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#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
using namespace cv::dnn::ocl4dnn;
#endif
namespace cv
{
namespace dnn
{
class BaseConvolutionLayerImpl : public ConvolutionLayer
{
public:
BaseConvolutionLayerImpl(const LayerParams ¶ms)
{
setParamsFrom(params);
getConvolutionKernelParams(params, kernel.height, kernel.width, pad.height,
pad.width, stride.height, stride.width, dilation.height,
dilation.width, padMode);
numOutput = params.get<int>("num_output");
int ngroups = params.get<int>("group", 1);
adjustPad.height = params.get<int>("adj_h", 0);
adjustPad.width = params.get<int>("adj_w", 0);
CV_Assert(numOutput % ngroups == 0);
CV_Assert(adjustPad.width < stride.width &&
adjustPad.height < stride.height);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if (type == "Convolution")
return preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height;
else
{
CV_Assert(type == "Deconvolution");
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (group != 1)
return false;
if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
return dilation.width == 1 && dilation.height == 1;
return true;
}
}
else
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
CV_Assert(inputs.size() > 0);
CV_Assert(blobs.size() >= 1 && blobs.size() <= 2);
CV_Assert(blobs[0].dims == 4 && blobs[0].size[3] == kernel.width && blobs[0].size[2] == kernel.height);
const Mat &input = *inputs[0];
CV_Assert(input.dims == 4 && (input.type() == CV_32F || input.type() == CV_64F || input.type() == CV_16S));
for (size_t i = 0; i < inputs.size(); i++)
{
CV_Assert(inputs[i]->type() == input.type());
CV_Assert(inputs[i]->dims == 4 && inputs[i]->size[1] == input.size[1]);
CV_Assert(inputs[i]->size[2] == input.size[2] && inputs[i]->size[3] == input.size[3]);
}
Size outSize = Size(outputs[0].size[3], outputs[0].size[2]);
getConvPoolPaddings(Size(input.size[3], input.size[2]), outSize,
kernel, stride, padMode, dilation, pad);
}
bool hasBias() const
{
return blobs.size() >= 2;
}
virtual MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const = 0;
bool is1x1() const
{
return (kernel.height == 1 && kernel.width == 1) &&
(stride.height == 1 && stride.width == 1) &&
(dilation.height == 1 && dilation.width == 1);
}
virtual void applyHalideScheduler(Ptr<BackendNode>& node,
const std::vector<Mat*> &inputs,
const std::vector<Mat> &outputs,
int targetId) const CV_OVERRIDE
{
#ifdef HAVE_HALIDE
if (targetId != DNN_TARGET_CPU)
{
Layer::applyHalideScheduler(node, inputs, outputs, targetId);
return;
}
Halide::Var x("x"), y("y"), c("c"), n("n"), tile("tile"), yi("yi"), yo("yo"), co("co"), ci("ci");
Halide::Func& top = node.dynamicCast<HalideBackendNode>()->funcs[1];
Halide::Func& padded_input = node.dynamicCast<HalideBackendNode>()->funcs[0];
int outW, outH, outC, outN;
getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN);
if (outW == 1 || outH <= 2)
return;
if (is1x1() || outC <= 16)
top.reorder(x, c, y)
.split(y, yo, yi, 2)
.fuse(yo, n, tile)
.parallel(tile)
.unroll(yi)
.vectorize(x, outW >= 16 ? 16 : outW);
else
top.reorder(x, c, y)
.split(y, yo, yi, 2)
.split(c, co, ci, 16)
.fuse(yo, co, tile).fuse(n, tile, tile)
.parallel(tile)
.unroll(yi)
.vectorize(x, outW >= 16 ? 16 : outW);
padded_input.compute_at(top, yi);
#endif // HAVE_HALIDE
}
};
#define IS_POWER_LAYER(layer) \
(!layer.empty() && !layer->type.compare("Power"))
//TODO: simultaneously convolution and bias addition for cache optimization
class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
enum { VEC_ALIGN = 8, DFT_TYPE = CV_32F };
Mat weightsMat;
std::vector<double> weightsMultipliers;
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
bool newWeightAndBias;
bool fusedBias;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
std::vector<UMat> umat_blobs;
bool newActiv;
ocl4dnnFusedActiv_t activType;
float power;
#endif
ConvolutionLayerImpl(const LayerParams ¶ms) : BaseConvolutionLayerImpl(params)
{
newWeightAndBias = false;
fusedBias = false;
#ifdef HAVE_OPENCL
newActiv = false;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
power = 0.f;
#endif
}
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
Size out(outShape[3], outShape[2]);
int inpGroupCn = blobs[0].size[1];
int ksize = inpGroupCn * kernel.height * kernel.width;
return shape(out.area(), ksize);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(blobs.size() != 0);
CV_Assert(!hasBias() || blobs[1].total() == (size_t)blobs[0].size[0]);
CV_Assert(inputs.size() == (size_t)1);
internals.clear();
int inpCn = inputs[0][1];
int inpH = inputs[0][2];
int inpW = inputs[0][3];
int outCn = blobs[0].size[0];
Size out;
if (padMode.empty())
{
out.height = (inpH + 2 * pad.height - (dilation.height * (kernel.height - 1) + 1)) / stride.height + 1;
out.width = (inpW + 2 * pad.width - (dilation.width * (kernel.width - 1) + 1)) / stride.width + 1;
}
else
{
getConvPoolOutParams(Size(inpW, inpH), kernel, stride, padMode, dilation, out);
}
int ngroups = inpCn / blobs[0].size[1];
CV_Assert(ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0);
int dims[] = {inputs[0][0], outCn, out.height, out.width};
outputs.resize(inputs.size(), shape(dims, 4));
return false;
}
virtual void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs, outputs);
CV_Assert(!blobs.empty());
const int outCn = blobs[0].size[0];
// prepare weightsMat where each row is aligned and has enough zero padding on the right to
// use vectorized (i.e. with intrinsics) loops without tail processing
Mat wm = blobs[0].reshape(1, outCn).clone();
if( wm.step1() % VEC_ALIGN != 0 )
{
