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opencv/modules/dnn/src/layers/convolution_layer.cpp
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Hanxi Guo 1fcf7ba5bc Merge pull request #20406 from MarkGHX:gsoc_2021_webnn 
[GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Fix the build issue

* Update concat_layer.cpp

Still have some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Delete bib19450.aux

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Update dnn.cpp

* Fix Error in dnn.cpp

* Resolve duplication in conditions in convolution_layer.cpp

* Fixed the issues in the comments

* Fix building issue

* Update tutorial

* Fixed comments

* Address the comments

* Update CMakeLists.txt

* Offer more accurate perf test on native

* Add better perf tests for both native and web

* Modify per tests for better results

* Use more latest version of Electron

* Support latest WebNN Clamp op

* Add definition of HAVE_WEBNN macro

* Support group convolution

* Implement Scale_layer using WebNN

* Add Softmax option for native classification example

* Fix comments

* Fix comments
2021-11-23 21:15:31 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_cuda.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include "../op_webnn.hpp"
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
#include <numeric>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
using namespace cv::dnn::ocl4dnn;
#endif
#ifdef HAVE_TENGINE
#include "../tengine4dnn/include/tengine_graph_convolution.hpp"
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/convolution.hpp"
#include "../cuda4dnn/primitives/transpose_convolution.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv
{
namespace dnn
{
class BaseConvolutionLayerImpl : public ConvolutionLayer
{
public:
bool fusedWeights, fusedBias;
std::vector<double> weightsMultipliers;
#ifdef HAVE_WEBNN
int groups;
#endif
BaseConvolutionLayerImpl(const LayerParams &params)
{
setParamsFrom(params);
getConvolutionKernelParams(params, kernel_size, pads_begin, pads_end, strides, dilations, padMode, adjust_pads);
numOutput = params.get<int>("num_output");
int ngroups = params.get<int>("group", 1);
#ifdef HAVE_WEBNN
groups = ngroups;
#endif
CV_Assert(numOutput % ngroups == 0);
if (kernel_size.size() == 2) {
kernel = Size(kernel_size[1], kernel_size[0]);
stride = Size(strides[1], strides[0]);
for (int i = 0; i < pads_begin.size(); i++) {
if (pads_begin[i] != pads_end[i])
CV_Error(Error::StsNotImplemented, "Unsupported asymmetric padding in convolution layer");
}
pad = Size(pads_begin[1], pads_begin[0]);
dilation = Size(dilations[1], dilations[0]);
adjustPad.height = adjust_pads[0];
adjustPad.width = adjust_pads[1];
}
for (int i = 0; i < adjust_pads.size(); i++) {
CV_Assert(adjust_pads[i] < strides[i]);
}
fusedWeights = false;
fusedBias = false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
CV_Assert((inputs.size() > outputs.size() && blobs.empty()) ||
(!inputs.empty() && (blobs.size() == 1 || blobs.size() == 2)));
MatSize weightShape = blobs.empty() ? inputs[1].size : blobs[0].size;
CV_Assert(inputs[0].dims == outputs[0].dims);
if (weightShape.dims() == 3)
{
kernel_size.assign(1, kernel_size[0]);
strides.assign(1, strides[0]);
dilations.assign(1, dilations[0]);
pads_begin.assign(1, pads_begin[0]);
pads_end.assign(1, pads_end[0]);
}
CV_Assert(weightShape.dims() == kernel_size.size() + 2);
for (int i = 0; i < kernel_size.size(); i++) {
CV_Assert(weightShape[i + 2] == kernel_size[i]);
}
const Mat &input = inputs[0];
CV_Assert(((input.dims == 3 && kernel_size.size() == 1) || input.dims == 4 || input.dims == 5) && (input.type() == CV_32F || input.type() == CV_16S));
for (size_t i = 0; i < outputs.size(); i++)
{
CV_Assert(inputs[i].type() == input.type());
CV_Assert(((input.dims == 3 && kernel_size.size() == 1) || inputs[i].dims == 4 || inputs[i].dims == 5) && inputs[i].size[1] == input.size[1]);
for (int j = 0; j < inputs[i].dims; j++) {
CV_Assert(inputs[i].size[j] == input.size[j]);
}
}
std::vector<int> inpShape;
std::vector<int> outShape;
for (int i = 2; i < inputs[0].dims; i++) {
inpShape.push_back(inputs[0].size[i]);
outShape.push_back(outputs[0].size[i]);
}
getConvPoolPaddings(inpShape, kernel_size, strides, padMode, pads_begin, pads_end);
if (pads_begin.size() == 2) {
for (int i = 0; i < pads_begin.size(); i++) {
if (pads_begin[i] != pads_end[i])
CV_Error(Error::StsNotImplemented, "Unsupported asymmetric padding in convolution layer");
}
pad = Size(pads_begin[1], pads_begin[0]);
}
fusedWeights = false;
fusedBias = false;
}
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 bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
Ptr<BlankLayer> blank_layer = top.dynamicCast<BlankLayer>();
if (blank_layer)
return true;
Mat w, b;
top->getScaleShift(w, b);
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
fusedWeights = fusedWeights || !w.empty();
fusedBias = fusedBias || (hasBias() && !w.empty()) || !b.empty();
return true;
}
return false;
}
virtual void fuseWeights(const Mat& w_, const Mat& b_) = 0;
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<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
std::vector<UMat> umat_blobs;
bool newActiv;
ocl4dnnFusedActiv_t activType;
float power;
#endif
#ifdef HAVE_TENGINE
teng_graph_t tengine_graph;
#endif
#ifdef HAVE_CUDA
cuda4dnn::ConvolutionConfiguration::FusionMode cudaFusionMode;
cuda4dnn::ConvolutionConfiguration::ActivationType cudaActType;
float cuda_relu_slope, cuda_crelu_floor, cuda_crelu_ceil;
float cuda_power_exp, cuda_power_scale, cuda_power_shift;
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
#ifdef HAVE_OPENCL
newActiv = false;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
power = 0.f;
#endif
#ifdef HAVE_CUDA
cudaFusionMode = cuda4dnn::ConvolutionConfiguration::FusionMode::NONE;
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY;
#endif
#ifdef HAVE_TENGINE
tengine_graph=NULL;
#endif
}
#ifdef HAVE_TENGINE
~ConvolutionLayerImpl()
{
if(NULL != tengine_graph )
{
tengine_release(tengine_graph);
}
}
#endif
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
CV_Assert(!blobs.empty());
int dims = inpShape.size();
int inpD = dims == 5 ? inpShape[2] : 1;
int inpH = inpShape[dims - 2];
int inpW = inpShape.back();
int inpGroupCn = blobs[0].size[1];
int ksize = inpGroupCn * std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>());
return shape(inpD * inpH * inpW, ksize);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
size_t ksize = kernel_size.size();
#ifdef HAVE_CUDA
if (backendId == DNN_BACKEND_CUDA)
{
/* only 1d, 2d and 3d convolutions supported */
if (ksize > 0 && ksize <= 3)
return true;
return false;
}
#endif
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
if (isArmTarget && blobs.empty())
return false;
if (ksize == 1)
return isArmTarget;
if (ksize == 3)
return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || !isMyriad) && blobs.empty())
return false;
return (!isMyriad || dilation.width == dilation.height);
}
#endif
