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Enable more deconvolution layer configurations with IE backend

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This commit is contained in:
Dmitry Kurtaev 2019-05-28 18:24:25 +03:00
parent 483e9f341c
commit 9c0af1f675
6 changed files with 81 additions and 49 deletions
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3
modules/dnn/perf/perf_net.cpp
View File

@ -213,8 +213,7 @@ PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
{
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
}

2
modules/dnn/src/layers/blank_layer.cpp
View File

@ -122,7 +122,7 @@ public:
else
{
ieLayer.setType("Split");
ieLayer.getParameters()["axis"] = input->dims.size() - 1;
ieLayer.getParameters()["axis"] = (size_t)0;
ieLayer.getParameters()["out_sizes"] = input->dims[0];
}
std::vector<size_t> shape(input->dims);

108
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -61,7 +61,7 @@ namespace dnn
class BaseConvolutionLayerImpl : public ConvolutionLayer
{
public:
bool newWeightAndBias;
bool fusedWeights, fusedBias;
std::vector<double> weightsMultipliers;
BaseConvolutionLayerImpl(const LayerParams &params)
{
@ -90,7 +90,8 @@ public:
CV_Assert(adjustPad.width < stride.width &&
adjustPad.height < stride.height);
}
newWeightAndBias = false;
fusedWeights = false;
fusedBias = false;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
@ -133,6 +134,8 @@ public:
}
pad = Size(pads_begin[1], pads_begin[0]);
}
fusedWeights = false;
fusedBias = false;
}
bool hasBias() const
@ -155,6 +158,8 @@ public:
if (!w.empty() || !b.empty())
{
fuseWeights(w, b);
fusedWeights = fusedWeights || !w.empty();
fusedBias = fusedBias || (hasBias() && !w.empty()) || !b.empty();
return true;
}
return false;
@ -215,7 +220,6 @@ public:
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
bool fusedBias;
#ifdef HAVE_OPENCL
Ptr<OCL4DNNConvSpatial<float> > convolutionOp;
@ -226,7 +230,6 @@ public:
#endif
ConvolutionLayerImpl(const LayerParams &params) : BaseConvolutionLayerImpl(params)
{
fusedBias = false;
#ifdef HAVE_OPENCL
newActiv = false;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
@ -406,9 +409,6 @@ public:
for (int i = 0; i < outCn; ++i)
biasvec[i] += b.at<float>(i);
}
newWeightAndBias = !w.empty() || !b.empty();
fusedBias = hasBias() || !b.empty();
biasvec[outCn] = biasvec[outCn+1] = biasvec[outCn-1];
}
@ -475,12 +475,12 @@ public:
InferenceEngine::Layout::NCDHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (newWeightAndBias)
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(fusedWeights, layout);
Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(cvWeights, layout);
}
else
{
@ -490,8 +490,8 @@ public:
ieWeights->allocate();
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
}
}
InferenceEngine::Blob::Ptr ieBiases;
@ -1015,17 +1015,18 @@ public:
}
}
if ( newWeightAndBias )
if (fusedWeights)
{
weightsMat.copyTo(umat_blobs[0]);
if ( fusedBias )
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
umat_blobs[1] = UMat(biasvec, true);
}
convolutionOp->setBias(fusedBias || hasBias());
newWeightAndBias = false;
fusedWeights = false;
}
if (fusedBias)
{
if ( umat_blobs.size() < 2 )
umat_blobs.resize(2);
umat_blobs[1] = UMat(biasvec, true);
convolutionOp->setBias(true);
fusedBias = false;
}
if ( newActiv )
@ -1070,7 +1071,7 @@ public:
return convolutionOp->Forward(inpMat,
inputs.size() == 2 ? inputs[1] : UMat(),
umat_blobs[0],
(hasBias() || fusedBias) ? umat_blobs[1] : UMat(),
umat_blobs.size() > 1 ? umat_blobs[1] : UMat(),
outMat,
batch_size);
}
@ -1175,16 +1176,34 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if (kernel_size.size() == 3)
CV_Error(Error::StsNotImplemented, "Unsupported deconvolution3D layer");
if (INF_ENGINE_RELEASE >= 2018050000 && (adjustPad.height || adjustPad.width))
return false;
{
if (padMode.empty())
{
if (preferableTarget != DNN_TARGET_CPU && group != 1)
{
if ((adjustPad.height && pad.height) || (adjustPad.width && pad.width))
return false;
}
return pad.width >= adjustPad.width && pad.height >= adjustPad.height;
}
else if (padMode == "SAME")
{
return kernel.width >= pad.width + 1 + adjustPad.width &&
kernel.height >= pad.height + 1 + adjustPad.height;
}
else if (padMode == "VALID")
return false;
}
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (group != 1)
{
return preferableTarget == DNN_TARGET_CPU;
@ -1302,8 +1321,6 @@ public:
{
cv::add(biasesMat, b.reshape(1, numOutput), biasesMat);
}
newWeightAndBias = !w.empty() || !b.empty();
}
class MatMulInvoker : public ParallelLoopBody
@ -1571,14 +1588,15 @@ public:
if (umat_weights.empty())
{
if (newWeightAndBias)
{
if (fusedWeights)
weightsMat.copyTo(umat_weights);
else
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (fusedBias)
biasesMat.copyTo(umat_biases);
}
else
{
transpose(blobs[0].reshape(1, inpCn), umat_weights);
if (hasBias())
blobs[1].reshape(1, outCn).copyTo(umat_biases);
else
@ -1778,6 +1796,19 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
auto ieWeights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (fusedWeights)
{
ieWeights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieWeights->dims());
ieWeights->allocate();
int inpCn = blobs[0].size[0];
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn);
transpose(weightsMat, newWeights);
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
@ -1788,14 +1819,23 @@ public:
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
ieLayer.setPaddingsEnd(pads_end);
if (padMode.empty())
{
ieLayer.setPaddingsEnd({pads_end[0] - adjust_pads[0], pads_end[1] - adjust_pads[1]});
}
else if (padMode == "SAME")
{
ieLayer.setPaddingsEnd({kernel_size[0] - pads_begin[0] - 1 - adjust_pads[0],
kernel_size[1] - pads_begin[1] - 1 - adjust_pads[1]});
}
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW), l);
addConstantData("weights", ieWeights, l);
if (hasBias())
addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C), l);
addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout

