opencv/modules/dnn/src/layers/expand_layer.cpp

// 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 "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace cv { namespace dnn {

class ExpandLayerImpl CV_FINAL : public ExpandLayer
{
public:
    ExpandLayerImpl(const LayerParams &params) {
        setParamsFrom(params);

        // shape as param
        CV_CheckTrue(params.has("shape"), "DNN/Expand: shape is required in Expand layer initialization");
        DictValue param_shape = params.get("shape");
        int ndims_shape = param_shape.size();
        CV_CheckGT(ndims_shape, 0, "DNN/Expand: ndims of shape must be > 0");
        target_shape.resize(ndims_shape);
        for (int i = 0; i < ndims_shape; i++) {
            target_shape[i] = param_shape.get<int>(i);
        }

        // FIXME: remove when 0d/1d mat is available
        const_input_1d = params.get("const_input_1d", false);
    }

    virtual bool supportBackend(int backendId) CV_OVERRIDE
    {
        return backendId == DNN_BACKEND_OPENCV ||
               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
    }

    virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
                                 const int requiredOutputs,
                                 std::vector<MatShape> &outputs,
                                 std::vector<MatShape> &internals) const CV_OVERRIDE {
        CV_CheckGE(inputs.size(), static_cast<size_t>(1), "DNN/Expand: one input at least");
        CV_CheckLE(inputs.size(), static_cast<size_t>(2), "DNN/Expand: two input at most");
        CV_CheckFalse(target_shape.empty(), "DNN/Expand: shape must known before memory is set");

        MatShape input_shape = inputs[0]; // 1d tensor is represented as 2d mat, e.g. [3] -> [3, 1]
        if (const_input_1d) {
            input_shape = {inputs[0][0]};
        }

        auto& moreDimension = input_shape.size() > target_shape.size() ? input_shape : target_shape;
        auto& lessDimension = input_shape.size() <= target_shape.size() ? input_shape : target_shape;

        /*  Example:
                             i = 3
                               |
            moreDimension: 1 2 3 4 5, assign non-aligned dimensions to output shape
            lessDimension:     1 1 5, when dimension is aligned, check valid dimension (either equal or one of them is 1) and assign bigger one
                               |
                             j = 0 = i - (moreDimension.size() - lessDimension.size());
        */
        MatShape outputShape(moreDimension.size(), 1);
        for (int i = 0; i < moreDimension.size(); i++) {
            int d = moreDimension[i];
            int j = i - (moreDimension.size() - lessDimension.size());
            if (j >= 0) {
                if (d == 1 || lessDimension[j] == 1 || // broadcast
                    d == lessDimension[j]) {           // plain copy
                    outputShape[i] = std::max(d, lessDimension[j]);
                } else {
                    CV_Error(Error::StsBadSize, cv::format("DNN/Expand: invalid dimension, d (%d) != d (%d)", moreDimension[i], lessDimension[j]));
                }
            } else {
                outputShape[i] = d;
            }
        }
        outputs.assign(1, outputShape);
        return false;
    }

    void getTypes(const std::vector<MatType>& inputs,
        const int requiredOutputs,
        const int requiredInternals,
        std::vector<MatType>& outputs,
        std::vector<MatType>& internals) const CV_OVERRIDE
    {
        CV_Assert(inputs.size());
        for (auto input : inputs)
            CV_CheckType(input, input == CV_32F || input == CV_16F || input == CV_8S || input == CV_32S || input == CV_64S, "");

        outputs.assign(requiredOutputs, inputs[0]);
    }


    virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
        std::vector<Mat> inputs;
        inputs_arr.getMatVector(inputs);

        const auto &input = inputs[0];
        auto input_shape = shape(input);
        if (const_input_1d) {
            input_shape = {input_shape[0]};
        }

        auto& moreDimension = input_shape.size() > target_shape.size() ? input_shape : target_shape;
        auto& lessDimension = input_shape.size() <= target_shape.size() ? input_shape : target_shape;

        MatShape final_target_shape(moreDimension.size(), 1);
        for (int i = 0; i < moreDimension.size(); i++) {
            int d = moreDimension[i];
            int j = i - (moreDimension.size() - lessDimension.size());
            if (j >= 0) {
                final_target_shape[i] = std::max(lessDimension[j], d);
            } else {
                final_target_shape[i] = d;
            }
        }
        target_shape.clear();
        target_shape = std::move(final_target_shape);
    }

    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);

        int target_shape_total = std::accumulate(target_shape.begin(), target_shape.end(), 1, std::multiplies<int>());
        if (target_shape_total == inputs[0].total()) {
            const char *data = inputs[0].ptr<const char>();
            char *output = outputs[0].ptr<char>();
            int step = target_shape_total * outputs[0].elemSize();
            std::memcpy(output, data, step);
            return;
        }

        if (const_input_1d) {
            const char *data = inputs[0].ptr<const char>();
            char *output = outputs[0].ptr<char>();
            int step = target_shape.back() * outputs[0].elemSize();
            int total = std::accumulate(target_shape.begin(), target_shape.end() - 1, 1, std::multiplies<int>());
            for (int i = 0; i < total; i++) {
                std::memcpy(output + i * step, data, step);
            }
        } else {
            cv::broadcast(inputs[0], target_shape, outputs[0]);
        }
    }

#ifdef HAVE_DNN_NGRAPH
    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
    {
        auto input_shape = nodes[0].dynamicCast<InfEngineNgraphNode>()->node.get_shape();
        CV_CheckGE(target_shape.size(), input_shape.size(), "");

        std::vector<int32_t> output_shape(target_shape.begin(), target_shape.end());
        for (int i = 1; i < input_shape.size() + 1; ++i)
            output_shape[output_shape.size() - i] = std::max(
                (int32_t)input_shape[input_shape.size() - i],
                output_shape[output_shape.size() - i]);

        auto shape_node = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{output_shape.size()}, output_shape.data());
        auto expand = std::make_shared<ov::op::v3::Broadcast>(nodes[0].dynamicCast<InfEngineNgraphNode>()->node, shape_node);
        return Ptr<BackendNode>(new InfEngineNgraphNode(expand));
    }
#endif  // HAVE_DNN_NGRAPH

private:
    MatShape target_shape;
    bool const_input_1d;
};

Ptr<ExpandLayer> ExpandLayer::create(const LayerParams &params) {
    return makePtr<ExpandLayerImpl>(params);
}

}}  // cv::dnn