add DIV support to EltwiseOp

modules/dnn/src/cuda/eltwise_ops.cu

@@ -102,6 +102,26 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
                 v_store(output_vPtr[i], vec_x);
             }
         }
+
+        template <class T, std::size_t N>
+        __global__ void eltwise_div_2_vec(Span<T> output, View<T> x, View<T> y) {
+            using vector_type = get_vector_type_t<T, N>;
+
+            auto output_vPtr = vector_type::get_pointer(output.data());
+            auto x_vPtr = vector_type::get_pointer(x.data());
+            auto y_vPtr = vector_type::get_pointer(y.data());
+
+            for (auto i : grid_stride_range(output.size() / vector_type::size())) {
+                vector_type vec_x, vec_y;
+                v_load(vec_x, x_vPtr[i]);
+                v_load(vec_y, y_vPtr[i]);
+
+                for (int j = 0; j < vector_type::size(); j++)
+                    vec_x.data[j] = vec_x.data[j] / vec_y.data[j];
+
+                v_store(output_vPtr[i], vec_x);
+            }
+        }
     }
 
     template <class T, std::size_t N>
@@ -221,4 +241,32 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
     template void eltwise_prod_2(const Stream& stream, Span<__half> output, View<__half> x, View<__half> y);
     template void eltwise_prod_2(const Stream& stream, Span<float> output, View<float> x, View<float> y);
 
+    template <class T, std::size_t N>
+    void launch_vectorized_eltwise_div_2(const Stream& stream, Span<T> output, View<T> x, View<T> y) {
+        CV_Assert(is_fully_aligned<T>(output, N));
+        CV_Assert(is_fully_aligned<T>(x, N));
+        CV_Assert(is_fully_aligned<T>(y, N));
+
+        auto kernel = raw::eltwise_div_2_vec<T, N>;
+        auto policy = make_policy(kernel, output.size() / N, 0, stream);
+        launch_kernel(kernel, policy, output, x, y);
+    }
+
+    template <class T>
+    void eltwise_div_2(const Stream& stream, Span<T> output, View<T> x, View<T> y) {
+        CV_Assert(x.size() == y.size());
+        CV_Assert(x.size() == output.size());
+
+        if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(x, 4) && is_fully_aligned<T>(y, 4)) {
+            launch_vectorized_eltwise_div_2<T, 4>(stream, output, x, y);
+        } else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(x, 2) && is_fully_aligned<T>(y, 2)) {
+            launch_vectorized_eltwise_div_2<T, 2>(stream, output, x, y);
+        } else {
+            launch_vectorized_eltwise_div_2<T, 1>(stream, output, x, y);
+        }
+    }
+
+    template void eltwise_div_2(const Stream& stream, Span<__half> output, View<__half> x, View<__half> y);
+    template void eltwise_div_2(const Stream& stream, Span<float> output, View<float> x, View<float> y);
+
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */

modules/dnn/src/cuda4dnn/kernels/eltwise_ops.hpp

@@ -24,6 +24,9 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
     template <class T>
     void eltwise_prod_2(const csl::Stream& stream, csl::Span<T> output, csl::View<T> x, csl::View<T> y);
 
+    template <class T>
+    void eltwise_div_2(const csl::Stream& stream, csl::Span<T> output, csl::View<T> x, csl::View<T> y);
+
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */
 
 #endif /* OPENCV_DNN_SRC_CUDA4DNN_KERNELS_ELTWISE_OPS_HPP */

modules/dnn/src/cuda4dnn/primitives/eltwise.hpp

@@ -24,7 +24,8 @@ namespace cv { namespace dnn { namespace cuda4dnn {
     enum class EltwiseOpType {
         MAX,
         SUM,
-        PRODUCT
+        PRODUCT,
+        DIV
     };
 
     template <class T>
@@ -64,6 +65,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
                 {
                 case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, input_x, input_y); break;
                 case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, input_x, input_y); break;
+                case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, input_x, input_y); break;
                 case EltwiseOpType::SUM:
                     if (coeffs.empty() || (coeffs[0] == 1 && coeffs[1] == 1))
                         kernels::eltwise_sum_2<T>(stream, output, input_x, input_y);
@@ -89,6 +91,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
                     {
                     case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, output, input); break;
                     case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, output, input); break;
+                    case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, output, input); break;
                     case EltwiseOpType::SUM:
                         if (coeffs.empty() || coeffs[i] == 1)
                             kernels::eltwise_sum_2<T>(stream, output, output, input);

modules/dnn/src/layers/eltwise_layer.cpp

@@ -108,7 +108,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               (backendId == DNN_BACKEND_CUDA && op != DIV) ||  // TODO: not implemented, see PR #15811
+               backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && op != DIV) ||  // TODO: not implemented, see PR #15811
                ((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
                 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !variableChannels));
@@ -471,6 +471,7 @@ public:
             case MAX: return cuda4dnn::EltwiseOpType::MAX;
             case SUM: return cuda4dnn::EltwiseOpType::SUM;
             case PROD: return cuda4dnn::EltwiseOpType::PRODUCT;
+            case DIV: return cuda4dnn::EltwiseOpType::DIV;
             }
             return cuda4dnn::EltwiseOpType::SUM;
         }();

modules/dnn/test/test_onnx_importer.cpp

@@ -380,6 +380,7 @@ TEST_P(Test_ONNX_layers, Div)
 
     normAssert(ref, out, "", default_l1,  default_lInf);
     expectNoFallbacksFromIE(net);
+    expectNoFallbacksFromCUDA(net);
 }
 
 TEST_P(Test_ONNX_layers, DynamicReshape)