if (backendId == DNN_BACKEND_OPENCV)
return ksize >= 1 && ksize <= 3;
#ifdef HAVE_HALIDE
if (backendId == DNN_BACKEND_HALIDE)
return ksize == 2 && !blobs.empty();
#endif
#ifdef HAVE_VULKAN
if (backendId == DNN_BACKEND_VKCOM)
return ksize == 2;
#endif
#ifdef HAVE_WEBNN
if (backendId == DNN_BACKEND_WEBNN)
{
if (ksize != 2)
{
CV_LOG_WARNING(NULL, "WebNN only supports Conv2d.");
return false;
}
return true;
}
#endif
return false;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(!blobs.empty() || inputs.size() > 1);
const int* weightShape = blobs.empty() ? &inputs[1][0] : blobs[0].size.p;
CV_Assert(!hasBias() || blobs[1].total() == (size_t)weightShape[0]);
internals.clear();
CV_Assert(inputs.size() != 0);
std::vector<int> inpShape(inputs[0].begin() + 2, inputs[0].end());
int outCn = weightShape[0];
std::vector<int> outShape;
outShape.push_back(inputs[0][0]);
outShape.push_back(outCn);
int inpCn = inputs[0][1];
if (padMode.empty())
{
for (int i = 0; i < inpShape.size(); i++)
outShape.push_back((inpShape[i] + pads_begin[i] + pads_end[i] - dilations[i] * (kernel_size[i] - 1) - 1) / strides[i] + 1);
}
else
{
getConvPoolOutParams(inpShape, kernel_size, strides, padMode, dilations, outShape);
}
int ngroups = inpCn / weightShape[1];
if (ngroups == 0 || ngroups * weightShape[1] != inpCn)
CV_Error(Error::StsError, format("Number of input channels should "
"be multiple of %d but got %d", weightShape[1], inpCn));
CV_Assert(ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0);
outputs.resize(1, outShape);
return false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs_arr, outputs_arr);
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
// 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
if (!blobs.empty())
{
Mat wm = blobs[0].reshape(1, numOutput);
if( wm.step1() % VEC_ALIGN != 0 )
{
int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_buffer = Mat(numOutput, 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;
}
else
{
// initialized in .forward()
weightsMat.release();
}
weightsMultipliers.assign(numOutput, 1.0);
Mat biasMat = hasBias() ? blobs[1].reshape(1, numOutput) : Mat();
biasvec.resize(numOutput+2);
if( biasMat.empty() )
{
for(int i = 0; i < numOutput; i++ )
biasvec[i] = 0.f;
}
else
{
for(int i = 0; i < numOutput; i++ )
biasvec[i] = biasMat.at<float>(i);
}
#ifdef HAVE_TENGINE
if(NULL != tengine_graph )
{
tengine_release(tengine_graph);
tengine_graph = NULL ;
}
#endif
#ifdef HAVE_OPENCL
convolutionOp.release();
#endif
}
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
if ((!activ.empty() && !layer.empty()) || blobs.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 != 1.0f) // not supported well by implementation, #17964
{
// FIXIT no way to check number of blobs (like, eltwise input)
CV_LOG_DEBUG(NULL, "DNN/OpenCL: can't configure Power activation (scale != 1.0f)");
activ.release();
newActiv = false;
return false;
}
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
#ifdef HAVE_CUDA
if (activ.empty())
{
/* setActivation was called with empty argument => reset all fusions */
cudaFusionMode = cuda4dnn::ConvolutionConfiguration::FusionMode::NONE;
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY;
}
if(IS_DNN_CUDA_TARGET(preferableTarget))
{
CV_Assert(cudaFusionMode == ConvolutionConfiguration::FusionMode::NONE ||
cudaFusionMode == ConvolutionConfiguration::FusionMode::ELTWISE_SUM);
Ptr<ReLULayer> activ_relu = activ.dynamicCast<ReLULayer>();
if(!activ_relu.empty())
{
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::RELU;
cuda_relu_slope = activ_relu->negativeSlope;
}
Ptr<ReLU6Layer> activ_relu6 = activ.dynamicCast<ReLU6Layer>();
if(!activ_relu6.empty())
{
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::CLIPPED_RELU;
cuda_crelu_floor = activ_relu6->minValue;
cuda_crelu_ceil = activ_relu6->maxValue;
}
Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
if (!activ_power.empty())
{
cuda_power_scale = activ_power->scale;
cuda_power_shift = activ_power->shift;
cuda_power_exp = activ_power->power;
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::POWER;
}
Ptr<TanHLayer> activ_tanh = activ.dynamicCast<TanHLayer>();
if(!activ_tanh.empty())
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::TANH;
Ptr<SigmoidLayer> activ_sigmoid = activ.dynamicCast<SigmoidLayer>();
if(!activ_sigmoid.empty())
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::SIGMOID;
Ptr<SwishLayer> activ_swish = activ.dynamicCast<SwishLayer>();
if(!activ_swish.empty())
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::SWISH;
Ptr<MishLayer> activ_mish = activ.dynamicCast<MishLayer>();
if(!activ_mish.empty())
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::MISH;
if (cudaActType == cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY)
{
/* no activation fused */
activ.reset();
}
else
{
/* activation was fused */
if (cudaFusionMode == ConvolutionConfiguration::FusionMode::NONE) /* no previous fusion */
cudaFusionMode = ConvolutionConfiguration::FusionMode::ACTIVATION; /* now activation */
else if (cudaFusionMode == ConvolutionConfiguration::FusionMode::ELTWISE_SUM) /* previously eltwise was fused */
cudaFusionMode = ConvolutionConfiguration::FusionMode::ELTWISE_SUM_THEN_ACTIVATION; /* now activation on eltwise output */
}
}
#endif
return !activ.empty();
}
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
#ifdef HAVE_CUDA
if(IS_DNN_CUDA_TARGET(preferableTarget))
{
Ptr<EltwiseLayer> eltwise = top.dynamicCast<EltwiseLayer>();
if (!eltwise.empty()) // && eltwise->op == EltwiseLayer::SUM && eltwise->coeffs.empty())
{
/* we also need to check that the eltwise input does not require shortcut mechanism
* it's difficult to verify it here but we hope that `fuseLayers` has done the check already
*/
if (cudaFusionMode == ConvolutionConfiguration::FusionMode::NONE)
{
/* no previous fusion */
cudaFusionMode = ConvolutionConfiguration::FusionMode::ELTWISE_SUM; /* now eltwise */
return true;
}
else if(cudaFusionMode == ConvolutionConfiguration::FusionMode::ACTIVATION)
{
/* previously an activation was fused */
cudaFusionMode = ConvolutionConfiguration::FusionMode::ACTIVATION_THEN_ELTWISE_SUM;
return true;
}
return false;
}
}
#endif
return BaseConvolutionLayerImpl::tryFuse(top);
}
void fuseWeights(const Mat& w_, const Mat& b_) CV_OVERRIDE
{
// 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];
Mat w = w_.total() == 1 ? Mat(1, outCn, CV_32F, Scalar(w_.at<float>(0))) : w_;
Mat b = b_.total() == 1 ? Mat(1, outCn, CV_32F, Scalar(b_.at<float>(0))) : b_;
CV_Assert_N(!weightsMat.empty(), biasvec.size() == outCn + 2,
w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
if (!w.empty())
{
// Keep origin weights unchanged.
if (weightsMat.data == blobs[0].data)
weightsMat = weightsMat.clone();
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);
}
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
#ifdef HAVE_VULKAN
int out_channel = blobs[0].size[0];
bool has_bias = hasBias() || fusedBias;
int filter_size[2] = {kernel.height, kernel.width};