int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_buffer = Mat(outCn, newcols, wm.type());
Mat wm_padding = wm_buffer.colRange(wm.cols, newcols);
wm_padding.setTo(Scalar::all(0.));
Mat wm_aligned = wm_buffer.colRange(0, wm.cols);
wm.copyTo(wm_aligned);
wm = wm_aligned;
}
weightsMat = wm;
weightsMultipliers.assign(outCn, 1.0);
Mat biasMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat();
biasvec.resize(outCn+2);
if( biasMat.empty() )
{
for(int i = 0; i < outCn; i++ )
biasvec[i] = 0.f;
}
else
{
for(int i = 0; i < outCn; i++ )
biasvec[i] = biasMat.at<float>(i);
}
#ifdef HAVE_OPENCL
convolutionOp.release();
#endif
}
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
if (!activ.empty() && !layer.empty())
return false;
activ = layer;
if (activ.empty())
reluslope.clear();
#ifdef HAVE_OPENCL
newActiv = true;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
if (IS_DNN_OPENCL_TARGET(preferableTarget))
{
Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
if (!activ_power.empty())
{
if (activ_power->scale != 1.f || activ_power->shift != 0.f)
{
const int outCh = blobs[0].size[0];
fuseWeights(Mat(1, outCh, CV_32F, Scalar(activ_power->scale)),
Mat(1, outCh, CV_32F, Scalar(activ_power->shift)));
}
power = activ_power->power;
activType = OCL4DNN_CONV_FUSED_ACTIV_POWER;
}
Ptr<TanHLayer> activ_tanh = activ.dynamicCast<TanHLayer>();
if (!activ_tanh.empty())
{
activType = OCL4DNN_CONV_FUSED_ACTIV_TANH;
}
}
#endif
return !activ.empty();
}
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
Mat w, b;
top->getScaleShift(w, b);
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
return true;
}
return false;
}
void fuseWeights(const Mat& w, const Mat& b)
{
// Convolution weights have OIHW data layout. Parameters fusion in case of
// (conv(I) + b1 ) * w + b2
// means to replace convolution's weights to [w*conv(I)] and bias to [b1 * w + b2]
const int outCn = weightsMat.size[0];
CV_Assert_N(!weightsMat.empty(), biasvec.size() == outCn + 2,
w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
if (!w.empty())
{
Mat originWeights = blobs[0].reshape(1, outCn);
for (int i = 0; i < outCn; ++i)
{
double wi = w.at<float>(i);
weightsMultipliers[i] *= wi;
cv::multiply(originWeights.row(i), weightsMultipliers[i], weightsMat.row(i));
biasvec[i] *= wi;
}
}
if (!b.empty())
{
for (int i = 0; i < outCn; ++i)
biasvec[i] += b.at<float>(i);
}
newWeightAndBias = !w.empty() || !b.empty();
fusedBias = hasBias() || !b.empty();
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
const int inpCn = inputBuffer.channels();
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn;
const int outGroupCn = outCn / group;
Halide::Buffer<float> weights = wrapToHalideBuffer(blobs[0]);
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Func padded_input(name + "_constant_exterior");
if (pad.width || pad.height)
{
Halide::Func bounded =
Halide::BoundaryConditions::constant_exterior(inputBuffer, 0);
padded_input(x, y, c, n) = bounded(x, y, c, n);
}
else
{
padded_input(x, y, c, n) = inputBuffer(x, y, c, n);
}
Halide::RDom r(0, kernel.width, 0, kernel.height, 0, inpGroupCn);
Halide::Expr kx = x * stride.width - pad.width + r.x * dilation.width;
Halide::Expr ky = y * stride.height - pad.height + r.y * dilation.height;
Halide::Expr kc = r.z;
for (int i = 1; i < group; ++i)
{
kc = select(c < outGroupCn * i, kc, inpGroupCn * i + r.z);
}
Halide::Expr topExpr = sum(padded_input(kx, ky, kc, n) *
weights(r.x, r.y, r.z, c));
if (hasBias())
{
Halide::Buffer<float> bias = wrapToHalideBuffer(blobs[1], {outCn});
topExpr += bias(c);
}
top(x, y, c, n) = topExpr;
return Ptr<BackendNode>(new HalideBackendNode({ padded_input, top }));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
CV_Assert(input->dims.size() == 4);
const int inpCn = input->dims[2]; // NOTE: input->dims are reversed (whcn)
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn;
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Convolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ConvolutionLayer> ieLayer(new InferenceEngine::ConvolutionLayer(lp));
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_out_depth = outCn;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (newWeightAndBias)
{
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieLayer->_weights = wrapToInfEngineBlob(fusedWeights, InferenceEngine::Layout::OIHW);
}
else
{
ieLayer->_weights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieLayer->_weights->dims());
ieLayer->_weights->allocate();
Mat newWeights = infEngineBlobToMat(ieLayer->_weights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
}
}
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieLayer->_biases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
class ParallelConv : public cv::ParallelLoopBody
{
public:
enum { BLK_SIZE = 32, BLK_SIZE_CN = 64 };
const Mat* input_;
const Mat* weights_;
Mat* output_;
int outShape[4];
Size kernel_, pad_, stride_, dilation_;
int ngroups_, nstripes_;
std::vector<int> ofstab_;
const std::vector<float>* biasvec_;
const std::vector<float>* reluslope_;
const ActivationLayer* activ_;
bool is1x1_;
bool useAVX;
bool useAVX2;
bool useAVX512;
ParallelConv()
: input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
biasvec_(0), reluslope_(0), activ_(0), is1x1_(false), useAVX(false), useAVX2(false), useAVX512(false)
{}
static void run( const Mat& input, Mat& output, const Mat& weights,
const std::vector<float>& biasvec,
const std::vector<float>& reluslope,
Size kernel, Size pad, Size stride, Size dilation,
const ActivationLayer* activ, int ngroups, int nstripes )
{
CV_Assert_N(
input.dims == 4 && output.dims == 4,
