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

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This commit is contained in:
Alexander Alekhin
2021-12-03 12:32:49 +00:00
parent c3910807c5 35ff9af6ce
commit 8b4fa2605e
89 changed files with 1116 additions and 542 deletions
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modules/dnn/include/opencv2/dnn/all_layers.hpp
+2
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@@ -545,6 +545,8 @@ CV__DNN_INLINE_NS_BEGIN
class CV_EXPORTS ELULayer : public ActivationLayer
{
public:
float alpha;
static Ptr<ELULayer> create(const LayerParams &params);
};
modules/dnn/perf/perf_recurrent.cpp
+90
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@@ -0,0 +1,90 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "perf_precomp.hpp"
namespace opencv_test {
struct LstmParams {
// Batch size
int nrSamples;
// Size of the input vector
int inputSize;
// Size of the internal state vector
int hiddenSize;
// Number of timesteps for the LSTM
int nrSteps;
};
static inline void PrintTo(const LstmParams& params, ::std::ostream* os) {
(*os) << "BATCH=" << params.nrSamples
<< ", IN=" << params.inputSize
<< ", HIDDEN=" << params.hiddenSize
<< ", TS=" << params.nrSteps;
}
static const LstmParams testLstmConfigs[] = {
{1, 192, 192, 100},
{1, 1024, 192, 100},
{1, 64, 192, 100},
{1, 192, 512, 100},
{64, 192, 192, 2},
{64, 1024, 192, 2},
{64, 64, 192, 2},
{64, 192, 512, 2},
{128, 192, 192, 2},
{128, 1024, 192, 2},
{128, 64, 192, 2},
{128, 192, 512, 2}
};
class Layer_LSTM : public TestBaseWithParam<LstmParams> {};
PERF_TEST_P_(Layer_LSTM, lstm) {
const LstmParams& params = GetParam();
LayerParams lp;
lp.type = "LSTM";
lp.name = "testLstm";
lp.set("produce_cell_output", false);
lp.set("use_timestamp_dim", true);
Mat weightH(params.hiddenSize * 4, params.hiddenSize, CV_32FC1, cv::Scalar(0));
Mat weightX(params.hiddenSize * 4, params.inputSize, CV_32FC1, cv::Scalar(0));
Mat bias(params.hiddenSize * 4, 1, CV_32FC1, cv::Scalar(0));
Mat hInternal(params.nrSteps, params.hiddenSize, CV_32FC1, cv::Scalar(0));
Mat cInternal(params.nrSteps, params.hiddenSize, CV_32FC1, cv::Scalar(0));
lp.blobs.push_back(weightH);
lp.blobs.push_back(weightX);
lp.blobs.push_back(bias);
lp.blobs.push_back(hInternal);
lp.blobs.push_back(cInternal);
std::vector<int> inputDims;
inputDims.push_back(params.nrSamples);
inputDims.push_back(params.nrSteps);
inputDims.push_back(params.inputSize);
Mat input(inputDims.size(), inputDims.data(), CV_32FC1);
input = cv::Scalar(0);
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(input);
// Warm up
std::vector<Mat> outputs(2);
net.forward(outputs, "testLstm");
TEST_CYCLE()
{
net.forward(outputs, "testLstm");
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_LSTM, testing::ValuesIn(testLstmConfigs));
} // namespace
modules/dnn/src/dnn.cpp
+62 -53
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@@ -646,29 +646,26 @@ struct DataLayer : public Layer
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
// FIXIT: add wrapper without exception suppression
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
if (outputs_arr.depth() == CV_16S)
{
forward_fallback(inputs_arr, outputs_arr, internals_arr);
return;
}
bool isFP16 = outputs_arr.depth() == CV_16S;
std::vector<Mat> outputs, internals;
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
// Supported modes:
// | Input type | Output type |
// | fp32 | fp32 |
// | uint8 | fp32 |
for (int i = 0; i < inputsData.size(); ++i)
{
double scale = scaleFactors[i];
Scalar& mean = means[i];
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
if (isFP16)
CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, "");
else
CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
bool singleMean = true;
for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
@@ -678,34 +675,49 @@ struct DataLayer : public Layer
if (singleMean)
{
inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
if (isFP16)
{
Mat input_f32;
inputsData[i].convertTo(input_f32, CV_32F, scale, -mean[0] * scale);
convertFp16(input_f32, outputs[i]);
}
else
{
inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
}
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
{
for (int c = 0; c < inputsData[i].size[1]; ++c)
{
Mat inp = getPlane(inputsData[i], n, c);