int pad_size[2] = {pad.height, pad.width};
int stride_size[2] = {stride.height, stride.width};
int dilation_size[2] = {dilation.height, dilation.width};
int activation = 0;
vkcom::Tensor input_tensor = VkComTensor(inputs[0]);
int in_channel = input_tensor.dimSize(1);
int group = in_channel / blobs[0].size[1];
// TODO: support group > 1
if (group != 1)
return Ptr<BackendNode>();
int padding_mode;
if (padMode.empty())
{
padding_mode = vkcom::kPaddingModeCaffe;
}
else if (padMode == "VALID")
{
padding_mode = vkcom::kPaddingModeValid;
}
else if (padMode == "SAME")
{
padding_mode = vkcom::kPaddingModeSame;
}
else
CV_Error(Error::StsError, "Unsupported padding mode " + padMode);
std::shared_ptr<vkcom::OpBase> op(new vkcom::OpConv(out_channel, has_bias,
filter_size, pad_size,
stride_size, dilation_size,
activation, group,
padding_mode));
std::vector<Ptr<BackendWrapper> > blobsWrapper;
if (fusedWeights)
{
Mat wm;
weightsMat.copyTo(wm); // to handle the case of isContinuous() == false
wm = wm.reshape(1, blobs[0].dims, blobs[0].size);
blobsWrapper.push_back(Ptr<BackendWrapper>(new VkComBackendWrapper(wm)));
}
else
{
blobsWrapper.push_back(Ptr<BackendWrapper>(new VkComBackendWrapper(blobs[0])));
}
if (has_bias)
{
Mat biasesMat({out_channel}, CV_32F, &biasvec[0]);
blobsWrapper.push_back(Ptr<BackendWrapper>(new VkComBackendWrapper(biasesMat)));
}
return Ptr<BackendNode>(new VkComBackendNode(inputs, op, blobsWrapper));
#endif // HAVE_VULKAN
return Ptr<BackendNode>();
}
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>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
CV_Assert(dims.size() == 4 || dims.size() == 5);
const int inpCn = dims[1];
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn;
InferenceEngine::Layout layout = (dims.size() == 4) ? InferenceEngine::Layout::OIHW :
InferenceEngine::Layout::NCDHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(cvWeights, layout);
}
else
{
ieWeights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32,
ieWeights->getTensorDesc().getDims(), layout
});
ieWeights->allocate();
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
}
}
InferenceEngine::Blob::Ptr ieBiases;
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
ieLayer.setPaddingsEnd(pads_end);
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)outCn);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", ieWeights, l);
if (ieBiases)
addConstantData("biases", ieBiases, l);
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, "");
std::shared_ptr<ngraph::Node> ieWeights = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
if (nodes.size() > 1)
CV_Assert(ieWeights); // dynamic_cast should not fail
const int inpCn = dims[1];
const int inpGroupCn = nodes.size() > 1 ? ieWeights->get_shape()[1] : blobs[0].size[1];
const int group = inpCn / inpGroupCn;
std::vector<size_t> kernel_shape;
if (group != 1)
{
kernel_shape.push_back(group);
}
kernel_shape.push_back(numOutput / group);
kernel_shape.push_back(inpCn / group);
std::copy(kernel_size.begin(), kernel_size.end(), back_inserter(kernel_shape));
if (nodes.size() == 1)
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
}
else
{
Mat newWeights;
Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / numOutput);
cvWeights.copyTo(newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
}
}
}
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
}
ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT;
if (!padMode.empty())
pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER;
std::shared_ptr<ngraph::Node> conv_node;
if (group != 1) {
conv_node = std::make_shared<ngraph::op::v1::GroupConvolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
pad_type);
} else {
conv_node = std::make_shared<ngraph::op::v1::Convolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
pad_type);
}
if (hasBias() || fusedBias || nodes.size() == 3)
{
std::vector<size_t> shape(conv_node->get_shape().size(), 1);
shape[1] = conv_node->get_shape()[1];
std::shared_ptr<ngraph::Node> bias;
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
}
else
{
bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), biasvec.data());
}
auto conv_bias = std::make_shared<ngraph::op::v1::Add>(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_bias));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_node));
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_WEBNN
virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
auto& webnnInpOperand = node->operand;
auto& webnnGraphBuilder = node->net->builder;
ml::Operand webnnWeights = nodes.size() > 1 ? nodes[1].dynamicCast<WebnnBackendNode>()->operand : nullptr;
if (nodes.size() > 1)
CV_Assert(webnnWeights);
const int inpCn = weightsMat.total()/(kernel_size[0]*kernel_size[1]*numOutput);
const int group = groups;
const int inpGroupCn = inpCn / group;
std::vector<int32_t> kernel_shape;
if (group != 1)
{
kernel_shape.push_back(group);
}
kernel_shape.push_back(numOutput / group);
kernel_shape.push_back(inpGroupCn);
std::copy(kernel_size.begin(), kernel_size.end(), back_inserter(kernel_shape));
if (nodes.size() == 1)
{
webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(blobs[0]), blobs[0].data, blobs[0].total()*blobs[0].elemSize(), ml::OperandType::Float32);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(weightsMat), weightsMat.data, weightsMat.total()*weightsMat.elemSize(), ml::OperandType::Float32);
}
else
{
Mat newWeights;
Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / numOutput);
cvWeights.copyTo(newWeights);
webnnWeights = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(newWeights), newWeights.data, newWeights.total()*newWeights.elemSize(), ml::OperandType::Float32);
}
}
}
else
{
webnnWeights = webnnGraphBuilder.Reshape(webnnWeights, kernel_shape.data(), kernel_shape.size());
}
ml::AutoPad pad_type = ml::AutoPad::Explicit;
if (!padMode.empty())
pad_type = padMode == "VALID" ? ml::AutoPad::Explicit : ml::AutoPad::SameUpper;
ml::Conv2dOptions options = {};
options.groups = group;
options.autoPad = pad_type;
std::vector<int32_t> Strides(strides.begin(), strides.end());
if (!Strides.empty())
{
options.stridesCount = Strides.size();
options.strides = Strides.data();
}
std::vector<int32_t> Padding;
if (padMode.empty())
{
Padding = {static_cast<int32_t>(pads_begin[0]),
static_cast<int32_t>(pads_end[0]),
static_cast<int32_t>(pads_begin[1]),
static_cast<int32_t>(pads_end[1])};
}
else if (padMode == "VALID")
{
Padding = {0, 0, 0, 0};
}
if (!Padding.empty())
{
options.paddingCount = Padding.size();
options.padding = Padding.data();
}
std::vector<int32_t> Dilations(dilations.begin(), dilations.end());
if (!Dilations.empty())
{
options.dilationsCount = Dilations.size();
options.dilations = Dilations.data();
}
ml::Operand operand = webnnGraphBuilder.Conv2d(webnnInpOperand, webnnWeights, &options);
// ml::Operand result = operand;
if (hasBias() || fusedBias || nodes.size() == 3)
{
ml::Operand webnnBias = nullptr;
if (nodes.size() == 3)
{
std::vector<int32_t> bias_shape = {1, numOutput, 1, 1};