input.size[0] == output.size[0],
weights.rows == output.size[1],
weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
input.type() == output.type(),
input.type() == weights.type(),
input.type() == CV_32FC1,
input.isContinuous(),
output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2);
ParallelConv p;
p.input_ = &input;
p.weights_ = &weights;
p.output_ = &output;
for( int i = 0; i < 4; i++ ) p.outShape[i] = output.size[i];
p.outShape[1] /= ngroups;
p.kernel_ = kernel; p.pad_ = pad; p.stride_ = stride; p.dilation_ = dilation;
p.ngroups_ = ngroups;
p.nstripes_ = nstripes;
int inpCnAll = input.size[1], width = input.size[3], height = input.size[2];
int inpCn = inpCnAll / ngroups;
p.is1x1_ = kernel == Size(0,0) && pad == Size(0, 0);
p.useAVX = checkHardwareSupport(CPU_AVX);
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
int ncn = std::min(inpCn, (int)BLK_SIZE_CN);
p.ofstab_.resize(kernel.width*kernel.height*ncn);
int* ofstab = &p.ofstab_[0];
for( int k = 0; k < ncn; k++ )
for( int k_r = 0; k_r < kernel.height; k_r++ )
for( int k_c = 0; k_c < kernel.width; k_c++ )
ofstab[(k*kernel.height + k_r)*kernel.width + k_c] =
(k*height + k_r*dilation.height)*width + k_c*dilation.width;
p.biasvec_ = &biasvec;
p.reluslope_ = &reluslope;
p.activ_ = p.reluslope_->empty() ? activ : 0;
parallel_for_(Range(0, nstripes), p, nstripes);
}
virtual void operator ()(const Range &r0) const CV_OVERRIDE
{
const int valign = ConvolutionLayerImpl::VEC_ALIGN;
int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
int outW = output_->size[3], outH = output_->size[2], outCn = output_->size[1]/ngroups;
int width = input_->size[3], height = input_->size[2], inpCn = input_->size[1]/ngroups;
const int nstripes = nstripes_;
int kernel_w = kernel_.width, kernel_h = kernel_.height;
int pad_w = pad_.width, pad_h = pad_.height;
int stride_w = stride_.width, stride_h = stride_.height;
int dilation_w = dilation_.width, dilation_h = dilation_.height;
int karea = kernel_w*kernel_h;
int i, j, k;
size_t inpPlaneSize = width*height;
size_t outPlaneSize = outW*outH;
bool is1x1 = is1x1_;
int stripesPerSample;
size_t stripeSize;
Range r = r0;
if( nstripes >= batchSize*2 )
{
stripesPerSample = nstripes/batchSize;
stripeSize = alignSize((outPlaneSize + stripesPerSample - 1)/stripesPerSample, valign);
stripeSize = std::min(stripeSize, outPlaneSize);
}
else
{
stripesPerSample = 1;
int samplesPerStripe = std::max((batchSize + nstripes - 1)/nstripes, 1);
r.start *= samplesPerStripe;
r.end *= samplesPerStripe;
stripeSize = outPlaneSize;
}
const float* data_inp0_ = input_->ptr<float>();
const int* ofstab = &ofstab_[0];
const float* wptr_orig_ = weights_->ptr<float>();
size_t wstep = weights_->step1();
const float* biasptr_ = &biasvec_->at(0);
const float* reluptr_ = reluslope_->empty() ? 0 : &reluslope_->at(0);
float* data_out0_ = output_->ptr<float>();
size_t rowbufsz = (size_t)karea*BLK_SIZE_CN*BLK_SIZE;
AutoBuffer<float> rowbuf0_(rowbufsz + valign);
float* rowbuf0 = alignPtr(rowbuf0_.data(), (int)(valign*sizeof(float)));
// we clear the buffer once; ultimately, it lets us to avoid
// tail processing after running the unrolled/vectorized loop.
// the main idea is to make sure that the tail (a.k.a. padding) of each row
// (i.e. the elements with indices between vsz=karea*ncn and vsz_a)
// does not contain NaNs or Infs. Because the padding in the weights
// matrix is explicitly initialized with 0's, we handle all other
// cases nicely, i.e. we can skip expliciting re-initialization
// of the padding - we just retain elements from the previous iteration
// of the loop over channels (cn0).
memset(rowbuf0, 0, rowbufsz*sizeof(rowbuf0[0]) );
for( int stripe = r.start; stripe < r.end; stripe++ )
{
int subsampleIdx = stripe/stripesPerSample;
if( subsampleIdx >= batchSize )
break;
int stripeStart = (int)((stripe - subsampleIdx*stripesPerSample)*stripeSize);
int stripeEnd = (int)std::min(stripeStart + stripeSize, outPlaneSize);
const float* data_inp0 = data_inp0_ + subsampleIdx*inpPlaneSize*inpCn;
float* data_out0 = data_out0_ + subsampleIdx*outPlaneSize*outCn;
int startOutCn = (subsampleIdx % ngroups)*outCn;
const float* wptr_orig = wptr_orig_ + wstep*startOutCn;
const float* biasptr = biasptr_ + startOutCn;
for( int cn0 = 0; cn0 < inpCn; cn0 += BLK_SIZE_CN )
{
int cn1 = std::min(cn0 + BLK_SIZE_CN, inpCn);
int ncn = cn1 - cn0, vsz = karea*ncn;
int vsz_a = (int)alignSize(vsz, valign);
const float* wptr = wptr_orig + cn0*karea;
// we apply [Channels][P]ReLU (if any) during the final pass only.
const float* relu = cn1 == inpCn && reluptr_ ? reluptr_ + startOutCn : 0;
for( int ofs0 = stripeStart; ofs0 < stripeEnd; ofs0 += BLK_SIZE )
{
int ofs, ofs1 = std::min(ofs0 + BLK_SIZE, stripeEnd);
int out_i = ofs0 / outW;
int out_j = ofs0 - out_i * outW;
// do im2row for a part of input tensor
float* rowbuf = rowbuf0;
for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
int out_j1 = out_j + delta;
int in_i = out_i * stride_h - pad_h;
int in_j = out_j * stride_w - pad_w;
const float* imgptr = data_inp0 + (cn0*height + in_i)*width + in_j;
ofs += delta;
// do im2row for a part of input tensor
if( is1x1 )
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w )
{
for( k = 0; k < vsz; k++ )
rowbuf[k] = imgptr[k*inpPlaneSize];
}
}
else
{
bool ok_i = 0 <= in_i && in_i < height - (kernel_h-1)*dilation_h;
int i0 = std::max(0, (-in_i + dilation_h-1)/dilation_h);
int i1 = std::min(kernel_h, (height - in_i + dilation_h-1)/dilation_h);
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w, in_j += stride_w )
{
// this condition should be true for most of the tensor elements, i.e.
