add Gather implementation

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -301,6 +301,14 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<ArgLayer> create(const LayerParams& params);
     };
 
+    /** @brief Gather layer
+     */
+    class CV_EXPORTS GatherLayer : public Layer
+    {
+    public:
+        static Ptr<GatherLayer> create(const LayerParams& params);
+    };
+
     class CV_EXPORTS PoolingLayer : public Layer
     {
     public:

modules/dnn/src/init.cpp

@@ -147,6 +147,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(Const,          ConstLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Arg,            ArgLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Reciprocal,     ReciprocalLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Gather,         GatherLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Crop,           CropLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Eltwise,        EltwiseLayer);

modules/dnn/src/layers/gather_layer.cpp

@@ -0,0 +1,91 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "../precomp.hpp"
+#include "layers_common.hpp"
+
+
+namespace cv { namespace dnn {
+
+class GatherLayerImpl CV_FINAL : public GatherLayer
+{
+public:
+    GatherLayerImpl(const LayerParams& params)
+    {
+        setParamsFrom(params);
+        m_axis = params.get<int>("axis", 0);
+        m_real_ndims = params.get<int>("real_ndims", -1);
+    }
+
+    virtual bool supportBackend(int backendId) CV_OVERRIDE
+    {
+        return backendId == DNN_BACKEND_OPENCV;
+    }
+
+    virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
+                                 const int requiredOutputs,
+                                 std::vector<MatShape> &outputs,
+                                 std::vector<MatShape> &internals) const CV_OVERRIDE
+    {
+        CV_CheckEQ(inputs.size(), 2ull, "");
+        MatShape inpShape = inputs[0];
+        const int axis = normalize_axis(m_axis, inpShape);
+
+        inpShape.erase(inpShape.begin() + axis);
+        auto end = m_real_ndims == -1 ? inputs[1].end() : inputs[1].begin() + m_real_ndims;
+        inpShape.insert(inpShape.begin() + axis, inputs[1].begin(), end);
+
+        outputs.assign(1, inpShape);
+        return false;
+    }
+
+    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
+    {
+        CV_TRACE_FUNCTION();
+        CV_TRACE_ARG_VALUE(name, "name", name.c_str());
+
+        std::vector<Mat> inputs, outputs;
+        inputs_arr.getMatVector(inputs);
+        outputs_arr.getMatVector(outputs);
+
+        const Mat& inp = inputs[0];
+        const Mat& indices = inputs[1];
+        Mat& out = outputs[0];
+
+        const int axis = normalize_axis(m_axis, shape(inp));
+
+        const size_t outer_size = axis == 0 ? inp.total() : inp.step1(axis - 1);
+        const size_t outer_dims = inp.total() / outer_size;
+        const size_t inner_size = inp.step1(axis);
+
+        const float* idx = indices.ptr<const float>(); // TODO: change type to integer in the future.
+        const char* src = inp.ptr<const char>();
+        char* dst = out.ptr<char>();
+
+        const size_t es = inp.elemSize1();
+        for (size_t i = 0; i < outer_dims; ++i)
+        {
+            const size_t src_offset = i * outer_size;
+            for (size_t j = 0 ; j < indices.total(); ++j)
+            {
+                const size_t index = (static_cast<int>(idx[j]) + inp.size[axis]) % inp.size[axis];
+                const size_t new_offset = src_offset + index * inp.step1(axis);
+                std::memcpy(dst, src + new_offset * es, inner_size * es);
+                dst += inner_size * es;
+            }
+        }
+    }
+
+private:
+    // The axis to gather along
+    int m_axis;
+    int m_real_ndims;
+};
+
+Ptr<GatherLayer> GatherLayer::create(const LayerParams& params)
+{
+    return makePtr<GatherLayerImpl>(params);
+}
+
+}}  // namespace cv::dnn

modules/dnn/src/onnx/onnx_importer.cpp

@@ -2622,83 +2622,57 @@ void ONNXImporter::parseConstantFill(LayerParams& layerParams, const opencv_onnx
     addConstant(node_proto.output(0), tensor);
 }
 
-void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
+void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {
-    opencv_onnx::NodeProto node_proto = node_proto_;
+    CV_CheckEQ(node_proto.input_size(), 2, "");
-    CV_Assert(node_proto.input_size() == 2);
-    Mat indexMat = getBlob(node_proto, 1);
-    CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1);
-    int index = indexMat.at<int>(0);
-    int axis = layerParams.get<int>("axis", 0);
 
