opencv/modules/dnn/src/int8layers/eltwise_layer.cpp
2021-08-19 09:56:47 +05:30

578 lines
22 KiB
C++

// 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/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class EltwiseLayerInt8Impl CV_FINAL : public EltwiseLayerInt8
{
public:
enum EltwiseOp
{
PROD = 0,
SUM = 1,
MAX = 2
} op;
std::vector<float> coeffs;
std::vector<int> zeropoints;
enum OutputChannelsMode
{
ELTWISE_CHANNNELS_SAME = 0, //!< number of channels from inputs must be the same and equal to output's number of channels
ELTWISE_CHANNNELS_INPUT_0, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to number of channels of first input
//!< number of channels of other inputs should not be greater than number of channels of first input
ELTWISE_CHANNNELS_INPUT_0_TRUNCATE, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to number of channels of first input
//!< there is restriction on number of channels of other inputs
//!< extra channels of other inputs is ignored
ELTWISE_CHANNNELS_USE_MAX, //!< number of channels from inputs may be different,
//!< output's number of channels is equal to maximal number of input channels
//!< @note supported operation: `SUM`
} channelsModeInput;
mutable OutputChannelsMode channelsMode; //!< "optimized" channels mode (switch to ELTWISE_CHANNNELS_SAME if number of input channels are equal)
mutable /*size_t*/int outputChannels;
EltwiseLayerInt8Impl(const LayerParams& params)
: outputChannels(0)
{
setParamsFrom(params);
offset = params.get<float>("offset", 0.f);
hasVecInput = false;
op = SUM;
if (params.has("operation"))
{
String operation = toLowerCase(params.get<String>("operation"));
if (operation == "prod")
op = PROD;
else if (operation == "sum")
op = SUM;
else if (operation == "max")
op = MAX;
else
CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
}
if (params.has("coeff"))
{
DictValue paramCoeff = params.get("coeff");
int i, n = paramCoeff.size();
coeffs.resize(n);
for (i = 0; i < n; i++)
{
coeffs[i] = paramCoeff.get<float>(i);
}
}
if (params.has("input_zeropoints"))
{
DictValue zp = params.get("input_zeropoints");
int i, n = zp.size();
zeropoints.resize(n);
for (i = 0; i < n; i++)
{
zeropoints[i] = zp.get<int>(i);
}
}
channelsModeInput = ELTWISE_CHANNNELS_SAME;
if (params.has("output_channels_mode"))
{
String v = toLowerCase(params.get<String>("output_channels_mode"));
if (v == "same")
{
channelsModeInput = ELTWISE_CHANNNELS_SAME;
}
else if (v == "input_0")
{
channelsModeInput = ELTWISE_CHANNNELS_INPUT_0;
}
else if (v == "input_0_truncate")
{
channelsModeInput = ELTWISE_CHANNNELS_INPUT_0_TRUNCATE;
}
else if (v == "max_input_channels")
{
channelsModeInput = ELTWISE_CHANNNELS_USE_MAX;
if (op != SUM)
CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") 'max' channels mode is limited to SUM operation only");
}
else
CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") unknown channels mode: \"" + v + "\"");
}
channelsMode = channelsModeInput;
// TODO Must have checks for other unknown options
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() >= 2);
CV_Assert(inputs[0].size() >= 2);
CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
CV_Assert(op == SUM || op == PROD || coeffs.size() == 0);
int dims = inputs[0].size();
// Number of channels in output shape is determined by the first input tensor.
