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Merge pull request #11867 from dkurt:dnn_ie_layers

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This commit is contained in:
Vadim Pisarevsky
2018-07-06 13:13:20 +00:00
parent 3b01777c98 019c2f2115
commit 523b6f32ba
13 changed files with 761 additions and 518 deletions
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modules/dnn/src/dnn.cpp
+3 -1
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@@ -2730,9 +2730,9 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
}
else if (targetId == DNN_TARGET_OPENCL)
{
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
if (outW == 1 && outH == 1)
{
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
top.split(c, co, ci, c_split)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.gpu_blocks(tile)
@@ -2742,6 +2742,8 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
{
int x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW;
int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH;
// Supported vectorization widths: 2, 3, 4, 8, 16
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : std::min(4, outC);
top.split(x, xo, xi, x_split).split(y, yo, yi, y_split)
.split(c, co, ci, c_split)
.gpu_blocks(xo, yo, co)
modules/dnn/src/layers/convolution_layer.cpp
+15 -1
View File
@@ -82,7 +82,21 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return preferableTarget != DNN_TARGET_MYRIAD || type != "Deconvolution" || adjustPad == Size();
{
if (type == "Convolution")
return preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height;
else
{
CV_Assert(type == "Deconvolution");
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (group != 1)
return false;
if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
return dilation.width == 1 && dilation.height == 1;
return true;
}
}
else
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
modules/dnn/src/layers/eltwise_layer.cpp
+2 -2
View File
@@ -97,8 +97,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
modules/dnn/src/layers/reorg_layer.cpp
+20 -1
View File
@@ -41,9 +41,9 @@
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <iostream>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
@@ -85,6 +85,11 @@ public:
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
@@ -169,6 +174,20 @@ public:
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ReorgYolo";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["stride"] = format("%d", reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
modules/dnn/src/layers/resize_layer.cpp
+21
View File
@@ -192,6 +192,11 @@ public:
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
if (!outWidth && !outHeight)
@@ -204,6 +209,22 @@ public:
scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f;
scaleWidth = (outWidth > 1) ? (static_cast<float>(inpWidth - 1) / (outWidth - 1)) : 0.f;
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Interp";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["pad_beg"] = "0";
ieLayer->params["pad_end"] = "0";
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
};
Ptr<Layer> InterpLayer::create(const LayerParams& params)
modules/dnn/src/layers/slice_layer.cpp
+15 -1
View File
@@ -266,7 +266,21 @@ public:
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
CV_Assert(sliceRanges.size() == 1);
for (int i = sliceRanges[0].size() - 1; i >= 0; --i)
int from, to, step;
if (preferableTarget == DNN_TARGET_MYRIAD)
{
from = 1;
to = sliceRanges[0].size() + 1;
step = 1;
}
else
{
from = sliceRanges[0].size() - 1;
to = -1;
step = -1;
}
for (int i = from; i != to; i += step)
{
ieLayer->axis.push_back(i);
ieLayer->offset.push_back(sliceRanges[0][i].start);