// most of the time the kernel aperture is inside the tensor X-Y plane.
if( ok_i && out_j + 2 <= out_j1 && 0 <= in_j && in_j + stride_w*2 <= width - (kernel_w-1)*dilation_w )
{
for( k = 0; k < vsz; k++ )
{
int k1 = ofstab[k];
float v0 = imgptr[k1];
float v1 = imgptr[k1 + stride_w];
rowbuf[k] = v0;
rowbuf[k+vsz_a] = v1;
}
out_j++;
rowbuf += vsz_a;
imgptr += stride_w;
in_j += stride_w;
}
else
{
int j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int j1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
// here some non-continuous sub-row of the row will not be
// filled from the tensor; we need to make sure that the uncovered
// elements are explicitly set to 0's. the easiest way is to
// set all the elements to 0's before the loop.
memset(rowbuf, 0, vsz*sizeof(rowbuf[0]));
for( k = 0; k < ncn; k++ )
{
for( i = i0; i < i1; i++ )
{
for( j = j0; j < j1; j++ )
{
int imgofs = k*(width*height) + i*(dilation_h*width) + j*dilation_w;
rowbuf[(k*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
}
}
}
}
}
}
}
// now compute dot product of the weights
// and im2row-transformed part of the tensor
int bsz = ofs1 - ofs0;
#if CV_TRY_AVX512_SKX
/* AVX512 convolution requires an alignment of 16, and ROI is only there for larger vector sizes */
if(useAVX512)
opt_AVX512_SKX::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_AVX2
if(useAVX2)
opt_AVX2::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
#if CV_TRY_AVX
if(useAVX)
opt_AVX::fastConv(wptr, wstep, biasptr, rowbuf0, data_out0 + ofs0,
outShape, bsz, vsz, vsz_a, relu, cn0 == 0);
else
#endif
for( int i = 0; i < outCn; i += 2 )
{
const float* wptr0 = wptr + i*wstep;
const float* wptr1 = wptr0 + wstep;
float* outptr0 = data_out0 + ofs0 + i*outPlaneSize;
float* outptr1 = outptr0 + outPlaneSize;
float bias0 = biasptr[i], bias1 = biasptr[i+1];
float r0 = 1.f, r1 = 1.f;
if( i+1 >= outCn )
{
wptr1 = wptr0;
outptr1 = outptr0;
bias1 = bias0;
}
if( relu )
{
r0 = relu[i]; r1 = relu[i+1];
if( i+1 >= outCn )
r1 = r0;
}
int j = 0;
#if CV_SIMD128
v_float32x4 vr0 = v_setall_f32(r0), vr1 = v_setall_f32(r1), z = v_setzero_f32();
for( ; j <= bsz - 4; j += 4 )
{
const float* rptr = rowbuf0 + j*vsz_a;
v_float32x4 s0, s1;
if( cn0 == 0 )
{
s0 = v_setall_f32(bias0);
s1 = v_setall_f32(bias1);
}
else
{
s0 = v_load(outptr0 + j);
s1 = v_load(outptr1 + j);
}
v_float32x4 vs00 = v_setzero_f32(), vs01 = v_setzero_f32(),
vs02 = v_setzero_f32(), vs03 = v_setzero_f32(),
vs10 = v_setzero_f32(), vs11 = v_setzero_f32(),
vs12 = v_setzero_f32(), vs13 = v_setzero_f32();
for( k = 0; k < vsz; k += 4, rptr += 4 )
{
v_float32x4 w0 = v_load_aligned(wptr0 + k), w1 = v_load_aligned(wptr1 + k);
v_float32x4 r0 = v_load_aligned(rptr), r1 = v_load_aligned(rptr + vsz_a),
r2 = v_load_aligned(rptr + vsz_a*2), r3 = v_load_aligned(rptr + vsz_a*3);
vs00 += w0*r0;
vs01 += w0*r1;
vs02 += w0*r2;
vs03 += w0*r3;
vs10 += w1*r0;
vs11 += w1*r1;
vs12 += w1*r2;
vs13 += w1*r3;
}
s0 += v_reduce_sum4(vs00, vs01, vs02, vs03);
s1 += v_reduce_sum4(vs10, vs11, vs12, vs13);
if( relu )
{
s0 = v_select(s0 > z, s0, s0*vr0);
s1 = v_select(s1 > z, s1, s1*vr1);
}
v_store(outptr0 + j, s0);
v_store(outptr1 + j, s1);
}
#endif
for( ; j < bsz; j++ )
{
const float* rptr = rowbuf0 + j*vsz_a;
float s00, s10;
if( cn0 == 0 )
{
s00 = bias0;
s10 = bias1;
}
else
{
s00 = outptr0[j];
s10 = outptr1[j];
}
for( k = 0; k < vsz; k++ )
{
float r0 = rptr[k];
s00 += wptr0[k]*r0;
s10 += wptr1[k]*r0;
}
if( relu )
{
s00 = s00 > 0.f ? s00 : s00*r0;
s10 = s10 > 0.f ? s10 : s10*r1;
}
outptr0[j] = s00;
outptr1[j] = s10;
}
}
}
}
if( activ_ )
activ_->forwardSlice(data_out0 + stripeStart, data_out0 + stripeStart,
(int)(stripeEnd - stripeStart),
outPlaneSize, startOutCn, startOutCn + outCn);
}
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
CV_Assert(outputs.size() == 1);
for (int i = 0; i < inputs.size(); ++i)
CV_Assert(inputs[i].u != outputs[0].u);
if (umat_blobs.empty())
{
size_t n = blobs.size();
umat_blobs.resize(n);
for (size_t i = 0; i < n; i++)
{
blobs[i].copyTo(umat_blobs[i]);
}
}
if (convolutionOp.empty())
{
OCL4DNNConvConfig config;
config.in_shape = shape(inputs[0]);
config.out_shape = shape(outputs[0]);
config.kernel = kernel;
config.pad = pad;
config.stride = stride;
config.dilation = dilation;
config.group = inputs[0].size[1] / umat_blobs[0].size[1];
config.bias_term = (hasBias()) ? true : false;
config.use_half = use_half;
convolutionOp = Ptr<OCL4DNNConvSpatial<float> >(new OCL4DNNConvSpatial<float>(config));
}
int outCn = umat_blobs[0].size[0];
reluslope.clear();
if( activ )
{
Ptr<ReLULayer> activ_relu = activ.dynamicCast<ReLULayer>();
if( !activ_relu.empty() )
{
reluslope.assign(outCn+2, activ_relu->negativeSlope);