webnnBias = webnnGraphBuilder.Reshape(nodes[2].dynamicCast<WebnnBackendNode>()->operand, bias_shape.data(), bias_shape.size());
}
else
{
webnnBias = webnn::BuildConstant(webnnGraphBuilder, {1, numOutput, 1, 1}, biasvec.data(), (numOutput) * sizeof(float), ml::OperandType::Float32);
}
operand = webnnGraphBuilder.Add(operand, webnnBias);
}
return Ptr<BackendNode>(new WebnnBackendNode(operand));
}
#endif // HAVE_WEBNN
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]; // used only for conv2d
std::vector<size_t> kernel_size, pads_begin, pads_end, strides, dilations;
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;
bool useRVV;
int blk_size_cn;
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), useRVV(false)
, blk_size_cn(0)
{}
static void run( const Mat& input, Mat& output, const Mat& weights,
const std::vector<float>& biasvec,
const std::vector<float>& reluslope,
const std::vector<size_t>& kernel_size, const std::vector<size_t>& strides,
const std::vector<size_t>& pads_begin, const std::vector<size_t>& pads_end,
const std::vector<size_t>& dilations,
const ActivationLayer* activ, int ngroups, int nstripes )
{
size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>());
bool isConv1D = input.dims == 3;
bool isConv2D = input.dims == 4;
bool isConv3D = input.dims == 5;
CV_CheckEQ(static_cast<int>(kernel_size.size()), input.dims - 2, "");
CV_Assert_N(input.dims == output.dims,
input.size[0] == output.size[0],
weights.rows == output.size[1],
weights.cols == (input.size[1]/ngroups)*karea,
input.type() == output.type(),
input.type() == weights.type(),
input.type() == CV_32FC1,
input.isContinuous(),
output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2);
CV_Check(weights.step1(), weights.step1() % VEC_ALIGN == 0, "");
CV_CheckType(weights.type(), CV_32FC1, "");
ParallelConv p;
p.input_ = &input;
p.weights_ = &weights;
p.output_ = &output;
int max_ind = isConv1D? 3: 4;
for( int i = 0; i < max_ind; i++ ) p.outShape[i] = output.size[i];
p.outShape[1] /= ngroups;
p.kernel_size = kernel_size; p.strides = strides; p.dilations = dilations;
p.pads_begin = pads_begin; p.pads_end = pads_end;
p.ngroups_ = ngroups;
p.nstripes_ = nstripes;
int inpCnAll = input.size[1];
int depth = (input.dims == 5) ? input.size[2] : 1;
int width = input.size[input.dims - 1];
int height = isConv1D? 1 : input.size[input.dims - 2];
int inpCn = inpCnAll / ngroups;
p.is1x1_ = (isConv2D && kernel_size[0] == 1 && kernel_size[1] == 1 &&
pads_begin[0] == 0 && pads_begin[1] == 0) ||
(isConv1D && pads_begin[0] == 0 && kernel_size[0] == 1);
p.useAVX = checkHardwareSupport(CPU_AVX) && isConv2D;
p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D;
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D;
p.useRVV = checkHardwareSupport(CPU_RVV) && isConv2D;
int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = isConv1D? 1 : kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back();
int blk_size_cn0 = cvCeil(800./(kernel_w*kernel_h));
int ncn = 16;
while (ncn*2 < blk_size_cn0 && ncn < inpCn)
ncn *= 2;
ncn = std::min(ncn, inpCn);
p.blk_size_cn = ncn;
int dil_d = isConv3D? dilations[0] : 1;
int dil_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dil_w = dilations.back();
p.ofstab_.resize(karea * ncn);
int* ofstab = &p.ofstab_[0];
if (isConv1D)
{
for( int k = 0; k < ncn; k++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[k*kernel_w + k_c] = k*width + k_c*dil_w;
}
else if (isConv2D)
{
for( int k = 0; k < ncn; k++ )
for( int k_r = 0; k_r < kernel_h; k_r++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[(k*kernel_h + k_r)*kernel_w + k_c] =
(k*height + k_r*dil_h)*width + k_c*dil_w;
}
else
{
for( int k = 0; k < ncn; k++ )
for (int k_d = 0; k_d < kernel_d; k_d++)
for( int k_r = 0; k_r < kernel_h; k_r++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[(k*kernel_d*kernel_h + k_d*kernel_h + k_r)*kernel_w + k_c] =
(k*depth*height + k_d*dil_d*height + k_r*dil_h)*width + k_c*dil_w;
}
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;
bool isConv1D = input_->dims == 3;
bool isConv2D = input_->dims == 4;
bool isConv3D = input_->dims == 5;
int outW = output_->size[output_->dims - 1];
int outH = isConv1D? 1 : output_->size[output_->dims - 2];
int outCn = output_->size[1]/ngroups;
int depth = isConv3D? input_->size[2] : 1;
int height = isConv1D? 1 : input_->size[input_->dims - 2];
int width = input_->size[input_->dims - 1];
int inpCn = input_->size[1]/ngroups;
const int nstripes = nstripes_;
int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = isConv1D? 1 : kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back();
int karea = kernel_w*kernel_h*kernel_d;
int pad_d = isConv3D? pads_begin[0] : 0;
int pad_t = isConv1D? 0 : pads_begin[pads_begin.size() - 2];
int pad_l = pads_begin.back();
int stride_d = isConv3D? strides[0] : 0;
int stride_h = isConv1D? 0 : strides[strides.size() - 2];
int stride_w = strides.back();
int dilation_d = isConv3D? dilations[0] : 1;
int dilation_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dilation_w = dilations.back();
int i, j, k, d;
int inpPlaneSize = (int)input_->total(2);
int outPlaneSize = (int)output_->total(2);
bool is1x1 = is1x1_;
int stripesPerSample;
int stripeSize;
Range r = r0;
bool depthWiseConvolution = !is1x1 && isConv2D && ngroups > 1 && inpCn == 1 &&
outCn == 1 && kernel_d == 1 && dilation_d == 1 && stride_d == 0 && pad_d == 0 &&
width >= 16 + dilation_w*(kernel_w - 1);
// for now only 3x3 depth-wise convolutions are supported
depthWiseConvolution = depthWiseConvolution && kernel_w == 3 && kernel_h == 3 &&
// computing at most 1 pixel from each side can involve padding
max(stride_w, dilation_w) >= pad_l && max(stride_h, dilation_h) >= pad_t &&
pad_l <= 1 && pad_t <= 1;
if( !depthWiseConvolution && nstripes >= batchSize*2 )
{
stripesPerSample = nstripes/batchSize;
stripeSize = (int)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>();
AutoBuffer<float> rowbuf0_;
float* rowbuf0 = 0;
bool use_rowbuf = !depthWiseConvolution;
int blk_size = depthWiseConvolution ? outPlaneSize : min((int)BLK_SIZE, stripeSize);
// im2row buffer is not used for depth-wise convolution
if(use_rowbuf)
{
size_t rowbufsz = alignSize(karea*blk_size_cn, valign)*min((int)BLK_SIZE, blk_size);
//printf("karea=%d, blk_size_cn=%d, rowbufsz=%d, stripeSize=%d\n", karea, blk_size_cn, (int)rowbufsz, stripeSize);
rowbuf0_.allocate(rowbufsz + valign);
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 bsz = ofs1 - ofs0;
int out_d = ofs0 / (outH * outW);
int out_i = (ofs0 - out_d * outH * outW) / outW;
int out_j = ofs0 % outW;
if (depthWiseConvolution)
{
CV_Assert(out_i == 0 && out_j == 0);
int in_d = out_d * stride_d - pad_d;
const float* inptr_ = data_inp0 + (cn0*depth*height + in_d*height)*width;
float* outptr_ = data_out0 + ofs0;
#if CV_TRY_AVX2
if(useAVX2)
opt_AVX2::fastDepthwiseConv(wptr, kernel_h, kernel_w,
stride_h, stride_w, dilation_h, dilation_w, pad_t, pad_l,
biasptr, relu, inptr_, height, width, outptr_, out_d, outH, outW);
else
#endif
#if CV_TRY_AVX