bool variableChannels = false;
int numChannels = inputs[0][1];
for (size_t i = 1; i < inputs.size(); i++)
{
CV_Assert(inputs[0][0] == inputs[i][0]); // batch sizes are equal
int input_channels = inputs[i][1];
if (numChannels != input_channels)
variableChannels = true;
if (channelsModeInput == ELTWISE_CHANNNELS_SAME)
{
CV_Assert(numChannels == input_channels);
}
else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0)
{
CV_Assert(numChannels >= input_channels);
}
else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
{
// nothing to check
}
else if (channelsModeInput == ELTWISE_CHANNNELS_USE_MAX)
{
numChannels = std::max(numChannels, input_channels);
}
else
{
CV_Assert(0 && "Internal error");
}
}
channelsMode = variableChannels ? channelsModeInput : ELTWISE_CHANNNELS_SAME;
outputChannels = numChannels;
outputs.assign(1, inputs[0]);
outputs[0][1] = numChannels;
if (dims > 2)
{
size_t vecIdx = 0;
bool isVecFound = false;
for (size_t i = 0; i < inputs.size(); i++)
{
bool allOnes = isAllOnes(inputs[i], 2, dims);
if (!allOnes && !isVecFound)
{
vecIdx = i;
isVecFound = true;
}
if (!allOnes && i != vecIdx)
{
for (size_t j = 2; j < dims; j++)
{
CV_Assert(inputs[vecIdx][j] == inputs[i][j]);
}
}
}
if (channelsModeInput == ELTWISE_CHANNNELS_SAME && isVecFound)
{
for (size_t j = 2; j < dims; j++)
{
outputs[0][j] = inputs[vecIdx][j];
}
}
}
return false;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape inpShape = shape(inputs[i].size);
if (isAllOnes(inpShape, 2, inputs[i].dims))
{
hasVecInput = true;
return;
}
}
}
class EltwiseInvoker : public ParallelLoopBody
{
EltwiseLayerInt8Impl& self;
std::vector<const Mat*> srcs;
std::vector<int> srcNumChannels;
int nsrcs;
Mat* dst;
Mat* buf;
std::vector<float> coeffs;
std::vector<int> zeropoints;
int nstripes;
const Mat* activLUT;
const ActivationLayerInt8* activ;
int channels;
size_t planeSize;
float offset;
EltwiseInvoker(EltwiseLayerInt8Impl& self_)
: self(self_)
, nsrcs(0), dst(0), buf(0), nstripes(0), activ(0), channels(0)
, planeSize(0), offset(0)
{}
public:
static void run(EltwiseLayerInt8Impl& self,
const Mat* srcs, int nsrcs, Mat& buf, Mat& dst,
int nstripes, float offset)
{
const EltwiseOp op = self.op;
CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 5, ""); CV_CheckTypeEQ(dst.type(), CV_8SC1, ""); CV_Assert(dst.isContinuous());
CV_Assert(self.coeffs.empty() || self.coeffs.size() == (size_t)nsrcs);
CV_CheckGE(nsrcs, 2, "");
CV_Assert(self.outputChannels == dst.size[1]);
EltwiseInvoker p(self);
p.srcs.resize(nsrcs);
p.srcNumChannels.resize(nsrcs);
p.coeffs = self.coeffs; // can be sorted
p.zeropoints = self.zeropoints;
bool sortInputs = false;
for( int i = 0; i < nsrcs; i++ )
{
p.srcs[i] = &srcs[i];
CV_CheckEQ(srcs[i].dims, dst.dims, "");
CV_Assert(srcs[i].isContinuous());
CV_Assert(srcs[i].type() == dst.type());
p.srcNumChannels[i] = (srcs[i].dims >= 4) ? srcs[i].size[1] : 1;
if (self.channelsMode == ELTWISE_CHANNNELS_SAME)
{
CV_Assert(srcs[i].size == dst.size);
}
else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0)
{
if (i == 0)
CV_Assert(srcs[0].size == dst.size);
CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
sortInputs = true;