activType = OCL4DNN_CONV_FUSED_ACTIV_RELU;
}
Ptr<ReLU6Layer> activ_relu6 = activ.dynamicCast<ReLU6Layer>();
if( !activ_relu6.empty() )
{
reluslope.resize(2);
reluslope[0] = activ_relu6->minValue;
reluslope[1] = activ_relu6->maxValue;
activType = OCL4DNN_CONV_FUSED_ACTIV_RELU6;
}
Ptr<ChannelsPReLULayer> activ_chprelu = activ.dynamicCast<ChannelsPReLULayer>();
if( !activ_chprelu.empty() )
{
const Mat& m = activ_chprelu->blobs[0];
CV_Assert(m.isContinuous() && m.type() == CV_32F && (int)m.total() == outCn);
const float* mdata = m.ptr<float>();
reluslope.resize(outCn+2);
std::copy(mdata, mdata + outCn, reluslope.begin());
reluslope[outCn] = reluslope[outCn+1] = reluslope[outCn-1];
activType = OCL4DNN_CONV_FUSED_ACTIV_PRELU;
}
}
if ( newWeightAndBias )
{
weightsMat.copyTo(umat_blobs[0]);
if ( fusedBias )
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
umat_blobs[1] = UMat(biasvec, true);
}
convolutionOp->setBias(fusedBias || hasBias());
newWeightAndBias = false;
}
if ( newActiv )
{
if ( activType == OCL4DNN_CONV_FUSED_ACTIV_RELU )
{
CV_Assert(!reluslope.empty());
convolutionOp->setActivReLU(true, reluslope[0]);
}
else if ( activType == OCL4DNN_CONV_FUSED_ACTIV_PRELU)
{
CV_Assert(!reluslope.empty());
convolutionOp->setActivPReLU(true, reluslope);
}
else if ( activType == OCL4DNN_CONV_FUSED_ACTIV_POWER)
{
convolutionOp->setActivPower(true, power);
}
else if ( activType == OCL4DNN_CONV_FUSED_ACTIV_TANH)
{
convolutionOp->setActivTanh(true);
}
else if ( activType == OCL4DNN_CONV_FUSED_ACTIV_RELU6)
{
convolutionOp->setActivReLU6(true, reluslope[0], reluslope[1]);
}
else
{
convolutionOp->setActivReLU(false, 0);
convolutionOp->setActivPReLU(false, reluslope);
convolutionOp->setActivPower(false, 1.f);
convolutionOp->setActivTanh(false);
convolutionOp->setActivReLU6(false, 0, 0);
}
newActiv = false;
}
UMat& inpMat = inputs[0];
UMat& outMat = outputs[0];
int batch_size = inpMat.size[0];
return convolutionOp->Forward(inpMat,
inputs.size() == 2 ? inputs[1] : UMat(),
umat_blobs[0],
(hasBias() || fusedBias) ? umat_blobs[1] : UMat(),
outMat,
batch_size);
}
#endif
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());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
/*printf("conv %s: input (%d x %d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n",
name.c_str(), inputs[0]->size[0], inputs[0]->size[1], inputs[0]->size[2], inputs[0]->size[3],
kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);*/
CV_Assert_N(inputs.size() == (size_t)1, inputs[0]->size[1] % blobs[0].size[1] == 0,
outputs.size() == 1, inputs[0]->data != outputs[0].data);
int ngroups = inputs[0]->size[1]/blobs[0].size[1];
CV_Assert(outputs[0].size[1] % ngroups == 0);
int outCn = blobs[0].size[0];
reluslope.clear();
if( activ )
{
Ptr<ReLULayer> activ_relu = activ.dynamicCast<ReLULayer>();
if( !activ_relu.empty() )
{
reluslope.assign(outCn+2, activ_relu->negativeSlope);
}
Ptr<ChannelsPReLULayer> activ_chprelu = activ.dynamicCast<ChannelsPReLULayer>();
if( !activ_chprelu.empty() )
{
const Mat& m = activ_chprelu->blobs[0];
CV_Assert(m.isContinuous() && m.type() == CV_32F && (int)m.total() == outCn);
const float* mdata = m.ptr<float>();
reluslope.resize(outCn+2);
std::copy(mdata, mdata + outCn, reluslope.begin());
reluslope[outCn] = reluslope[outCn+1] = reluslope[outCn-1];
}
}
int nstripes = std::max(getNumThreads(), 1);
ParallelConv::run(*inputs[0], outputs[0], weightsMat, biasvec, reluslope,
kernel, pad, stride, dilation, activ.get(), ngroups, nstripes);
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size());
int64 flops = 0;
for (int i = 0; i < inputs.size(); i++)
{
flops += total(outputs[i])*(CV_BIG_INT(2)*kernel.area()*inputs[i][1] + 1);
}
return flops;
}
};
class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
Mat weightsMat, biasesMat;
UMat umat_weights;
UMat umat_biases;
DeConvolutionLayerImpl(const LayerParams& params) : BaseConvolutionLayerImpl(params) {}
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
int inpCn = inpShape[1];
int inpH = inpShape[2];
int inpW = inpShape[3];
int outCn = outShape[1];
int ngroups = inpCn / blobs[0].size[0];
int outGroupCn = outCn / ngroups;
int ksize = outGroupCn * kernel.height * kernel.width;
return shape(ksize, inpH * inpW);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(!hasBias() || blobs[1].total() == (size_t)numOutput);
CV_Assert(inputs.size() != 0);
int inpCn = inputs[0][1];
int inpH = inputs[0][2];
int inpW = inputs[0][3];
int outH = -1, outW = -1;
if (padMode.empty())
{
outH = stride.height * (inpH - 1) + kernel.height - 2 * pad.height + adjustPad.height;
outW = stride.width * (inpW - 1) + kernel.width - 2 * pad.width + adjustPad.width;
