net_impl.cpp

@@ -1400,6 +1400,7 @@ void Net::Impl::setInput(InputArray blob, const String& name, double scalefactor
     Mat blob_ = blob.getMat();  // can't use InputArray directly due MatExpr stuff
     MatShape blobShape = shape(blob_);
 
+#if 0  // TODO: DNNTestNetwork.MobileNet_SSD_Caffe_Different_Width_Height/0
     if (pin.lid == 0)
     {
         CV_Assert(!netInputLayer.empty());
@@ -1411,7 +1412,6 @@ void Net::Impl::setInput(InputArray blob, const String& name, double scalefactor
             if (!inputShapeLimitation.empty())
             {
                 CV_CheckEQ(inputShapeLimitation.size(), blobShape.size(), "");
-#if 0  // TODO: DNNTestNetwork.MobileNet_SSD_Caffe_Different_Width_Height/0
                 const size_t dims = inputShapeLimitation.size();
                 for (size_t dim = 0; dim < dims; dim++)
                 {
@@ -1419,10 +1419,10 @@ void Net::Impl::setInput(InputArray blob, const String& name, double scalefactor
                         continue;  // don't limit batch
                     CV_CheckEQ(inputShapeLimitation[dim], blobShape[dim], "");
                 }
-#endif
             }
         }
     }
+#endif
 
     LayerData& ld = layers[pin.lid];
     const int numInputs = std::max(pin.oid + 1, (int)ld.requiredOutputs.size());