if(useAVX)
opt_AVX::fastDepthwiseConv(wptr, kernel_h, kernel_w,
stride_h, stride_w, dilation_h, dilation_w, pad_t, pad_l,
biasptr, relu, inptr_, height, width, outptr_, out_d, outH, outW);
else
#endif
#if CV_TRY_RVV
if(useRVV)
opt_RVV::fastDepthwiseConv(wptr, kernel_h, kernel_w,
stride_h, stride_w, dilation_h, dilation_w, pad_t, pad_l,
biasptr, relu, inptr_, height, width, outptr_, out_d, outH, outW);
else
#endif
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2],
w10 = wptr[3], w11 = wptr[4], w12 = wptr[5],
w20_ = wptr[6], w21_ = wptr[7], w22_ = wptr[8];
int outW1 = min(outW, (width - dilation_w*(kernel_w - 1) + pad_l)/stride_w);
float relu_coeff = relu ? relu[out_d] : 1.f, bias = biasptr[out_d];
for (int out_i = 0; out_i < outH; out_i++)
{
int in_i = out_i * stride_h - pad_t, out_j = 0;
const float* imgptr0 = inptr_ + in_i*width;
const float* imgptr1 = imgptr0 + dilation_h*width;
const float* imgptr2 = imgptr0 + (dilation_h*2)*width;
float out, w00 = w00_, w01 = w01_, w02 = w02_;
float w20 = w20_, w21 = w21_, w22 = w22_;
if (in_i < 0)
{
w00 = w01 = w02 = 0.f;
imgptr0 = imgptr1;
}
else if (in_i + dilation_h*(kernel_h-1) >= height)
{
w20 = w21 = w22 = 0.f;
imgptr2 = imgptr1;
}
float* outptr = outptr_ + out_i*outW;
if (pad_l > 0)
{
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 +
imgptr1[0]*w11 + imgptr1[dilation_w]*w12 +
imgptr2[0]*w21 + imgptr2[dilation_w]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
out_j = 1;
}
#if CV_SIMD
// maybe with AVX or AVX512 strided depthwise convolution
// can be accelerated with vector code, but with 4xfloat vectors
// it's hardly the case
if( stride_w == 1 )
{
const int VECSZ = v_float32::nlanes;
const int out_delta = VECSZ/stride_w;
v_float32 vw00 = vx_setall_f32(w00), vw01 = vx_setall_f32(w01), vw02 = vx_setall_f32(w02),
vw10 = vx_setall_f32(w10), vw11 = vx_setall_f32(w11), vw12 = vx_setall_f32(w12),
vw20 = vx_setall_f32(w20), vw21 = vx_setall_f32(w21), vw22 = vx_setall_f32(w22);
v_float32 z = vx_setzero_f32(), vbias = vx_setall_f32(bias), vrc = vx_setall_f32(relu_coeff);
for( ; out_j < outW1; out_j += out_delta )
{
if (out_j + out_delta > outW1)
{
if (out_j <= pad_l)
break;
out_j = outW1 - out_delta;
}
int in_j = out_j * stride_w - pad_l;
v_float32 v00 = vx_load(imgptr0 + in_j),
v01 = vx_load(imgptr0 + in_j + dilation_w),
v02 = vx_load(imgptr0 + in_j + dilation_w*2),
v10 = vx_load(imgptr1 + in_j),
v11 = vx_load(imgptr1 + in_j + dilation_w),
v12 = vx_load(imgptr1 + in_j + dilation_w*2),
v20 = vx_load(imgptr2 + in_j),
v21 = vx_load(imgptr2 + in_j + dilation_w),
v22 = vx_load(imgptr2 + in_j + dilation_w*2);
v_float32 vout = v00*vw00 + v01*vw01 + v02*vw02 +
v10*vw10 + v11*vw11 + v12*vw12 +
v20*vw20 + v21*vw21 + v22*vw22 + vbias;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
}
}
#endif
for (; out_j < outW1; out_j++)
{
int in_j = out_j * stride_w - pad_l;
out = imgptr0[in_j]*w00 + imgptr0[in_j + dilation_w]*w01 + imgptr0[in_j + dilation_w*2]*w02 +
imgptr1[in_j]*w10 + imgptr1[in_j + dilation_w]*w11 + imgptr1[in_j + dilation_w*2]*w12 +
imgptr2[in_j]*w20 + imgptr2[in_j + dilation_w]*w21 + imgptr2[in_j + dilation_w*2]*w22 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
for (; out_j < outW; out_j++ )
{
int in_j0 = out_j * stride_w - pad_l, in_j1 = in_j0 + dilation_w, in_j2 = in_j0 + dilation_w*2;
float s0 = 1.f, s1 = 1.f, s2 = 1.f;
if (in_j0 >= width)
{
in_j0 = 0;
s0 = 0.f;
}
if (in_j1 >= width)
{
in_j1 = 0;
s1 = 0.f;
}
if (in_j2 >= width)
{
in_j2 = 0;
s2 = 0.f;
}
out = imgptr0[in_j0]*w00*s0 + imgptr0[in_j1]*w01*s1 + imgptr0[in_j2]*w02*s2 +
imgptr1[in_j0]*w10*s0 + imgptr1[in_j1]*w11*s1 + imgptr1[in_j2]*w12*s2 +
imgptr2[in_j0]*w20*s0 + imgptr2[in_j1]*w21*s1 + imgptr2[in_j2]*w22*s2 + bias;
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
}
}
}
continue;
}
// do im2row for a part of input tensor
float* rowbuf = rowbuf0;
if (isConv1D)
{
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_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + cn0*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
{
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( 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 i0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int i1 = 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++ )
{
int imgofs = k*width + i*dilation_w;
rowbuf[k*kernel_w + i] = imgptr[imgofs];
}
}
}
}
}
}
}
else if (isConv2D)
{
if( is1x1 && stride_w == 1 && stride_h == 1 )
{
const float* imgptr = data_inp0 + (cn0*height + out_i)*width + out_j;
for( int j = 0; j < bsz; j++, rowbuf += vsz_a )
{
if( j + 4 <= bsz )
{
k = 0;
#if CV_SIMD128
for( ; k <= vsz - 4; k += 4 )
{
const float* inp = imgptr + j + k*inpPlaneSize;
v_float32x4 p0 = v_load(inp), p1 = v_load(inp + inpPlaneSize);
v_float32x4 p2 = v_load(inp + inpPlaneSize*2), p3 = v_load(inp + inpPlaneSize*3);
v_float32x4 r0, r1, r2, r3;
v_transpose4x4(p0, p1, p2, p3, r0, r1, r2, r3);
v_store(rowbuf + k, r0);
v_store(rowbuf + k + vsz_a, r1);
v_store(rowbuf + k + vsz_a*2, r2);
v_store(rowbuf + k + vsz_a*3, r3);
}
#endif
for( ; k < vsz; k++ )
{
const float* inp = imgptr + j + k*inpPlaneSize;
float v0 = inp[0], v1 = inp[1], v2 = inp[2], v3 = inp[3];
rowbuf[k] = v0;
rowbuf[k + vsz_a] = v1;
rowbuf[k + vsz_a*2] = v2;
rowbuf[k + vsz_a*3] = v3;
}
j += 3;
rowbuf += vsz_a*3;
}
else
{
for( k = 0; k < vsz; k++ )
{
rowbuf[k] = imgptr[j + k*inpPlaneSize];
}
}
}
}
else
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_t;
int in_j = out_j * stride_w - pad_l;
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];
}
}
}
}
}
}
}
}
else
{
for( ofs = ofs0; ofs < ofs1; out_d += (out_i + 1) / outH, out_i = (out_i + 1) % outH, out_j = 0 )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
int out_j1 = out_j + delta;
int in_d = out_d * stride_d - pad_d;
int in_i = out_i * stride_h - pad_t;
int in_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + (cn0*depth*height + in_d*height + in_i)*width + in_j;
ofs += delta;
int d0 = std::max(0, (-in_d + dilation_d - 1) / dilation_d);
int d1 = std::min(kernel_d, (depth - in_d + dilation_d - 1) / dilation_d);
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 )
{
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 ( d = d0; d < d1; d++)
{
for( i = i0; i < i1; i++ )
{
for( j = j0; j < j1; j++ )
{
int imgofs = k*(depth*width*height) + d*dilation_d*width*height + i*(dilation_h*width) + j*dilation_w;
rowbuf[(k*kernel_d*kernel_h + d*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
}
}
}
}
}
}
}
// now compute dot product of the weights
// and im2row-transformed part of the tensor
#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
#if CV_TRY_RVV
if(useRVV)
opt_RVV::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);
v_float32x4 w1 = v_load_aligned(wptr1 + k);
v_float32x4 r0 = v_load_aligned(rptr);
v_float32x4 r1 = v_load_aligned(rptr + vsz_a);
v_float32x4 r2 = v_load_aligned(rptr + vsz_a*2);
v_float32x4 r3 = v_load_aligned(rptr + vsz_a*3);
vs00 = v_fma(w0, r0, vs00);
vs01 = v_fma(w0, r1, vs01);
vs02 = v_fma(w0, r2, vs02);