}
else if (padMode == "VALID")
{
outH = stride.height * (inpH - 1) + kernel.height + adjustPad.height;
outW = stride.width * (inpW - 1) + kernel.width + adjustPad.width;
}
else if (padMode == "SAME")
{
outH = stride.height * (inpH - 1) + 1 + adjustPad.height;
outW = stride.width * (inpW - 1) + 1 + adjustPad.width;
}
else
CV_Error(Error::StsError, "Unsupported padding mode " + padMode);
int outCn = numOutput;
CV_Assert(outCn % blobs[0].size[1] == 0);
int ngroups = outCn / blobs[0].size[1];
CV_Assert(inpCn % ngroups == 0 && outCn % ngroups == 0);
CV_Assert(blobs[0].size[0] == inpCn);
int dims[] = {inputs[0][0], outCn, outH, outW};
outputs.resize(inputs.size(), shape(dims, 4));
internals.push_back(MatShape());
if (!is1x1())
internals[0] = computeColRowShape(inputs[0], outputs[0]);
if (hasBias())
internals.push_back(shape(1, outH*outW));
return false;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs, outputs);
getConvPoolPaddings(Size(outputs[0].size[3], outputs[0].size[2]),
Size(inputs[0]->size[3], inputs[0]->size[2]),
kernel, stride, padMode, dilation, pad);
}
class MatMulInvoker : public ParallelLoopBody
{
public:
MatMulInvoker(const Mat& a, const Mat& b, Mat& c, int nstripes)
{
a_ = &a;
b_ = &b;
c_ = &c;
nstripes_ = nstripes;
useAVX = checkHardwareSupport(CPU_AVX);
useAVX2 = checkHardwareSupport(CPU_AVX2);
useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
}
void operator()(const Range& range_) const CV_OVERRIDE
{
int stripeSize = (int)alignSize((b_->cols + nstripes_ - 1)/nstripes_, 16);
Range range(range_.start*stripeSize, std::min(range_.end*stripeSize, b_->cols));
int mmax = a_->rows;
int nmax = range.end - range.start;
int kmax = a_->cols;
int m, n, k;
const float* aptr = a_->ptr<float>();
const float* bptr = b_->ptr<float>() + range.start;
float* cptr = c_->ptr<float>() + range.start;
size_t astep = a_->step1();
size_t bstep = b_->step1();
size_t cstep = c_->step1();
#if CV_TRY_AVX512_SKX
if( useAVX512 )
opt_AVX512_SKX::fastGEMM( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
#if CV_TRY_AVX2
if( useAVX2 )
opt_AVX2::fastGEMM( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
#if CV_TRY_AVX
if( useAVX )
opt_AVX::fastGEMM( aptr, astep, bptr, bstep, cptr, cstep, mmax, kmax, nmax );
else
#endif
for( m = 0; m < mmax; m += 2 )
{
float* dst0 = cptr + cstep*m;
float* dst1 = cptr + cstep*std::min(m+1, mmax-1);
const float* aptr0 = aptr + astep*m;
const float* aptr1 = aptr + astep*std::min(m+1, mmax-1);
for( n = 0; n < nmax; n++ )
{
dst0[n] = 0.f;
dst1[n] = 0.f;
}
for( k = 0; k < kmax; k += 4 )
{
float alpha00 = aptr0[k];
float alpha01 = aptr1[k];
float alpha10 = 0.f, alpha11 = 0.f;
float alpha20 = 0.f, alpha21 = 0.f;
float alpha30 = 0.f, alpha31 = 0.f;
const float* bptr0 = bptr + k*bstep;
const float* bptr1 = bptr0;
const float* bptr2 = bptr0;
const float* bptr3 = bptr0;
if( k+1 < kmax )
{
alpha10 = aptr0[k+1];
alpha11 = aptr1[k+1];
bptr1 = bptr0 + bstep;
if( k+2 < kmax )
{
alpha20 = aptr0[k+2];
alpha21 = aptr1[k+2];
bptr2 = bptr1 + bstep;
if( k+3 < kmax )
{
alpha30 = aptr0[k+3];
alpha31 = aptr1[k+3];
bptr3 = bptr2 + bstep;
}
}
}
n = 0;
#if CV_SIMD128
v_float32x4 a00 = v_setall_f32(alpha00);
v_float32x4 a01 = v_setall_f32(alpha01);
v_float32x4 a10 = v_setall_f32(alpha10);
v_float32x4 a11 = v_setall_f32(alpha11);
v_float32x4 a20 = v_setall_f32(alpha20);
v_float32x4 a21 = v_setall_f32(alpha21);
v_float32x4 a30 = v_setall_f32(alpha30);
v_float32x4 a31 = v_setall_f32(alpha31);
for( ; n <= nmax - 4; n += 4 )
{
v_float32x4 b0 = v_load(bptr0 + n);
v_float32x4 b1 = v_load(bptr1 + n);
v_float32x4 b2 = v_load(bptr2 + n);
v_float32x4 b3 = v_load(bptr3 + n);
v_float32x4 d0 = v_load(dst0 + n);
v_float32x4 d1 = v_load(dst1 + n);
d0 += b0*a00;
d1 += b0*a01;
d0 += b1*a10;
d1 += b1*a11;
d0 += b2*a20;
d1 += b2*a21;
d0 += b3*a30;
d1 += b3*a31;
v_store(dst0 + n, d0);
v_store(dst1 + n, d1);
}
#endif
for( ; n < nmax; n++ )
{
float b0 = bptr0[n], b1 = bptr1[n];
float b2 = bptr2[n], b3 = bptr3[n];
float d0 = dst0[n] + alpha00*b0 + alpha10*b1 + alpha20*b2 + alpha30*b3;
float d1 = dst1[n] + alpha01*b0 + alpha11*b1 + alpha21*b2 + alpha31*b3;
dst0[n] = d0;
dst1[n] = d1;
}
}
}
}
const Mat *a_, *b_;
Mat* c_;
int nstripes_;
bool useAVX;
bool useAVX2;
bool useAVX512;
};
class Col2ImInvoker : public cv::ParallelLoopBody
{
public:
const float* data_col;
const float* biasvec;
int channels, height, width;
int kernel_h, kernel_w;
int pad_h, pad_w;
int stride_h, stride_w;
float* data_im;
int height_col, width_col;
int nstripes;
bool is1x1;
Col2ImInvoker()
: data_col(0), biasvec(0), channels(0), height(0), width(0),
kernel_h(0), kernel_w(0), pad_h(0), pad_w(0), stride_h(0), stride_w(0), data_im(0),