-    if ((constBlobs.find(node_proto.input(0)) != constBlobs.end()))
+    // TODO: get rid of the type conversions and 1-d/0-d special-casing when the time comes
+    if (layer_id.find(node_proto.input(1)) == layer_id.end())
     {
-        Mat input = getBlob(node_proto, 0);
+        int real_ndims = getBlobExtraInfo(node_proto.input(1)).real_ndims;
-        Mat out;
+        layerParams.set("real_ndims", real_ndims);
-        std::vector<cv::Range> ranges(input.dims, Range::all());
+        if (layer_id.find(node_proto.input(0)) == layer_id.end())
-        ranges[axis] = Range(index, index + 1);
-
-        out = input(ranges);
-        MatShape outShape = shape(out);
-        if (outShape.size() > 1)
         {
-            outShape.erase(outShape.begin() + axis);
+            std::vector<Mat> inputs, output;
-            out.reshape(0, outShape);
+
-        } else {
+            Mat input = getBlob(node_proto, 0);
-            out.dims = 1;
+            int input_real_ndims = input.dims;
+            int type = input.type();
+            input.convertTo(input, CV_32FC1);
+            inputs.push_back(input);
+
+            Mat indices = getBlob(node_proto, 1);
+            indices.convertTo(indices, CV_32FC1);
+            inputs.push_back(indices);
+
+            runLayer(layerParams, inputs, output);
+            output.back().convertTo(output.back(), type);
+            output.back().dims = std::max(input_real_ndims - real_ndims, 1);
+            addConstant(node_proto.output(0), output.back());
+            return;
         }
-        addConstant(node_proto.output(0), out);
-        return;
     }
-    else
+
+    for (int i = 0; i < node_proto.input_size(); ++i)
     {
-        IterShape_t shapeIt = outShapes.find(node_proto.input(0));
+        if (layer_id.find(node_proto.input(i)) == layer_id.end())
-        CV_Assert(shapeIt != outShapes.end());
-        MatShape inpShape = shapeIt->second;
-
-        LayerParams sliceLp;
-        sliceLp.type = "Slice";
-        sliceLp.name = inpShape.size() > 1 ? layerParams.name + "/slice" : layerParams.name;
-        std::vector<int> begin(inpShape.size(), 0);
-        std::vector<int> end(inpShape.size(), INT_MAX);
-        begin[axis] = index;
-        end[axis] = index + 1;
-
-        cv::dnn::DictValue paramBegin = cv::dnn::DictValue::arrayInt(begin.data(), begin.size());
-        cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end.data(), end.size());
-        sliceLp.set("begin", paramBegin);
-        sliceLp.set("end", paramEnd);
-        sliceLp.set("has_dynamic_shapes", hasDynamicShapes);
-
-        if (inpShape.size() > 1)
         {
-            opencv_onnx::NodeProto proto;
+            LayerParams constParams;
-            proto.add_input(node_proto.input(0));
+            constParams.name = node_proto.input(i);
-            proto.add_output(sliceLp.name);
+            constParams.type = "Const";
-            addLayer(sliceLp, proto);
+            Mat blob = getBlob(node_proto, i);
-
+            if (i == 1)
-            inpShape.erase(inpShape.begin() + axis);
-            layerParams.type = "Reshape";
-            layerParams.set("axis", 0);
-            layerParams.set("dim", DictValue::arrayInt(&inpShape[0], inpShape.size()));
-            if (hasDynamicShapes)
             {
-                std::vector<int> dynamicAxes;
+                blob.convertTo(blob, CV_32FC1);
-                std::vector<int> inputIndices;
-                for (int index = 0; index < inpShape.size(); ++index)
-                    dynamicAxes.push_back(index);
-                for (int index = 0; index < inpShape.size(); ++index)
-                    inputIndices.push_back(index);
-                layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size()));
-                layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size()));
             }
-            node_proto.set_input(0, sliceLp.name);
+            constParams.blobs.push_back(blob);
-        }
+
-        else
+            opencv_onnx::NodeProto proto;
-        {
+            proto.add_output(constParams.name);
-            layerParams = sliceLp;
+            addLayer(constParams, proto);
         }
     }
+
     addLayer(layerParams, node_proto);
 }
 

modules/dnn/test/test_onnx_importer.cpp

@@ -207,11 +207,16 @@ TEST_P(Test_ONNX_layers, Gather)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
-    testONNXModels("gather");
+    testONNXModels("gather", npy, 0, 0, false, false);
+    testONNXModels("gather_scalar", npy, 0, 0, false, false);
+}
+
+TEST_P(Test_ONNX_layers, GatherMulti)
+{
     // GPU plugin unsupported slice for constant
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
-    testONNXModels("gather_scalar", npy, 0, 0, false, false);
+    testONNXModels("gather_multi", npy, 0, 0, false, false);
 }
 
 TEST_P(Test_ONNX_layers, Convolution3D)
@@ -1424,7 +1429,7 @@ TEST_P(Test_ONNX_layers, GatherMultiOutput)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE);
 #endif
 
-    testONNXModels("gather_multi_output");
+    testONNXModels("gather_multi_output", npy, 0, 0, false, false);
 }
 
 TEST_P(Test_ONNX_layers, DynamicAxes_squeeze_and_conv)
@@ -1475,7 +1480,7 @@ TEST_P(Test_ONNX_layers, DynamicAxes_gather)
     }
 #endif
 #endif
-    testONNXModels("gather_dynamic_axes");
+    testONNXModels("gather_dynamic_axes", npy, 0, 0, false, false);
 }
 
 TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar)
@@ -1504,7 +1509,7 @@ TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar)
     }
 #endif
 #endif
-    testONNXModels("gather_scalar_dynamic_axes");
+    testONNXModels("gather_scalar_dynamic_axes", npy, 0, 0, false, false);
 }
 
 TEST_P(Test_ONNX_layers, DynamicAxes_slice)