potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 15:23:05 +04:00
Code Packages Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin
2019-12-02 16:18:07 +03:00
parent 78c5e41c23 7523c777c5
commit 4b0132ed7a
51 changed files with 3276 additions and 496 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/src/layers/convolution_layer.cpp
+152 -2
View File
@@ -45,7 +45,9 @@
#include "../op_cuda.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
@@ -270,7 +272,7 @@ public:
}
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
@@ -618,6 +620,73 @@ public:
}
#endif // HAVE_INF_ENGINE
#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_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;
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)
{
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
}
else
{
Mat newWeights = blobs[0].reshape(1, outCn);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
}
}
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::GroupConvolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::Strides(dilations),
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{},
group,
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)
{
std::vector<size_t> shape(conv_node->get_shape().size(), 1);
shape[1] = outCn;
auto 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
class ParallelConv : public cv::ParallelLoopBody
{
public:
@@ -1410,7 +1479,24 @@ public:
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)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
if (padMode.empty()) {
for (int i = 0; i < adjust_pads.size(); i++) {
if (pads_end[i] < adjust_pads[i])
return false;
}
} 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;
}
} else if (padMode == "VALID")
return false;
return group == 1;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
return false;
@@ -2153,6 +2239,70 @@ public:
}
#endif // HAVE_INF_ENGINE
#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)
{
int inpCn = blobs[0].size[0];
Mat newWeights = blobs[0].reshape(1, inpCn);
transpose(weightsMat, newWeights);
}
size_t batch = ieInpNode->get_shape()[0];
std::vector<size_t> out_shape = {batch, (size_t)numOutput};
std::vector<size_t> paddings_end;
std::vector<size_t> inpShape = ieInpNode->get_shape();
if (padMode.empty())
{
for (int i = 0; i < pads_end.size(); i++) {
out_shape.push_back(strides[i] * (inpShape[2 + i] - 1) +
kernel_size[i] - pads_begin[i] - pads_end[i] + adjust_pads[i]);
paddings_end.push_back(pads_end[i] - adjust_pads[i]);
}
}
else if (padMode == "SAME")
{
for (int i = 0; i < pads_begin.size(); i++) {
out_shape.push_back(strides[i] * (inpShape[2 + i] - 1) + 1 + adjust_pads[i]);
paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
}
} else {
paddings_end = pads_end;
}
auto deconv = std::make_shared<ngraph::op::ConvolutionBackpropData>(
ngraph::Shape{out_shape},
ieWeights,
ieInpNode,
ngraph::Strides(strides),
ngraph::Strides(dilations),
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())),
(strides.size() == 2 ? ngraph::Strides{1, 1} : ngraph::Strides{1, 1, 1}));
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
{