height_col(0), width_col(0), nstripes(0), is1x1(0)
{}
static void run(const float* data_col,
int channels, int height, int width,
int kernel_h, int kernel_w,
int pad_h, int pad_w,
int stride_h, int stride_w,
int height_col, int width_col,
float* data_im,
const float* biasvec,
bool is1x1)
{
const int nstripes = getNumThreads();
Col2ImInvoker t;
t.data_col = data_col;
t.data_im = data_im;
t.channels = channels; t.height = height; t.width = width;
t.kernel_h = kernel_h; t.kernel_w = kernel_w;
t.pad_h = pad_h; t.pad_w = pad_w;
t.stride_h = stride_h; t.stride_w = stride_w;
t.height_col = height_col;
t.width_col = width_col;
t.nstripes = nstripes;
t.is1x1 = is1x1;
t.biasvec = biasvec;
parallel_for_(Range(0, nstripes), t, nstripes);
}
virtual void operator ()(const Range &r) const CV_OVERRIDE
{
const float* data_col_ = data_col;
float* data_im_ = data_im;
int coeff_h = (1 - stride_h * kernel_w * height_col) * width_col;
int coeff_w = (1 - stride_w * height_col * width_col);
size_t total = (size_t)channels * height * width;
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t startIndex = r.start*stripeSize;
size_t endIndex = std::min(r.end*stripeSize, total);
int w = (int)(startIndex % width + pad_w);
int h = (int)((startIndex / width) % height + pad_h);
int c = (int)(startIndex / (width * height));
int h_col_start = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
int h_col_end = std::min(h / stride_h + 1, height_col);
int plane_size_col = height_col * width_col;
int offset = (c * kernel_h * kernel_w + h * kernel_w + w) * plane_size_col;
bool is1x1_ = is1x1;
const float* biasvec_ = biasvec;
for (size_t index = startIndex; index < endIndex; index++)
{
// compute the start and end of the output
int w_col_start = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
int w_col_end = std::min(w / stride_w + 1, width_col);
float val;
if( is1x1_ )
val = data_im_[index];
else
{
val = 0.f;
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
val += data_col_[offset + h_col * coeff_h + w_col * coeff_w];
}
}
}
data_im_[index] = val + biasvec_[c];
offset += plane_size_col;
if( ++w >= width + pad_w )
{
w = (int)((index + 1)% width + pad_w);
h = (int)(((index + 1) / width) % height + pad_h);
c = (int)((index + 1) / (width * height));
h_col_start = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
h_col_end = std::min(h / stride_h + 1, height_col);
offset = (c * kernel_h * kernel_w + h * kernel_w + w) * plane_size_col;
}
}
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
if (inputs_.depth() == CV_16S)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
int outCn = numOutput;
int inpCn = inputs[0].size[1];
if (is1x1())
return false;
if (umat_weights.empty())
{
transpose(blobs[0].reshape(1, inpCn), umat_weights);
umat_biases = hasBias() ? blobs[1].reshape(1, outCn).getUMat(ACCESS_READ) :
UMat::zeros(outCn, 1, CV_32F);
}
String buildopt = format("-DT=%s ", ocl::typeToStr(inputs[0].type()));
buildopt += format("-DPAD_H=%d -DPAD_W=%d -DKERNEL_H=%d -DKERNEL_W=%d -DSTRIDE_H=%d -DSTRIDE_W=%d ",
pad.height, pad.width, kernel.height, kernel.width, stride.height, stride.width);
for (size_t ii = 0; ii < outputs.size(); ii++)
{
int ngroups = outCn / blobs[0].size[1];
int inpGroupCn = inpCn / ngroups;
int outGroupCn = blobs[0].size[1];
const UMat& inp = inputs[ii];
UMat& out = outputs[ii];
int numImg = inp.size[0];
int inpH = inp.size[2], inpW = inp.size[3];
int outH = out.size[2], outW = out.size[3];
MatShape inpshape = shape(numImg*inpCn, inpH*inpW);
MatShape outshape = shape(numImg*outCn, outH*outW);
UMat convBlob = inputs[ii].reshape(1, inpshape.size(), &inpshape[0]);
UMat decnBlob = out.reshape(1, outshape.size(), &outshape[0]);
int rows = internals[0].rows / ngroups;
for (int n = 0; n < numImg; n++)
{
for (int g = 0; g < ngroups; g++)
{
UMat colMat = internals[0].rowRange(_Range(g * rows, rows));
UMat convMat = convBlob.rowRange(_Range((g + n * ngroups) * inpGroupCn, inpGroupCn));
UMat wghtMat = umat_weights.colRange(_Range(g * inpGroupCn, inpGroupCn));
gemm(wghtMat, convMat, 1, noArray(), 0, colMat, 0);
}
for (int g = 0; g < ngroups; g++)
{
int total = outGroupCn * decnBlob.cols;
int index = 0;
int height_col = inpH;
int width_col = inpW;
int coeff_h = (1 - stride.height * kernel.width * height_col) * width_col;
int coeff_w = (1 - stride.width * height_col * width_col);
ocl::Kernel k("col2im", ocl::dnn::col2im_oclsrc, buildopt);
k.set(index++, total);
k.set(index++, ocl::KernelArg::PtrReadOnly(internals[0]));
k.set(index++, (int)(g * rows * internals[0].cols));
k.set(index++, outGroupCn);
k.set(index++, outH);
k.set(index++, outW);
k.set(index++, height_col);
k.set(index++, width_col);