Mat out = getPlane(outputs[i], n, c);
inp.convertTo(out, CV_32F, scale, -mean[c] * scale);
if (isFP16)
{
Mat input_f32;
inp.convertTo(input_f32, CV_32F, scale, -mean[c] * scale);
convertFp16(input_f32, out);
}
else
{
inp.convertTo(out, CV_32F, scale, -mean[c] * scale);
}
}
}
}
}
}
#ifdef HAVE_OPENCL
std::vector<Mat> tmp_expressions;
bool forward_ocl(InputArrayOfArrays, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
// Supported modes:
// | Input type | Output type |
// | fp32 | fp32 |
// | fp32 | fp16 |
// | uint8 | fp32 |
bool isFP16 = outputs_.depth() == CV_16S;
std::vector<UMat> outputs;
outputs_.getUMatVector(outputs);
tmp_expressions.clear();
for (int i = 0; i < inputsData.size(); ++i)
{
Mat inputData = inputsData[i];
@@ -713,58 +725,55 @@ struct DataLayer : public Layer
double scale = scaleFactors[i];
Scalar& mean = means[i];
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
CV_Assert(mean == Scalar() || inputData.size[1] <= 4);
if (isFP16)
CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, "");
else
CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
bool singleMean = true;
for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
for (int j = 1; j < std::min(4, inputData.size[1]) && singleMean; ++j)
{
singleMean = mean[j] == mean[j - 1];
}
if (outputs_.depth() == CV_16S)
if (singleMean)
{
if (singleMean)
if (isFP16)
{
tmp_expressions.push_back(Mat(scale * (inputsData[i] - mean[0])));
convertFp16(tmp_expressions.back(), outputs[i]);
UMat input_i;
inputData.convertTo(input_i, CV_32F, scale, -mean[0] * scale);
convertFp16(input_i, outputs[i]);
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
for (int c = 0; c < inputsData[i].size[1]; ++c)
{
Mat inp = getPlane(inputsData[i], n, c);
std::vector<cv::Range> plane(4, Range::all());
plane[0] = Range(n, n + 1);
plane[1] = Range(c, c + 1);
UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size);
tmp_expressions.push_back(scale * (inp - mean[c]));
convertFp16(tmp_expressions.back(), out);
}
inputData.convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
}
}
else
{
CV_Assert(outputs_.depth() == CV_32F);
if (singleMean)
for (int n = 0; n < inputData.size[0]; ++n)
{
inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale);
}
else
{
for (int n = 0; n < inputsData[i].size[0]; ++n)
for (int c = 0; c < inputsData[i].size[1]; ++c)
for (int c = 0; c < inputData.size[1]; ++c)
{
Mat inp = getPlane(inputData, n, c);
std::vector<cv::Range> plane(4, Range::all());
plane[0] = Range(n, n + 1);
plane[1] = Range(c, c + 1);
UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size);
if (isFP16)
{
UMat input_i;
inp.convertTo(input_i, CV_32F, scale, -mean[c] * scale);
convertFp16(input_i, out);
}
else
{
Mat inp = getPlane(inputsData[i], n, c);
std::vector<cv::Range> plane(4, Range::all());
plane[0] = Range(n, n + 1);
plane[1] = Range(c, c + 1);
UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size);
inp.convertTo(out, CV_32F, scale, -mean[c] * scale);
}
}
}
}
}
modules/dnn/src/layers/elementwise_layers.cpp
+14 -4
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@@ -987,6 +987,9 @@ const char* const SigmoidFunctor::BaseDefaultFunctor<SigmoidFunctor>::ocl_kernel
struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
{
typedef ELULayer Layer;
float alpha;
explicit ELUFunctor(float alpha_ = 1.f) : alpha(alpha_) {}
bool supportBackend(int backendId, int)
{
@@ -998,7 +1001,12 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
inline float calculate(float x) const
{
return x >= 0.f ? x : exp(x) - 1.f;
return x >= 0.f ? x : alpha * (exp(x) - 1.f);
}
inline void setKernelParams(ocl::Kernel& kernel) const
{
kernel.set(3, alpha);
}
#ifdef HAVE_CUDA
@@ -1012,7 +1020,7 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
void attachHalide(const Halide::Expr& input, Halide::Func& top)
{
Halide::Var x("x"), y("y"), c("c"), n("n");
top(x, y, c, n) = select(input >= 0.0f, input, exp(input) - 1);
top(x, y, c, n) = select(input >= 0.0f, input, alpha * (exp(input) - 1));
}
#endif // HAVE_HALIDE
@@ -1026,7 +1034,7 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Elu>(node, 1.0);
return std::make_shared<ngraph::op::Elu>(node, alpha);
}
#endif // HAVE_DNN_NGRAPH
@@ -1856,8 +1864,10 @@ Ptr<SigmoidLayer> SigmoidLayer::create(const LayerParams& params)
Ptr<ELULayer> ELULayer::create(const LayerParams& params)
