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Added optimization fuse

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This commit is contained in:
Alexander Nesterov 2019-02-08 13:12:33 -01:00
parent 3132c8ee08
commit 74574dfae4
3 changed files with 86 additions and 21 deletions
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90
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -61,6 +61,8 @@ namespace dnn
class BaseConvolutionLayerImpl : public ConvolutionLayer
{
public:
bool newWeightAndBias;
std::vector<double> weightsMultipliers;
BaseConvolutionLayerImpl(const LayerParams &params)
{
setParamsFrom(params);
@ -84,6 +86,8 @@ public:
CV_Assert(numOutput % ngroups == 0);
CV_Assert(adjustPad.width < stride.width &&
adjustPad.height < stride.height);
newWeightAndBias = false;
}
void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
@ -134,6 +138,20 @@ public:
(dilation.height == 1 && dilation.width == 1);
}
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
Mat w, b;
top->getScaleShift(w, b);
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
return true;
}
return false;
}
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,
@ -184,11 +202,9 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
public:
enum { VEC_ALIGN = 8, DFT_TYPE = CV_32F };
Mat weightsMat;
std::vector<double> weightsMultipliers;
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
bool newWeightAndBias;
bool fusedBias;
#ifdef HAVE_OPENCL
@ -200,7 +216,6 @@ public:
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
newWeightAndBias = false;
fusedBias = false;
#ifdef HAVE_OPENCL
newActiv = false;
@ -346,19 +361,7 @@ public:
return !activ.empty();
}
virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
{
Mat w, b;
top->getScaleShift(w, b);
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
return true;
}
return false;
}
void fuseWeights(const Mat& w_, const Mat& b_)
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
@ -1238,6 +1241,45 @@ public:
pad.width = pad_l;
pad.height = pad_t;
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);
}
newWeightAndBias = !w.empty() || !b.empty();
}
class MatMulInvoker : public ParallelLoopBody
@ -1505,11 +1547,19 @@ public:
if (umat_weights.empty())
{
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (hasBias())
blobs[1].reshape(1, outCn).copyTo(umat_biases);
if (newWeightAndBias)
{
weightsMat.copyTo(umat_weights);
biasesMat.copyTo(umat_biases);
}
else
umat_biases = UMat::zeros(outCn, 1, CV_32F);
{
transpose(blobs[0].reshape(1, inpCn), umat_weights);
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()));

9
modules/dnn/test/test_backends.cpp
View File

@ -305,9 +305,16 @@ TEST_P(DNNTestNetwork, DenseNet_121)
TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
{
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE <= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("");
#endif
#endif
Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
// Output image has values in range [-143.526, 148.539].

8
modules/dnn/test/test_torch_importer.cpp
View File

@ -394,6 +394,14 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
checkBackend();
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE <= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("");
#endif
#endif
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"};
std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};