k.set(index++, coeff_h);
k.set(index++, coeff_w);
k.set(index++, ocl::KernelArg::PtrReadOnly(umat_biases));
k.set(index++, (int)(g * outGroupCn * umat_biases.cols));
k.set(index++, ocl::KernelArg::PtrWriteOnly(decnBlob));
k.set(index++, (int)((g + n * ngroups) * outGroupCn * decnBlob.cols));
size_t global[] = { (size_t)total };
bool ret = k.run(1, global, NULL, false);
if (!ret)
return false;
}
}
}
return true;
}
#endif
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());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat *> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
int outCn = numOutput;
int inpCn = inputs[0]->size[1];
bool is1x1flag = is1x1();
int nstripes = getNumThreads();
if( weightsMat.empty() )
{
transpose(blobs[0].reshape(1, inpCn), weightsMat);
biasesMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat::zeros(outCn, 1, CV_32F);
}
for (size_t ii = 0; ii < outputs.size(); ii++)
{
int ngroups = outCn / blobs[0].size[1];
int inpGroupCn = inpCn / ngroups;
int outGroupCn = blobs[0].size[1];
const Mat& inp = *inputs[ii];
Mat& out = outputs[ii];
int numImg = inp.size[0];
int inpH = inp.size[2], inpW = inp.size[3];
int outH = out.size[2], outW = out.size[3];
Mat convBlob = inputs[ii]->reshape(1, numImg*inpCn);
Mat decnBlob = out.reshape(1, numImg*outCn);
for (int n = 0; n < numImg; n++)
{
for (int g = 0; g < ngroups; g++)
{
Mat dstMat = decnBlob.rowRange(_Range((g + n * ngroups) * outGroupCn, outGroupCn));
Mat &colMat = is1x1flag ? dstMat : internals[0];
Mat convMat = convBlob.rowRange(_Range((g + n * ngroups) * inpGroupCn, inpGroupCn));
Mat wghtMat = weightsMat.colRange(_Range(g * inpGroupCn, inpGroupCn));
Mat curBiasMat = biasesMat.rowRange(_Range(g * outGroupCn, outGroupCn));
//gemm(wghtMat, convMat, 1, colMat, 0, colMat, 0);
MatMulInvoker mminvoker(wghtMat, convMat, colMat, nstripes);
parallel_for_(Range(0, nstripes), mminvoker, nstripes);
Col2ImInvoker::run(colMat.ptr<float>(), outGroupCn, outH, outW,
kernel.height, kernel.width, pad.height, pad.width,
stride.height, stride.width, inpH, inpW, dstMat.ptr<float>(),
curBiasMat.ptr<float>(), is1x1flag);
}
}
}
}
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_HALIDE
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
int inW, inH, inC, inN;
getCanonicalSize(inputBuffer, &inW, &inH, &inC, &inN);
const int outGroupCn = blobs[0].size[1];
const int group = numOutput / outGroupCn;
const int inpGroupCn = blobs[0].size[0] / group;
Halide::Var x("x"), y("y"), c("c"), n("n");
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
Halide::Func padded_input(name + "_constant_exterior");
auto weights = wrapToHalideBuffer(blobs[0]);
Halide::Func dilated_input("dilated_input");
dilated_input(x, y, c, n) = 0.0f;
Halide::RDom r1(0, inW, 0, inH);
dilated_input(r1.x * stride.width, r1.y * stride.height, c, n) =
inputBuffer(r1.x, r1.y, c, n);
dilated_input.compute_root();
Halide::Func bounded =
Halide::BoundaryConditions::constant_exterior(dilated_input, 0,
0, (inW - 1) * stride.width + 1,
0, (inH - 1) * stride.height + 1,
0, inC, 0, inN);
padded_input(x, y, c, n) = bounded(x, y, c, n);
Halide::RDom r(0, kernel.width, 0, kernel.height, 0, inpGroupCn);
Halide::Expr kx = x + pad.width - r.x;
Halide::Expr ky = y + pad.height - r.y;
Halide::Expr kInC = r.z;
Halide::Expr kOutC = c;
for (int i = 1; i < group; ++i)
{
kInC = select(c < outGroupCn * i, kInC, inpGroupCn * i + r.z);
kOutC = select(c < outGroupCn * i, kOutC, c - outGroupCn * i);
}
Halide::Expr topExpr = sum(padded_input(kx, ky, kInC, n) *
weights(r.x, r.y, kOutC, kInC));
if (hasBias())
{
auto bias = wrapToHalideBuffer(blobs[1], {numOutput});
topExpr += bias(c);
}
top(x, y, c, n) = topExpr;
return Ptr<BackendNode>(new HalideBackendNode({ padded_input, top }));
#endif // HAVE_HALIDE
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Deconvolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::DeconvolutionLayer> ieLayer(new InferenceEngine::DeconvolutionLayer(lp));
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_out_depth = numOutput;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (hasBias())
{
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size());
float flops = 0;
int outChannels = blobs[0].size[0];
for (int i = 0; i < inputs.size(); i++)
{
flops += CV_BIG_INT(2)*outChannels*kernel.area()*total(inputs[i]);
}
return flops;
}
};
Ptr<BaseConvolutionLayer> ConvolutionLayer::create(const LayerParams ¶ms)
{
Ptr<ConvolutionLayerImpl> l(new ConvolutionLayerImpl(params));
return l;
}
Ptr<BaseConvolutionLayer> DeconvolutionLayer::create(const LayerParams ¶ms)
{
return Ptr<BaseConvolutionLayer>(new DeConvolutionLayerImpl(params));
}
}
}