Merge pull request #22809 from fengyuentau:tile

asmorkalov · web-flow · commit 421ba8730a1a · 2022-11-29T14:42:28.000+03:00
dnn: support ONNX Tile
diff --git a/modules/dnn/include/opencv2/dnn/all_layers.hpp b/modules/dnn/include/opencv2/dnn/all_layers.hpp
@@ -1079,6 +1079,12 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<ScatterNDLayer> create(const LayerParams& params);
     };
 
+    class CV_EXPORTS TileLayer : public Layer
+    {
+    public:
+        static Ptr<TileLayer> create(const LayerParams& params);
+    };
+
 //! @}
 //! @}
 CV__DNN_INLINE_NS_END
diff --git a/modules/dnn/src/init.cpp b/modules/dnn/src/init.cpp
@@ -183,6 +183,7 @@ void initializeLayerFactory()
 
     CV_DNN_REGISTER_LAYER_CLASS(Scatter,        ScatterLayer);
     CV_DNN_REGISTER_LAYER_CLASS(ScatterND,      ScatterNDLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Tile,           TileLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Quantize,         QuantizeLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Dequantize,       DequantizeLayer);
diff --git a/modules/dnn/src/layers/tile_layer.cpp b/modules/dnn/src/layers/tile_layer.cpp
@@ -0,0 +1,97 @@
+// 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 https://opencv.org/license.html.
+
+#include "../precomp.hpp"
+#include "layers_common.hpp"
+
+#include <opencv2/dnn/shape_utils.hpp>
+
+namespace cv { namespace dnn {
+
+class TileLayerImpl CV_FINAL : public TileLayer
+{
+public:
+    TileLayerImpl(const LayerParams& params)
+    {
+        setParamsFrom(params);
+        if (params.has("repeats"))
+        {
+            DictValue param_repeats = params.get("repeats");
+            int n_repeats = param_repeats.size();
+
+            CV_Assert(n_repeats > 0);
+            repeats.resize(n_repeats);
+            for (int i = 0; i < n_repeats; i++)
+                repeats[i] = param_repeats.get<int>(i);
+        }
+        else
+            CV_Error(Error::StsNotImplemented, "Tile: repeats needs to be treated as parameter but it is missing.");
+    }
+
+    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(), 1ull, "Tile: one input is expected");
+
+        // repeats must have the same length as input's dimension number
+        // FIXIT: it breaks when the input is 1d tensor (represented as 2d mat with size=2 in opencv dnn)
+        CV_CheckEQ(inputs[0].size(), repeats.size(), "Tile: repeats must be a 1D tensor of the same length as input's dimension number");
+
+        outputs.assign(1, inputs[0]);
+        for (int i = 0; i < repeats.size(); i++)
+        {
+            outputs[0][i] *= repeats[i];
+        }
+        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& data = inputs[0];
+        Mat& out = outputs[0];
+
+        Mat tmp = data.clone();
+        MatShape tmp_shape = shape(tmp);
+        MatShape out_shape = shape(out);
+        int rep_i, ndims = data.dims;
+        int dims = 1;
+        for (int i = 0; i < ndims; i++)
+        {
+            rep_i = repeats[i];
+            if (rep_i != 1)
+            {
+                tmp = tmp.reshape(0, dims);
+                tmp = cv::repeat(tmp, 1, rep_i);
+                dims *= out_shape[i];
+            }
+        }
+        tmp = tmp.reshape(0, out_shape);
+
+        tmp.copyTo(out);
+    }
+
+private:
+    std::vector<int> repeats;
+};
+
+Ptr<TileLayer> TileLayer::create(const LayerParams& params)
+{
+    return makePtr<TileLayerImpl>(params);
+}
+
+}} // namespace cv::dnn
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -189,6 +189,7 @@ class ONNXImporter
     void parseDepthToSpace         (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseRange                (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseScatter              (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
+    void parseTile                 (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseSimpleLayers         (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
 
     // Domain: com.microsoft
@@ -3190,6 +3191,82 @@ void ONNXImporter::parseScatter(LayerParams& layerParams, const opencv_onnx::Nod
     addLayer(layerParams, node_proto);
 }
 