}
else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
{
if (i == 0)
CV_Assert(srcs[0].size == dst.size);
sortInputs = true;
}
else if (self.channelsMode == ELTWISE_CHANNNELS_USE_MAX)
{
CV_Assert(op == SUM);
CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
sortInputs = true;
}
else
{
CV_Assert(0 && "Internal error");
}
if (sortInputs)
{
// Sort srcs and coefficients in the desc order by number of channels
for (int j = i; j >= 1; j--)
{
if (std::min(self.outputChannels, p.srcs[j - 1]->size[1]) < std::min(self.outputChannels, p.srcs[j]->size[1]))
{
std::swap(p.srcs[j - 1], p.srcs[j]);
std::swap(p.srcNumChannels[j - 1], p.srcNumChannels[j]);
if (!p.coeffs.empty())
std::swap(p.coeffs[j - 1], p.coeffs[j]);
if (!p.zeropoints.empty())
std::swap(p.zeropoints[j - 1], p.zeropoints[j]);
}
else
break;
}
}
}
p.nsrcs = nsrcs;
p.dst = &dst;
p.buf = &buf;
p.nstripes = nstripes;
p.offset = offset;
p.channels = (dst.dims >= 4 ? dst.size[1] : 1);
p.planeSize = dst.total(dst.dims >= 4 ? 2 : 1);
CV_CheckEQ(dst.total(), dst.size[0] * p.channels * p.planeSize, "");
p.activLUT = &self.activationLUT;
p.activ = !self.activationLUT.empty() ? self.activ.get() : 0;
parallel_for_(Range(0, nstripes), p, nstripes);
}
void operator()(const Range& r) const CV_OVERRIDE
{
const EltwiseOp op = self.op;
size_t total = dst->size[0]*planeSize;
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, total);
const float* coeffsptr = !coeffs.empty() ? &coeffs[0] : 0;
const int* zeropointsptr = !zeropoints.empty() ? &zeropoints[0] : 0;
const int8_t* lutptr = !activLUT->empty() ? activLUT->ptr<int8_t>() : 0;
int8_t* dstptr0 = dst->ptr<int8_t>();
float* bufptr0 = buf->ptr<float>();
int blockSize0 = 1 << 12;
for (size_t ofs = stripeStart; ofs < stripeEnd; )
{
int sampleIdx = (int)(ofs / planeSize);
int delta = (int)ofs - sampleIdx * planeSize;
int blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
if( blockSize <= 0 )
break;
ofs += blockSize;
for (int c = 0; c < channels; c++)
{
size_t dstIdx = delta + (sampleIdx*channels + c)*planeSize;
int8_t* dstptr = dstptr0 + dstIdx;
float* bufptr = bufptr0 + dstIdx;
// process first two inputs
{
const int8_t* srcptr0 = srcs[0]->ptr<int8_t>() + dstIdx;
const int inputIdx = 1;
int src1_channels = srcNumChannels[inputIdx];
if (c >= src1_channels)
{
// no data from second input
if (!coeffsptr)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = srcptr0[j];
}
}
else
{
float c0 = coeffsptr[0];
int z0 = op == PROD ? zeropointsptr[0] : 0;
for (int j = 0; j < blockSize; j++)
{
bufptr[j] = c0 * (srcptr0[j] - z0);
}
}
}
else
{
size_t srcIdx = delta + (sampleIdx * src1_channels + c) * planeSize;
const int8_t* srcptrI = srcs[inputIdx]->ptr<int8_t>() + srcIdx;
if (op == PROD)
{
float c0 = coeffsptr[0];
float c1 = coeffsptr[1];
int z0 = zeropointsptr[0];
int z1 = zeropointsptr[1];
for (int j = 0; j < blockSize; j++)
{
bufptr[j] = (c0*(srcptr0[j] - z0)) * (c1*(srcptrI[j] - z1));
}
}
else if (op == MAX)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = std::max(srcptr0[j], srcptrI[j]);
}
}
else if (op == SUM)
{
float c0 = coeffsptr[0];
float c1 = coeffsptr[1];
for (int j = 0; j < blockSize; j++)
