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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 {
class ExpandLayerImpl CV_FINAL : public ExpandLayer
{
public:
ExpandLayerImpl(const LayerParams ¶ms) {
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
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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
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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;
}
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());
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);
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 ¶ms) {
return makePtr<ExpandLayerImpl>(params);
}
}} // cv::dnn
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