vs03 = v_fma(w0, r3, vs03);
vs10 = v_fma(w1, r0, vs10);
vs11 = v_fma(w1, r1, vs11);
vs12 = v_fma(w1, r2, vs12);
vs13 = v_fma(w1, r3, vs13);
}
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)
{
if (kernel_size.size() != 2)
{
// no OpenCL optimizations, see .supportedBacked()
return false;
}
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 (blobs.empty())
{
size_t n = inputs.size() - 1;
umat_blobs.resize(n);
for (size_t i = 0; i < n; i++)
{
inputs[i + 1].copyTo(umat_blobs[i]);
}
inputs.resize(1);
}
if (umat_blobs.empty())
{
size_t n = blobs.size();
umat_blobs.resize(n);
for (size_t i = 0; i < n; i++)
{
if (use_half)
convertFp16(blobs[i], umat_blobs[i]);
else
blobs[i].copyTo(umat_blobs[i]);
}
}
if (convolutionOp.empty() || blobs.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;
if (inputs[0].dims != 4 && inputs[0].dims != umat_blobs[0].dims)
{
static bool bypassCheck = utils::getConfigurationParameterBool("OPENCV_OCL4DNN_CONVOLUTION_IGNORE_INPUT_DIMS_4_CHECK", false);
if (!bypassCheck)
{
CV_LOG_ERROR(NULL, "DNN/OpenCL: Unsupported configuration: inputs[0].dims=" << inputs[0].dims << " umat_blobs[0].dims=" << umat_blobs[0].dims
<< ". Consider reporting complete reproducer to https://github.com/opencv/opencv/issues/20833."
<< " You can skip this check temporary through OPENCV_OCL4DNN_CONVOLUTION_IGNORE_INPUT_DIMS_4_CHECK=1"
);
return false;
}
}
config.group = inputs[0].size[1] / umat_blobs[0].size[1];
if (config.group < 1) // config.group == 0 causes div by zero in ocl4dnn code
{
CV_LOG_WARNING(NULL, "DNN/OpenCL: Unsupported config.group=" << config.group
<< ". Consider reporting complete reproducer to https://github.com/opencv/opencv/issues/20833"
);
return false;
}
config.bias_term = umat_blobs.size() == 2;
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 (fusedWeights)
{
if (use_half)
convertFp16(weightsMat, umat_blobs[0]);
else
weightsMat.copyTo(umat_blobs[0]);
fusedWeights = false;
}
if (fusedBias)
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
if (use_half)
convertFp16(Mat(biasvec, true), umat_blobs[1]);
else
Mat(biasvec, true).copyTo(umat_blobs[1]);
convolutionOp->setBias(true);
fusedBias = 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],
umat_blobs.size() > 1 ? 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());
#if CV_SSE3
uint32_t ftzMode = _MM_GET_FLUSH_ZERO_MODE();
uint32_t dazMode = _MM_GET_DENORMALS_ZERO_MODE();
_MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
_MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);
#endif
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
int outCn = blobs.empty() ? inputs[1].size[0] : blobs[0].size[0];
// Need to align non-const blobs
if (blobs.empty())
{
Mat wm = inputs[1].reshape(1, outCn);
if (wm.data != weightsMat.data)
{
int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_buffer = Mat(numOutput, newcols, wm.type());
Mat wm_padding = wm_buffer.colRange(wm.cols, newcols);
wm_padding.setTo(Scalar::all(0.));
weightsMat = wm_buffer.colRange(0, wm.cols);
wm.copyTo((const Mat&)weightsMat);
if (inputs.size() > 2)
{
Mat biasMat = inputs[2].reshape(1, outCn);
biasMat.col(0).copyTo(biasvec);
}
biasvec.resize(outCn + 2, 0);
}
}
/*if (inputs[0].dims > 3) {
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);
}
else {
printf("conv %s: input (%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],
kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);
}*/
int inpGroupCn = blobs.empty() ? inputs[1].size[1] : blobs[0].size[1];
CV_Assert_N(inputs.size() >= (size_t)1, inputs[0].size[1] % inpGroupCn == 0,
outputs.size() == 1, inputs[0].data != outputs[0].data);
int ngroups = inputs[0].size[1] / inpGroupCn;
CV_Assert(outputs[0].size[1] % ngroups == 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];
}
}
#ifdef HAVE_TENGINE
bool tengine_ret = false; ;
std::vector<Mat> teng_in, teng_out;
inputs_arr.getMatVector(teng_in);
outputs_arr.getMatVector(teng_out);
int inch = teng_in[0].size[1]; // inch
int in_h = teng_in[0].size[2]; // in_h
int in_w = teng_in[0].size[3]; // in_w
int out_b = teng_out[0].size[0]; // out batch size
int outch = teng_out[0].size[1]; // outch
int out_h = teng_out[0].size[2]; // out_h
int out_w = teng_out[0].size[3]; // out_w
float *input_ = teng_in[0].ptr<float>();
float *output_ = teng_out[0].ptr<float>();
float *kernel_ = weightsMat.ptr<float>();
float *teg_bias = &biasvec[0];
int nstripes = std::max(getNumThreads(), 1);
/* tengine_init will run when first time. */
if(NULL == tengine_graph)
{
tengine_graph = tengine_init(name.c_str(), input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode, tengine_graph, nstripes);
/*printf("Init(%s): input=%p(%d %d %d %d ),output=%p(%d %d %d %d ),kernel=%p(%ld %d %d ), bias=%p ,"
"stride(%d %d), pad(%d %d), dilation(%d %d) ,weightsMat=%ld, padMode=%s ,tengine_graph = %p \n",
name.c_str(),input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode.c_str() ,tengine_graph);*/
}
if(NULL != tengine_graph)
{
tengine_ret = tengine_forward(tengine_graph);
}
/* activation */
if((true == tengine_ret) && activ )
{
int out_cstep = out_h * out_w; // out_cstep
ActivationLayer* activ_ = activ.get();
activ_->forwardSlice(output_, output_, out_cstep, out_cstep, 0, outch);
}
if(false == tengine_ret)
#endif
{
int nstripes = std::max(getNumThreads(), 1);
ParallelConv::run(inputs[0], outputs[0], weightsMat, biasvec, reluslope,
kernel_size, strides, pads_begin, pads_end, dilations, activ.get(), ngroups, nstripes);
}
#if CV_SSE3
_MM_SET_FLUSH_ZERO_MODE(ftzMode);
_MM_SET_DENORMALS_ZERO_MODE(dazMode);
#endif
}
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
CV_Assert(inputs.size() == 1 || inputs.size() == 2);
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto input_shape = input_wrapper->getShape();
CV_Assert(outputs.size() == 1);
auto output_wrapper = outputs[0].dynamicCast<CUDABackendWrapper>();
auto output_shape = output_wrapper->getShape();
const auto output_feature_maps = blobs[0].size[0];
const auto input_feature_maps = input_shape[1];
const auto input_feature_maps_per_group = blobs[0].size[1];
const auto groups = input_feature_maps / input_feature_maps_per_group;
ConvolutionConfiguration config;
if (input_shape.size() == 3)
{
// Conv1D
// We add an extra dim for input and output tensors, because CuDNN doesn't support convolution with 3D tensors
input_shape.insert(std::end(input_shape) - 1, 1);
output_shape.insert(std::end(output_shape) - 1, 1);
// Do the similar thing for the other parameters
pads_begin.insert(std::begin(pads_begin), 0);
pads_end.insert(std::begin(pads_end), 0);
strides.insert(std::begin(strides), 1);
dilations.insert(std::begin(dilations), 1);
kernel_size.insert(std::begin(kernel_size), 1);
}
config.kernel_size.assign(std::begin(kernel_size), std::end(kernel_size));
config.dilations.assign(std::begin(dilations), std::end(dilations));
config.strides.assign(std::begin(strides), std::end(strides));
if (padMode.empty())
{
config.padMode = ConvolutionConfiguration::PaddingMode::MANUAL;