3
modules/dnn/test/test_backends.cpp
View File

@ -397,8 +397,9 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
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].
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.28 : 2e-3;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.45 : 2e-3;
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
expectNoFallbacksFromIE(net);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(true, true, false));

6
modules/dnn/test/test_halide_layers.cpp
View File

@ -159,10 +159,6 @@ TEST_P(Deconvolution, Accuracy)
Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && (targetId == DNN_TARGET_CPU || targetId == DNN_TARGET_MYRIAD) &&
dilation.width == 2 && dilation.height == 2)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU
&& hasBias && group != 1)
@ -216,7 +212,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
/*in size*/ Values(Size(5, 6)),
/*kernel*/ Values(Size(3, 1), Size(1, 3)),
/*pad*/ Values(Size(1, 0), Size(0, 1)),
/*dilation*/ Values(Size(1, 1), Size(2, 2)),
/*dilation*/ Values(Size(1, 1)),
/*stride, adj. pad*/ Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)),
/*has bias*/ Bool(),
dnnBackendsAndTargetsWithHalide()

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

@ -172,10 +172,6 @@ TEST_P(Test_Torch_layers, run_depth_concat)
TEST_P(Test_Torch_layers, run_deconv)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
runTorchNet("net_deconv");
}
@ -398,10 +394,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
#if defined INF_ENGINE_RELEASE
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
throw SkipTestException("Test is disabled for OpenVINO <= 2018R5 + MyriadX target");
throw SkipTestException("Test is disabled for MyriadX target");
#endif
checkBackend();