+void ONNXImporter::parseTile(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
+{
+    // for Tile>1, only the case of 'repeats' being constant is supported.
+    // 'repeats' is treated as a parameter instead of an input to determine shape in pre-run.
+
+    CV_Assert(node_proto.input_size() == 2 || node_proto.input_size() == 3); // tile-1: 3 inputs, tile>1: 2 inputs
+    bool is_opset_1 = node_proto.input_size() == 3;
+
+    std::vector<size_t> const_input_idx;
+    for (size_t i = 0; i < node_proto.input_size(); ++i)
+        if (layer_id.find(node_proto.input(i)) == layer_id.end())
+            const_input_idx.push_back(i);
+
+    bool all_const = false;
+    if (const_input_idx.size() == node_proto.input_size()) // all inputs are constant
+    {
+        all_const = true;
+    }
+    else if ((const_input_idx.size() == 1 && const_input_idx[0] == 1) || // tile>1
+             (const_input_idx.size() == 2 && const_input_idx[0] == 1 && const_input_idx[1] == 2)) // tile-1
+    {
+        all_const = false;
+    }
+    else
+    {
+        if (!is_opset_1)
+            CV_Error(Error::StsNotImplemented, "ONNX/Tile: repeats being non-constant is not supported.");
+        else
+            CV_Error(Error::StsNotImplemented, "ONNX/Tile: tiles or axis being non-constant are not supported.");
+    }
+
+    int input0_dims = 1;
+    if (all_const)
+        input0_dims = getBlob(node_proto, 0).dims;
+    else
+        input0_dims = outShapes[node_proto.input(0)].size();
+
+    // repeats, treated as paramenter
+    std::vector<int> repeats_vec(input0_dims, 1);
+    Mat input1_blob = getBlob(node_proto, 1);
+    if (is_opset_1)
+    {
+        // input1 in tile-1: tiles, 1d tensor of shape [1]
+        CV_CheckEQ(input1_blob.total(), 1ull, "ONNX/Tile: tiles must be a 0D tensor or 1D tensor of shape [1].");
+        int tiles = input1_blob.at<int>(0);
+        // input2 in tile-1: axis, 1d tensor of shape [1]
+        Mat input2_blob = getBlob(node_proto, 2);
+        CV_CheckEQ(input2_blob.total(), 1ull, "ONNX/Tile: axis must be a 0D tensor or 1D tensor of shape [1].");
+        int axis = input2_blob.at<int>(0);
+        repeats_vec[axis] = tiles;
+    }
+    else
+    {
+        // input1 in tile>1: repeats
+        CV_CheckEQ(input1_blob.dims, 2, "ONNX/Tile: repeats must be a 1D tensor."); // 1D tensor is represented as a 2D Mat
+        for (int i = 0; i < input1_blob.total(); i++)
+            repeats_vec[i] = input1_blob.at<int>(i);
+    }
+    layerParams.set("repeats", DictValue::arrayInt(repeats_vec.data(), repeats_vec.size()));
+
+    if (all_const)
+    {
+        std::vector<Mat> inputs, output;
+        Mat input0_blob = getBlob(node_proto, 0);
+        inputs.push_back(input0_blob);
+        runLayer(layerParams, inputs, output);
+        CV_Assert(output.size() == 1);
+        addConstant(node_proto.output(0), output[0]);
+        return;
+    }
+    else
+    {
+        addLayer(layerParams, node_proto);
+    }
+}
+
 void ONNXImporter::parseSimpleLayers(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {
     bool is_all_input_const = true;
@@ -3891,6 +3968,7 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version)
     dispatch["CumSum"] = &ONNXImporter::parseCumSum;
     dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter::parseDepthToSpace;
     dispatch["ScatterElements"] = dispatch["Scatter"] = dispatch["ScatterND"] = &ONNXImporter::parseScatter;
+    dispatch["Tile"] = &ONNXImporter::parseTile;
 
     dispatch["Equal"] = dispatch["Greater"] = dispatch["Less"] = dispatch["Pow"] = dispatch["Add"] =
             dispatch["Sub"] = dispatch["Mul"] = dispatch["Div"] = dispatch["GreaterOrEqual"] =
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
@@ -2492,6 +2492,11 @@ TEST_P(Test_ONNX_layers, YOLOv7)
     normAssertDetections(refClassIds, refScores, refBoxes, keep_classIds, keep_confidences, keep_boxes);
 }
 
+TEST_P(Test_ONNX_layers, Tile)
+{
+    testONNXModels("tile", pb);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
 
 }} // namespace
