Merge pull request #20406 from MarkGHX:gsoc_2021_webnn

[GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Fix the build issue

* Update concat_layer.cpp

Still have some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Delete bib19450.aux

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Update dnn.cpp

* Fix Error in dnn.cpp

* Resolve duplication in conditions in convolution_layer.cpp

* Fixed the issues in the comments

* Fix building issue

* Update tutorial

* Fixed comments

* Address the comments

* Update CMakeLists.txt

* Offer more accurate perf test on native

* Add better perf tests for both native and web

* Modify per tests for better results

* Use more latest version of Electron

* Support latest WebNN Clamp op

* Add definition of HAVE_WEBNN macro

* Support group convolution

* Implement Scale_layer using WebNN

* Add Softmax option for native classification example

* Fix comments

* Fix comments

modules/dnn/src/layers/convolution_layer.cpp

@@ -47,6 +47,7 @@
 #include "../op_inf_engine.hpp"
 #include "../ie_ngraph.hpp"
 #include "../op_vkcom.hpp"
+#include "../op_webnn.hpp"
 
 #include <opencv2/core/utils/configuration.private.hpp>
 #include <opencv2/core/utils/logger.hpp>
@@ -80,6 +81,9 @@ class BaseConvolutionLayerImpl : public ConvolutionLayer
 public:
     bool fusedWeights, fusedBias;
     std::vector<double> weightsMultipliers;
+#ifdef HAVE_WEBNN
+    int groups;
+#endif
     BaseConvolutionLayerImpl(const LayerParams &params)
     {
         setParamsFrom(params);
@@ -87,6 +91,9 @@ public:
 
         numOutput = params.get<int>("num_output");
         int ngroups = params.get<int>("group", 1);
+#ifdef HAVE_WEBNN
+        groups = ngroups;
+#endif
         CV_Assert(numOutput % ngroups == 0);
 
         if (kernel_size.size() == 2) {
@@ -347,6 +354,17 @@ public:
 #ifdef HAVE_VULKAN
         if (backendId == DNN_BACKEND_VKCOM)
             return ksize == 2;
+#endif
+#ifdef HAVE_WEBNN
+        if (backendId == DNN_BACKEND_WEBNN)
+        {
+            if (ksize != 2)
+            {
+                CV_LOG_WARNING(NULL, "WebNN only supports Conv2d.");
+                return false;
+            }
+            return true;
+        }
 #endif
         return false;
     }
@@ -896,6 +914,108 @@ public:
     }
 #endif  // HAVE_DNN_NGRAPH
 
+#ifdef HAVE_WEBNN
+    virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
+    {
+        CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
+        Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
+        auto& webnnInpOperand = node->operand;
+        auto& webnnGraphBuilder = node->net->builder;
+        ml::Operand webnnWeights = nodes.size() > 1 ? nodes[1].dynamicCast<WebnnBackendNode>()->operand : nullptr;
+        if (nodes.size() > 1)
+            CV_Assert(webnnWeights);
+        const int inpCn = weightsMat.total()/(kernel_size[0]*kernel_size[1]*numOutput);
+        const int group = groups;
+        const int inpGroupCn = inpCn / group;
+        std::vector<int32_t> kernel_shape;
+        if (group != 1)
+        {
+            kernel_shape.push_back(group);
+        }
+        kernel_shape.push_back(numOutput / group);
+        kernel_shape.push_back(inpGroupCn);
+        std::copy(kernel_size.begin(), kernel_size.end(), back_inserter(kernel_shape));
+
+        if (nodes.size() == 1)
+        {
+            webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(blobs[0]), blobs[0].data, blobs[0].total()*blobs[0].elemSize(), ml::OperandType::Float32);
+            if (fusedWeights)
+            {
+                if (weightsMat.isContinuous())
+                {
+                    webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(weightsMat), weightsMat.data, weightsMat.total()*weightsMat.elemSize(), ml::OperandType::Float32);
+                }
+                else
+                {
+                    Mat newWeights;
+                    Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / numOutput);
+                    cvWeights.copyTo(newWeights);
+                    webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(newWeights), newWeights.data, newWeights.total()*newWeights.elemSize(), ml::OperandType::Float32);
+                }
+            }
+        }
+        else
+        {
+            webnnWeights  = webnnGraphBuilder.Reshape(webnnWeights, kernel_shape.data(), kernel_shape.size());
+        }
+
+        ml::AutoPad pad_type = ml::AutoPad::Explicit;
+        if (!padMode.empty())
+            pad_type = padMode == "VALID" ? ml::AutoPad::Explicit : ml::AutoPad::SameUpper;
+
+        ml::Conv2dOptions options = {};
+        options.groups = group;
+        options.autoPad = pad_type;
+        std::vector<int32_t> Strides(strides.begin(), strides.end());
+        if (!Strides.empty())
+        {
+            options.stridesCount = Strides.size();
+            options.strides = Strides.data();
+        }
+        std::vector<int32_t> Padding;
+        if (padMode.empty())
+        {
+            Padding = {static_cast<int32_t>(pads_begin[0]),
+                       static_cast<int32_t>(pads_end[0]),
+                       static_cast<int32_t>(pads_begin[1]),
+                       static_cast<int32_t>(pads_end[1])};
+        }
+        else if (padMode == "VALID")
+        {
+            Padding = {0, 0, 0, 0};
+        }
+        if (!Padding.empty())
+        {
+            options.paddingCount = Padding.size();
+            options.padding = Padding.data();
+        }
+        std::vector<int32_t> Dilations(dilations.begin(), dilations.end());
+        if (!Dilations.empty())
+        {
+            options.dilationsCount = Dilations.size();
+            options.dilations = Dilations.data();
+        }
+        ml::Operand operand = webnnGraphBuilder.Conv2d(webnnInpOperand, webnnWeights, &options);
+
+        // ml::Operand result = operand;
+        if (hasBias() || fusedBias || nodes.size() == 3)
+        {
+            ml::Operand webnnBias = nullptr;
+            if (nodes.size() == 3)
+            {
+                std::vector<int32_t> bias_shape = {1, numOutput, 1, 1};
+                webnnBias = webnnGraphBuilder.Reshape(nodes[2].dynamicCast<WebnnBackendNode>()->operand, bias_shape.data(), bias_shape.size());
+            }
+            else
+            {
+                webnnBias = webnn::BuildConstant(webnnGraphBuilder, {1, numOutput, 1, 1}, biasvec.data(), (numOutput) * sizeof(float), ml::OperandType::Float32);
+            }
+            operand = webnnGraphBuilder.Add(operand, webnnBias);
+        }
+        return Ptr<BackendNode>(new WebnnBackendNode(operand));
+    }
+#endif // HAVE_WEBNN
+
     class ParallelConv : public cv::ParallelLoopBody
     {
     public: