[ONNX] Added Reduce ops for batch and channel

modules/dnn/src/onnx/onnx_importer.cpp

@@ -551,11 +551,36 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
 
                     CV_Assert(axes.size() <= inpShape.size() - 2);
                     std::vector<int> kernel_size(inpShape.size() - 2, 1);
-                    for (int i = 0; i < axes.size(); i++) {
-                        int axis = clamp(axes.get<int>(i), inpShape.size());
-                        CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
-                        kernel_size[axis - 2] = inpShape[axis];
+                    if (axes.size() == 1 && (clamp(axes.get<int>(0), inpShape.size()) <= 1))
+                    {
+                        int axis = clamp(axes.get<int>(0), inpShape.size());
+                        MatShape newShape = inpShape;
+                        newShape[axis + 1] = total(newShape, axis + 1);
+                        newShape.resize(axis + 2);
+                        newShape.insert(newShape.begin(), 2 - axis, 1);
+
+                        LayerParams reshapeLp;
+                        reshapeLp.type = "Reshape";
+                        reshapeLp.name = layerParams.name + "/reshape";
+                        CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
+                        reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], newShape.size()));
+
+                        node_proto.set_output(0, reshapeLp.name);
+                        addLayer(reshapeLp, node_proto);
+
+                        kernel_size.resize(2);
+                        kernel_size[0] = inpShape[axis];
+                        node_proto.set_input(0, node_proto.output(0));
+                    }
+                    else
+                    {
+                        for (int i = 0; i < axes.size(); i++) {
+                            int axis = clamp(axes.get<int>(i), inpShape.size());
+                            CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
+                            kernel_size[axis - 2] = inpShape[axis];
+                        }
                     }
+
                     LayerParams poolLp = layerParams;
                     poolLp.name = layerParams.name + "/avg";
                     CV_Assert(layer_id.find(poolLp.name) == layer_id.end());

modules/dnn/test/test_onnx_importer.cpp

@@ -267,9 +267,11 @@ TEST_P(Test_ONNX_layers, ReduceSum)
     testONNXModels("reduce_sum");
 }
 
-TEST_P(Test_ONNX_layers, ReduceMaxGlobal)
+TEST_P(Test_ONNX_layers, ReduceMax)
 {
     testONNXModels("reduce_max");
+    testONNXModels("reduce_max_axis_0");
+    testONNXModels("reduce_max_axis_1");
 }
 
 TEST_P(Test_ONNX_layers, Scale)