{
bufptr[j] = c0*srcptr0[j] + c1*srcptrI[j];
}
}
else
CV_Error(Error::StsInternal, "");
}
}
// aggregate other inputs (3+)
for (size_t inputIdx = 2; inputIdx < nsrcs; inputIdx++)
{
int srcI_channels = srcNumChannels[inputIdx];
if (c >= srcI_channels)
continue; // no data from second input
size_t srcIdx = delta + (sampleIdx * srcI_channels + c) * planeSize;
const int8_t* srcptrI = srcs[inputIdx]->ptr<int8_t>() + srcIdx;
if (op == PROD)
{
float cI = coeffsptr[inputIdx];
int zI = zeropointsptr[inputIdx];
for (int j = 0; j < blockSize; j++)
{
bufptr[j] *= cI*(srcptrI[j] - zI);
}
}
else if (op == MAX)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = std::max(dstptr[j], srcptrI[j]);
}
}
else if (op == SUM)
{
float cI = coeffsptr[inputIdx];
for (int j = 0; j < blockSize; j++)
{
bufptr[j] += cI * srcptrI[j];
}
}
else
CV_Error(Error::StsInternal, "");
}
// add offset and saturate cast to int8
if (op == SUM || op == PROD)
{
for (int j = 0; j < blockSize; j++)
{
dstptr[j] = saturate_cast<int8_t>(std::round(bufptr[j] + offset));
}
}
}
if( activ )
{
int8_t* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
activ->forwardSlice(ptr, lutptr, ptr, blockSize, planeSize, 0, channels);
}
}
}
};
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);
CV_Assert(outputs.size() == 1);
const int nstripes = getNumThreads();
if (channelsModeInput == ELTWISE_CHANNNELS_SAME && inputs[0].dims > 2)
{
for (size_t i = 0; i < inputs.size(); i++)
{
MatShape inpShape = shape(inputs[i].size);
bool allOnes = isAllOnes(inpShape, 2, inputs[i].dims);
if (allOnes)
{
Mat tmpInput = inputs[i];
MatShape outShape = shape(outputs[0].size);
size_t xSize = outShape[2];
for (size_t j = 3; j < outShape.size(); j++)
xSize *= outShape[j];
int dimVec[3] = {outShape[0], outShape[1], (int) xSize};
std::vector<int> matSizesVec(&dimVec[0], &dimVec[0] + 3);
inputs[i] = Mat(matSizesVec, tmpInput.type());
std::vector<int> idx(outShape.size(), 0);
std::vector<int> outIdx(inpShape.size(), 0);
for (size_t j = 0; j < outShape[0]; j++)
{
outIdx[0] = idx[0] = j;
for(size_t k = 0; k < outShape[1]; k++)
{
outIdx[1] = idx[1] = k;
for (size_t x = 0; x < xSize; x++)
{
outIdx[2] = x;
inputs[i].at<int8_t>(outIdx.data()) = tmpInput.at<int8_t>(idx.data());
}
}
}
inputs[i] = inputs[i].reshape(0, outShape);
}
}
}
Mat buf = Mat(shape(outputs[0]), CV_32F); // to store intermediate results
EltwiseInvoker::run(*this, &inputs[0], (int)inputs.size(), buf, outputs[0], nstripes, offset);
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_UNUSED(outputs); // suppress unused variable warning
CV_Assert(inputs.size());
// FIXIT: handle inputs with different number of channels
long flops = inputs.size() * total(inputs[0]);
return flops;
}
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
if (!activ_int8.empty())
{
activ = activ_int8;
if (!activ_int8->blobs.empty())
activationLUT = activ_int8->blobs[0];
return true;
}
return false;
}
Mat activationLUT;
Ptr<ActivationLayerInt8> activ;
private:
bool hasVecInput;
float offset;
};
Ptr<EltwiseLayerInt8> EltwiseLayerInt8::create(const LayerParams& params)
{
return Ptr<EltwiseLayerInt8>(new EltwiseLayerInt8Impl(params));
}
}
}