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Merge pull request #16092 from YashasSamaga:cuda4dnn-conv-act-fuse

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cuda4dnn: fuse activations with convolutions

* fuse ReLU, ReLU6, TanH, Sigmoid with conv

* fix OpenCL errors

* improve ReLU, add power, swish and mish

* fix missing fusion entries

* fix handling of unsetAttached

* remove whole file indentation

* optimize power = 1.0, use IDENTITY instead of NONE

* handle edge case: change backend and then clear
This commit is contained in:
Yashas Samaga B L
2019-12-15 00:56:58 +05:30
committed by Alexander Alekhin
parent 5b0b59ecfb
commit 17c485eb03
5 changed files with 545 additions and 4 deletions
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modules/dnn/src/layers/convolution_layer.cpp
+71
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@@ -239,6 +239,12 @@ public:
ocl4dnnFusedActiv_t activType;
float power;
#endif
#ifdef HAVE_CUDA
cuda4dnn::ConvolutionConfiguration::ActivationType cudaActType;
float cuda_relu_slope, cuda_crelu_floor, cuda_crelu_ceil, cuda_power_exp;
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
#ifdef HAVE_OPENCL
@@ -246,6 +252,10 @@ public:
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
power = 0.f;
#endif
#ifdef HAVE_CUDA
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY;
#endif
}
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
@@ -406,6 +416,61 @@ public:
}
}
#endif
#ifdef HAVE_CUDA
cudaActType = cuda4dnn::ConvolutionConfiguration::ActivationType::IDENTITY;
if(IS_DNN_CUDA_TARGET(preferableTarget))
{
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())
{
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)));
}
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)
activ.reset();
}
#endif
return !activ.empty();
}
@@ -1418,6 +1483,12 @@ public:
config.output_shape.assign(std::begin(output_shape), std::end(output_shape));
config.groups = groups;
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;
Mat filtersMat = fusedWeights ? weightsMat : blobs[0];
Mat biasMat = (hasBias() || fusedBias) ? Mat(output_feature_maps, 1, CV_32F, biasvec.data()) : Mat();
if (countNonZero(biasMat) == 0)