config.pads_begin.assign(std::begin(pads_begin), std::end(pads_begin));
config.pads_end.assign(std::begin(pads_end), std::end(pads_end));
}
else if (padMode == "VALID")
{
config.padMode = ConvolutionConfiguration::PaddingMode::VALID;
}
else if (padMode == "SAME")
{
config.padMode = ConvolutionConfiguration::PaddingMode::SAME;
}
else
{
CV_Error(Error::StsNotImplemented, padMode + " padding mode not supported by ConvolutionLayer");
}
config.input_shape.assign(std::begin(input_shape), std::end(input_shape));
config.output_shape.assign(std::begin(output_shape), std::end(output_shape));
config.groups = groups;
config.fusion_mode = cudaFusionMode;
config.activation_type = cudaActType;
config.relu_negative_slope = cuda_relu_slope;
config.crelu_floor = cuda_crelu_floor;
config.crelu_ceil = cuda_crelu_ceil;
config.power_exp = cuda_power_exp;
config.power_scale = cuda_power_scale;
config.power_shift = cuda_power_shift;
Mat filtersMat = fusedWeights ? weightsMat : blobs[0];
Mat biasMat = (hasBias() || fusedBias) ? Mat(output_feature_maps, 1, CV_32F, biasvec.data()) : Mat();
if (countNonZero(biasMat) == 0)
biasMat = Mat();
return make_cuda_node<cuda4dnn::ConvolutionOp>(
preferableTarget, std::move(context->stream), std::move(context->cudnn_handle), config, filtersMat, biasMat);
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
// References - https://arxiv.org/pdf/1712.05877.pdf
// Quantized convolution with variable weights is not supported.
if (blobs.empty())
return false;
float inputScale = scales[0][0], outputScale = scales[1][0];
int inputZp = zeropoints[0][0];
params.set("input_zeropoint", inputZp);
Mat weightsQuantized(weightsMat.rows, weightsMat.cols, CV_8S);
Mat biasQuantized(1, numOutput, CV_32S);
Mat outputMultiplier(1, numOutput, CV_32F);
double realMin, realMax, weightsScale;
for( int i = 0; i < numOutput; i++ )
{
// Quantize weights
cv::minMaxIdx(weightsMat.row(i), &realMin, &realMax);
realMin = std::min(realMin, 0.0);
realMax = std::max(realMax, 0.0);
weightsScale = (realMax == realMin) ? 1.0 : std::max(-realMin, realMax)/127;
weightsMat.row(i).convertTo(weightsQuantized.row(i), CV_8S, 1.f/weightsScale);
// Quantize biases
float biasScale = inputScale * weightsScale;
biasQuantized.at<int>(i) = (int)std::round(biasvec[i]/biasScale) - inputZp*(cv::sum(weightsQuantized.row(i))[0]);
// Store multiplier
outputMultiplier.at<float>(i) = biasScale / outputScale;
}
params.blobs.clear();
params.blobs.push_back(weightsQuantized.reshape(1, shape(blobs[0])));
params.blobs.push_back(biasQuantized);
params.blobs.push_back(outputMultiplier);
return true;
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size() || inputs.size() == outputs.size() + blobs.size());
int64 flops = 0;
int karea = std::accumulate(kernel_size.begin(), kernel_size.end(), 1, std::multiplies<size_t>());
for (int i = 0; i < outputs.size(); i++)
{
flops += total(outputs[i])*(CV_BIG_INT(2)*karea*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 dims = inpShape.size();
int inpCn = inpShape[1];
int inpD = dims == 5 ? inpShape[2] : 1;
int inpH = inpShape[dims - 2];
int inpW = inpShape.back();
int outCn = outShape[1];
int ngroups = inpCn / blobs[0].size[0];
int outGroupCn = outCn / ngroups;
int ksize = outGroupCn * std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>());
return shape(ksize, inpD * inpH * inpW);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_CUDA)
{
/* only deconvolution 2d and 3d supported */
if (kernel_size.size() == 2 || kernel_size.size() == 3)
return true;
return false;
}
#ifdef HAVE_INF_ENGINE
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW or IODHW layout
const int group = numOutput / outGroupCn;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return group == 1;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
return false;
}
if (std::accumulate(adjust_pads.begin(), adjust_pads.end(), 0, std::plus<size_t>()) > 0)
{
if (padMode.empty())
{
if (preferableTarget != DNN_TARGET_CPU && group != 1)
{
for (int i = 0; i < adjust_pads.size(); i++) {
if (adjust_pads[i] && pads_begin[i])
return false;
}
}
for (int i = 0; i < adjust_pads.size(); i++) {
if (pads_end[i] < adjust_pads[i])
return false;
}
return true;
}
else if (padMode == "SAME")
{
for (int i = 0; i < adjust_pads.size(); i++) {
if (kernel_size[i] < pads_begin[i] + 1 + adjust_pads[i])
return false;
}
return true;
}
else if (padMode == "VALID")
return false;
}
if (group != 1)
{
return preferableTarget == DNN_TARGET_CPU;
}
if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies<size_t>()) == 1;
return true;
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#endif // HAVE_INF_ENGINE
{
return backendId == DNN_BACKEND_CUDA ||
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
}
}
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 outCn = numOutput;
std::vector<int> outShape;
outShape.push_back(inputs[0][0]); // batch
outShape.push_back(outCn);
if (padMode.empty())
{
for (int i = 0; i < kernel_size.size(); i++)
outShape.push_back(strides[i] * (inputs[0][2 + i] - 1) + kernel_size[i] - pads_begin[i] - pads_end[i] + adjust_pads[i]);
}
else if (padMode == "VALID")
{
for (int i = 0; i < kernel_size.size(); i++)
outShape.push_back(strides[i] * (inputs[0][2 + i] - 1) + kernel_size[i] + adjust_pads[i]);
}
else if (padMode == "SAME")
{
for (int i = 0; i < kernel_size.size(); i++)
outShape.push_back(strides[i] * (inputs[0][2 + i] - 1) + 1 + adjust_pads[i]);
}
else
CV_Error(Error::StsError, "Unsupported padding mode " + padMode);
CV_Assert(outCn % blobs[0].size[1] == 0);
int ngroups = outCn / blobs[0].size[1];
int inpCn = inputs[0][1];
CV_Assert(inpCn % ngroups == 0 && outCn % ngroups == 0);
CV_Assert(blobs[0].size[0] == inpCn);
outputs.resize(1, outShape);
if (!is1x1())
internals.push_back(computeColRowShape(inputs[0], outputs[0]));
return false;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
BaseConvolutionLayerImpl::finalize(inputs_arr, outputs_arr);
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
std::vector<int> inpShape;
std::vector<int> outShape;
for (int i = 2; i < inputs[0].dims; i++) {
inpShape.push_back(inputs[0].size[i]);
outShape.push_back(outputs[0].size[i]);
}
getConvPoolPaddings(outShape, kernel_size, strides, padMode, pads_begin, pads_end);
if (pads_begin.size() == 2) {
for (int i = 0; i < pads_begin.size(); i++) {
if (pads_begin[i] != pads_end[i])
CV_Error(Error::StsNotImplemented, "Unsupported asymmetric padding in deconvolution layer");
}
pad = Size(pads_begin[1], pads_begin[0]);
}
weightsMultipliers.assign(numOutput, 1.0);
if (weightsMat.empty())
{
transpose(blobs[0].reshape(1, blobs[0].size[0]), weightsMat);
biasesMat = hasBias() ? blobs[1].reshape(1, numOutput)
: Mat::zeros(numOutput, 1, CV_32F);
}
}
void fuseWeights(const Mat& w_, const Mat& b_) CV_OVERRIDE
{
Mat w = w_.total() == 1 ? Mat(1, numOutput, CV_32F, Scalar(w_.at<float>(0))) : w_;
Mat b = b_.total() == 1 ? Mat(1, numOutput, CV_32F, Scalar(b_.at<float>(0))) : b_;
CV_Assert_N(!weightsMat.empty(),
w.empty() || numOutput == w.total(),
