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// 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 {
static inline int calculateOffset(int outer_dim, const MatShape &shape_indices, int axis_skip, const MatStep &step_data) {
int offset = 0;
for (int axis = static_cast<int>(shape_indices.size()) - 2; axis >= 0; axis--) {
int dim = shape_indices[axis];
if (axis != axis_skip) {
offset += (outer_dim % dim) * step_data[axis];
}
outer_dim /= dim;
}
return offset;
}
class GatherElementsLayerImpl CV_FINAL : public GatherElementsLayer
{
public:
GatherElementsLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
axis = params.get<int>("axis", 0);
}
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_CheckEQ(inputs.size(), 2ull, "GatherElements: requires two inputs");
const auto &data = inputs[0];
const auto &indices = inputs[1];
CV_CheckEQ(data.size(), indices.size(), "GatherElements: data and indices should have the same dimension");
int normalized_axis = normalize_axis(axis, static_cast<int>(data.size()));
CV_CheckGE(normalized_axis, 0, "GatherElements: axis out of range");
CV_CheckLT(normalized_axis, static_cast<int>(data.size()), "GatherElements: axis out of range");
for (size_t i = 0; i < data.size(); i++) {
if (i != normalized_axis) {
CV_CheckEQ(data[i], indices[i], "GatherElements: shape mismatched");
}
}
outputs.assign(1, inputs[1]); // shape of output is same as indices
return false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
const auto &data = inputs[0];
axis = normalize_axis(axis, data.dims);
}
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());
if (inputs_arr.depth() == CV_16F)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
const Mat& data = inputs[0];
const Mat& indices = inputs[1];
Mat& out = outputs[0];
typeDispatch(outputs[0].type(), data, indices, out);
}
template <typename T>
void forward_impl(const Mat& data_, const Mat& indices_, Mat& out_)
{
const auto *ptr_data = data_.ptr<const T>();
const auto *ptr_indices = indices_.ptr<const T>();
auto *ptr_out = out_.ptr<T>();
const auto shape_data = shape(data_);
const auto &step_data = data_.step;
const auto shape_indices = shape(indices_);
int inner_most_dim = shape_indices.back();
int axis_dim = shape_data[axis];
size_t axis_step = static_cast<size_t>(step_data[axis] / sizeof(T));
bool innermost_axis = axis == static_cast<int>(shape_data.size() - 1);
auto fn = [&](const Range &r) {
for (int i = r.start; i < r.end; i++) {
auto *data = ptr_data + static_cast<size_t>(calculateOffset(i, shape_indices, axis, step_data) / sizeof(T));
auto *indices = ptr_indices + i * inner_most_dim;
auto *out = ptr_out + i * inner_most_dim;
if (innermost_axis) {
for (int j = 0; j < inner_most_dim; j++) {
int index = static_cast<int>((indices[j] + axis_dim)) % axis_dim; // TODO: Check out-of-range index
out[j] = data[index];
}
} else {
for (int j = 0; j < inner_most_dim; j++) {
int index = static_cast<int>(indices[j] + axis_dim) % axis_dim; // TODO: Check out-of-range index
out[j] = data[index * axis_step + j];
}
}
}
};
int outer_dims = total(shape_indices, 0, shape_indices.size() - 1);
double nstripes = static_cast<size_t>(outer_dims * inner_most_dim * (1 / 1024.0));
parallel_for_(Range(0, outer_dims), fn, nstripes);
}
template<typename... Args>
inline void typeDispatch(const int type, Args&&... args)
{
switch (type)
{
case CV_8U:
forward_impl<uint8_t>(std::forward<Args>(args)...);
break;
case CV_32S:
forward_impl<int32_t>(std::forward<Args>(args)...);
break;
case CV_32F:
forward_impl<float>(std::forward<Args>(args)...);
break;
default:
CV_Error(cv::Error::BadDepth, "DNN/GatherElements: Unsupported type.");
};
}
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
int32_t indicesBoundValue = nodes[0].dynamicCast<InfEngineNgraphNode>()->node.get_shape()[axis];
auto indicesBound = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{}, &indicesBoundValue);
auto indices = std::make_shared<ov::op::v0::Convert>(nodes[1].dynamicCast<InfEngineNgraphNode>()->node, ov::element::i32);
auto indicesNonNegative = std::make_shared<ov::op::v1::Mod>(
std::make_shared<ov::op::v1::Add>(indices, indicesBound),
indicesBound);
auto gatherElements = std::make_shared<ov::op::v6::GatherElements>(
nodes[0].dynamicCast<InfEngineNgraphNode>()->node,
indicesNonNegative,
axis);
return Ptr<BackendNode>(new InfEngineNgraphNode(gatherElements));
}
#endif // HAVE_DNN_NGRAPH
private:
int axis;
};
Ptr<GatherElementsLayer> GatherElementsLayer::create(const LayerParams& params)
{
return makePtr<GatherElementsLayerImpl>(params);
}
}} // namespace cv::dnn
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