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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 "layers_common.hpp"
#include <opencv2/imgproc.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include "../ie_ngraph.hpp"
namespace cv
{
namespace dnn
{
class ScaleLayerInt8Impl CV_FINAL : public ScaleLayerInt8
{
public:
Mat weights, bias;
ScaleLayerInt8Impl(const LayerParams& params)
{
setParamsFrom(params);
hasBias = params.get<bool>("bias_term", false);
axis = params.get<int>("axis", 1);
hasWeights = false;
output_sc = params.get<float>("scales");
output_zp = params.get<int>("zeropoints");
DictValue inpSc = params.get("input_scales");
DictValue inpZp = params.get("input_zeropoints");
for (int i = 0; i < inpSc.size(); i++)
{
inp_sc.push_back(inpSc.get<float>(i));
inp_zp.push_back(inpZp.get<int>(i));
}
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
outputs.assign(1, inputs[0]);
return true;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
hasWeights = blobs.size() == 2 || (blobs.size() <= 1 && !hasBias);
CV_Assert((inputs.size() == 2 && blobs.empty()) || blobs.size() == (int)hasWeights + (int)hasBias);
if (!blobs.empty())
{
Mat w = hasWeights ? blobs[0] : Mat::ones(blobs[0].size(), CV_32F);
Mat b = hasBias ? blobs.back() : Mat::zeros(blobs.back().size(), CV_32F);
w = w.reshape(1, 1);
b = b.reshape(1, 1);
w.convertTo(weights, CV_32F, inp_sc[0]/output_sc);
addWeighted(b, 1.0/output_sc, weights, -inp_zp[0], output_zp, bias, CV_32F);
}
else
{
// initialized during forward()
weights = Mat(); bias = Mat();
}
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
if (!activ_int8.empty())
{
return activ_int8->blobs.empty();
}
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);
Mat &inpBlob = inputs[0];
Mat &outBlob = outputs[0];
if (blobs.empty())
{
CV_Assert(inp_sc.size() == 2 && inp_zp.size() == 2);
Mat inp_dequantized, w, b;
inputs[1].reshape(1, 1).convertTo(inp_dequantized, CV_32F, inp_sc[1], -(inp_sc[1]*inp_zp[1]));
w = hasWeights ? inp_dequantized : Mat::ones(inp_dequantized.size(), CV_32F);
b = hasBias ? inp_dequantized : Mat::zeros(inp_dequantized.size(), CV_32F);
w.convertTo(weights, CV_32F, inp_sc[0]/output_sc);
addWeighted(b, 1.0/output_sc, weights, -inp_zp[0], output_zp, bias, CV_32F);
}
MatShape inpShape = shape(inpBlob);
const int numWeights = weights.total();
CV_Assert(numWeights != 0);
CV_CheckEQ(weights.total(), bias.total(), "Incompatible weights/bias blobs");
int endAxis;
for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
{
if (total(inpShape, axis, endAxis) == numWeights)
break;
}
CV_Assert(total(inpShape, axis, endAxis) == numWeights);
CV_CheckTypeEQ(inpBlob.type(), CV_8SC1, ""); CV_CheckTypeEQ(outBlob.type(), CV_8SC1, "");
int numSlices = total(inpShape, 0, axis);
int8_t* inpData = (int8_t*)inpBlob.data;
int8_t* outData = (int8_t*)outBlob.data;
if (endAxis != inpBlob.dims)
{
float* weightsData = (float*)weights.data;
float* biasesData = (float*)bias.data;
int spatialSize = total(inpShape, endAxis); // spatialSize != 1
for (int i = 0; i < numSlices; ++i)
{
for (int j = 0; j < numWeights; ++j)
{
float w = weightsData[j];
float b = biasesData[j];
Mat inpSlice(1, spatialSize, CV_8S, inpData);
Mat outSlice(1, spatialSize, CV_8S, outData);
inpSlice.convertTo(outSlice, CV_8S, w, b);
inpData += spatialSize;
outData += spatialSize;
}
}
}
else
{
for (int i = 0; i < numSlices; ++i)
{
Mat inpSlice(1, numWeights, CV_8S, inpData);
Mat outSlice(1, numWeights, CV_8S, outData);
multiply(inpSlice, weights, outSlice, 1.0, CV_8S);
add(outSlice, bias, outSlice, Mat(), CV_8S);
inpData += numWeights;
outData += numWeights;
}
}
}
void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE
{
scale = (hasWeights && !blobs.empty()) ? blobs[0] : Mat();
shift = (hasBias && !blobs.empty()) ? blobs.back() : Mat();
}
void getScaleZeropoint(float& scale, int& zeropoint) const CV_OVERRIDE
{
scale = output_sc;
zeropoint = output_zp;
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_UNUSED(outputs); // suppress unused variable warning
long flops = 0;
for(int i = 0; i < inputs.size(); i++)
{
flops += 2*total(inputs[i]);
}
return flops;
}
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
std::vector<ov::Output<ov::Node>> ieInpNodes(nodes.size());
for (int i = 0; i < nodes.size(); ++i) {
ieInpNodes[i] = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
}
ieInpNodes[0] = ngraphDequantize(ieInpNodes[0], inp_sc[0], inp_zp[0]);
CV_Assert(!blobs.empty() || ieInpNodes.size() == 1 + (int)hasWeights + (int)hasBias);
ov::Output<ov::Node> weights, bias;
if (blobs.empty()) {
if (hasWeights)
weights = ieInpNodes[1];
if (hasBias)
bias = ieInpNodes[1 + (int)hasWeights];
} else {
std::vector<size_t> shape = ieInpNodes[0].get_shape();
int cAxis = normalize_axis(axis, shape.size());
size_t numWeights = blobs[0].total();
for (int i = 0; i < cAxis; ++i) {
shape[i] = 1;
}
for (int i = cAxis; i < shape.size(); ++i) {
if (numWeights == 1) {
shape[i] = 1;
}
numWeights = std::max(numWeights / shape[i], (size_t)1);
}
if (hasWeights)
weights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, blobs[0].data);
if (hasBias)
bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, blobs[(int)hasWeights].data);
}
ov::Output<ov::Node> res = ieInpNodes[0];
if (hasWeights) {
res = std::make_shared<ov::op::v1::Multiply>(res, weights);
}
if (hasBias) {
res = std::make_shared<ov::op::v1::Add>(res, bias);
}
res = ngraphQuantize(res, output_sc, output_zp);
return new InfEngineNgraphNode(res);
}
#endif // HAVE_DNN_NGRAPH
private:
bool hasWeights;
std::vector<float> inp_sc;
std::vector<int> inp_zp;
};
Ptr<ScaleLayerInt8> ScaleLayerInt8::create(const LayerParams& params)
{
return Ptr<ScaleLayerInt8>(new ScaleLayerInt8Impl(params));
}
Ptr<Layer> ShiftLayerInt8::create(const LayerParams& params)
{
LayerParams scaleParams = params;
scaleParams.type = "ScaleInt8";
scaleParams.set("bias_term", true);
scaleParams.set("axis", 0);
return Ptr<ScaleLayerInt8>(new ScaleLayerInt8Impl(scaleParams));
}
} // namespace dnn
} // namespace cv
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