b.empty() || numOutput == b.total());
if (!w.empty())
{
transpose(blobs[0].reshape(1, blobs[0].size[0]), weightsMat);
weightsMat = weightsMat.reshape(1, numOutput);
for (int i = 0; i < numOutput; ++i)
{
double wi = w.at<float>(i);
weightsMultipliers[i] *= wi;
cv::multiply(weightsMat.row(i), weightsMultipliers[i], weightsMat.row(i));
biasesMat.at<float>(i) *= wi;
}
weightsMat = weightsMat.reshape(1, weightsMat.total() / blobs[0].size[0]);
}
if (!b.empty())
{
cv::add(biasesMat, b.reshape(1, numOutput), biasesMat);
}
}
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;
useRVV = checkHardwareSupport(CPU_RVV);
}
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
#if CV_TRY_RVV
if( useRVV ) {
opt_RVV::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 d0 = v_load(dst0 + n);
v_float32x4 d1 = v_load(dst1 + n);
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);
// TODO try to improve pipeline width
d0 = v_fma(b0, a00, d0);
d1 = v_fma(b0, a01, d1);
d0 = v_fma(b1, a10, d0);
d1 = v_fma(b1, a11, d1);
d0 = v_fma(b2, a20, d0);
d1 = v_fma(b2, a21, d1);
d0 = v_fma(b3, a30, d0);
d1 = v_fma(b3, a31, d1);
v_store(dst0 + n, d0);
v_store(dst1 + n, d1);
}
#endif
for( ; n < nmax; n++ )
{
float b0 = bptr0[n];
float b1 = bptr1[n];
float b2 = bptr2[n];
float 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;
bool useRVV;
};
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())
{
if (fusedWeights)
weightsMat.copyTo(umat_weights);
else
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (fusedBias)
biasesMat.copyTo(umat_biases);
else
{
if (hasBias())
blobs[1].reshape(1, outCn).copyTo(umat_biases);
else
umat_biases = 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),
forward_ocl(inputs_arr, outputs_arr, internals_arr));
if (inputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
std::vector<Mat> inputs, outputs, internals;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
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);
}
}
}
}
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
CV_Assert(inputs.size() == 1);
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
auto input_shape = input_wrapper->getShape();
CV_Assert(outputs.size() == 1);
auto output_wrapper = outputs[0].dynamicCast<CUDABackendWrapper>();
auto output_shape = output_wrapper->getShape();
const auto output_feature_maps = numOutput;
const auto output_feature_maps_per_group = blobs[0].size[1];
const auto groups = output_feature_maps / output_feature_maps_per_group;
TransposeConvolutionConfiguration config;
config.kernel_size.assign(std::begin(kernel_size), std::end(kernel_size));
config.dilations.assign(std::begin(dilations), std::end(dilations));
config.strides.assign(std::begin(strides), std::end(strides));
if (padMode.empty())
{
config.padMode = TransposeConvolutionConfiguration::PaddingMode::MANUAL;
config.pads_begin.assign(std::begin(pads_begin), std::end(pads_begin));
config.pads_end.assign(std::begin(pads_end), std::end(pads_end));
}
else if (padMode == "VALID")
{
config.padMode = TransposeConvolutionConfiguration::PaddingMode::VALID;
}
else if (padMode == "SAME")
{
config.padMode = TransposeConvolutionConfiguration::PaddingMode::SAME;
}
else
{
CV_Error(Error::StsNotImplemented, padMode + " padding mode not supported by DeconvolutionLayer");
}
config.input_shape.assign(std::begin(input_shape), std::end(input_shape));
config.output_shape.assign(std::begin(output_shape), std::end(output_shape));
config.groups = groups;
CV_Assert(blobs.size() >= 1);
Mat filtersMat = fusedWeights ? weightsMat.t() : blobs[0];
Mat biasMat = (hasBias() || fusedBias) ? biasesMat : Mat();
if (countNonZero(biasMat) == 0)
biasMat = Mat();
return make_cuda_node<cuda4dnn::TransposeConvolutionOp>(
preferableTarget, std::move(context->stream), std::move(context->cudnn_handle), config, filtersMat, biasMat);
}
#endif
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>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
InferenceEngine::Layout::OIHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (fusedWeights)
{
ieWeights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32,
ieWeights->getTensorDesc().getDims(), layout
});
ieWeights->allocate();
int inpCn = blobs[0].size[0];
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn);
transpose(weightsMat, newWeights);
}
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW or OIDHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
if (padMode.empty())
{
std::vector<size_t> paddings_end;
for (int i = 0; i < pads_end.size(); i++) {
paddings_end.push_back(pads_end[i] - adjust_pads[i]);
}
ieLayer.setPaddingsEnd(paddings_end);
}
else if (padMode == "SAME")
{
std::vector<size_t> paddings_end;
for (int i = 0; i < pads_begin.size(); i++) {
paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
}
ieLayer.setPaddingsEnd(paddings_end);
}
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", ieWeights, l);
if (hasBias())
addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
const int outGroupCn = blobs[0].size[1];
const int group = numOutput / outGroupCn;
CV_Assert(group == 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> kernel_shape = getShape<size_t>(blobs[0]);
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
Mat newWeights;
transpose(weightsMat, newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
}
std::vector<size_t> paddings_end;
if (padMode == "SAME")
{
for (int i = 0; i < pads_begin.size(); i++) {
paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
}
adjust_pads = std::vector<size_t>(pads_begin.size(), 0);
} else {
paddings_end = pads_end;
}
ngraph::op::PadType pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::EXPLICIT;
auto deconv = std::make_shared<ngraph::op::v1::ConvolutionBackpropData>(
ieInpNode,
ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(paddings_end.begin(), paddings_end.end())),
ngraph::Strides(dilations),
pad_type,
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(adjust_pads.begin(), adjust_pads.end())));
if (hasBias() || fusedBias)
{
std::vector<size_t> shape(deconv->get_shape().size(), 1);
shape[1] = numOutput;
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[1].data);
auto deconv_bias = std::make_shared<ngraph::op::v1::Add>(deconv, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(deconv_bias));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(deconv));
}
#endif // HAVE_DNN_NGRAPH
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];
size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>());
for (int i = 0; i < inputs.size(); i++)
{
flops += CV_BIG_INT(2)*outChannels*karea*total(inputs[i]);
}
return flops;
}
};
Ptr<BaseConvolutionLayer> ConvolutionLayer::create(const LayerParams &params)
{
Ptr<ConvolutionLayerImpl> l(new ConvolutionLayerImpl(params));
return l;
}
Ptr<BaseConvolutionLayer> DeconvolutionLayer::create(const LayerParams &params)
{
return Ptr<BaseConvolutionLayer>(new DeConvolutionLayerImpl(params));
}
}
}
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