{
Ptr<ELULayer> l(new ElementWiseLayer<ELUFunctor>(ELUFunctor()));
float alpha = params.get<float>("alpha", 1.0f);
Ptr<ELULayer> l(new ElementWiseLayer<ELUFunctor>(ELUFunctor(alpha)));
l->setParamsFrom(params);
l->alpha = alpha;
return l;
}
modules/dnn/src/layers/fully_connected_layer.cpp
+3 -3
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@@ -230,17 +230,17 @@ public:
#if CV_TRY_AVX512_SKX
if( useAVX512 )
opt_AVX512_SKX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
opt_AVX512_SKX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize_aligned);
else
#endif
#if CV_TRY_AVX2
if( useAVX2 )
opt_AVX2::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
opt_AVX2::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize_aligned);
else
#endif
#if CV_TRY_AVX
if( useAVX )
opt_AVX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
opt_AVX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize_aligned);
else
#endif
#if CV_TRY_RVV
modules/dnn/src/layers/layers_common.simd.hpp
+52 -13
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@@ -550,13 +550,24 @@ void fastDepthwiseConv( const float* wptr,
_mm256_zeroupper();
}
// Used to generate the mask used when calculating tails
static const uint32_t tailMaskArray[15] = {
0, 0, 0, 0, 0, 0, 0, 0,
0xffffffffUL, 0xffffffffUL, 0xffffffffUL, 0xffffffffUL, 0xffffffffUL, 0xffffffffUL, 0xffffffffUL
};
// dst = vec * weights^t + bias
// Requires that vecsize is at least 8 or equal to 0 to avoid memory access problems. Does not require alignment.
void fastGEMM1T( const float* vec, const float* weights,
size_t wstep, const float* bias,
float* dst, int nvecs, int vecsize )
{
int i = 0;
CV_Assert(vecsize >= 8 || vecsize == 0);
__m256 tailMask = _mm256_loadu_ps(reinterpret_cast<const float*>(tailMaskArray) + (vecsize % 8));
for( ; i <= nvecs - 8; i += 8 )
{
const float* wptr = weights + i*wstep;
@@ -565,18 +576,36 @@ void fastGEMM1T( const float* vec, const float* weights,
vs4 = _mm256_setzero_ps(), vs5 = _mm256_setzero_ps(),
vs6 = _mm256_setzero_ps(), vs7 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
int k = 0;
for( ; k <= vecsize-8; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
__m256 v = _mm256_loadu_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
vs1 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep), v, vs1);
vs2 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*2), v, vs2);
vs3 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*3), v, vs3);
vs4 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*4), v, vs4);
vs5 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*5), v, vs5);
vs6 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*6), v, vs6);
vs7 = _mm256_fmadd_ps(_mm256_load_ps(wptr + wstep*7), v, vs7);
vs0 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr), v, vs0);
vs1 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep), v, vs1);
vs2 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*2), v, vs2);
vs3 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*3), v, vs3);
vs4 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*4), v, vs4);
vs5 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*5), v, vs5);
vs6 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*6), v, vs6);
vs7 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr + wstep*7), v, vs7);
}
if (k != vecsize) {
// Tail
k = vecsize - 8;
wptr = weights + i * wstep + k;
__m256 v = _mm256_loadu_ps(vec + k);
v = _mm256_and_ps(v, tailMask);
vs0 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr), tailMask), v, vs0);
vs1 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep), tailMask), v, vs1);
vs2 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 2), tailMask), v, vs2);
vs3 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 3), tailMask), v, vs3);
vs4 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 4), tailMask), v, vs4);
vs5 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 5), tailMask), v, vs5);
vs6 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 6), tailMask), v, vs6);
vs7 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr + wstep * 7), tailMask), v, vs7);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs1), _mm256_hadd_ps(vs2, vs3));
@@ -598,10 +627,20 @@ void fastGEMM1T( const float* vec, const float* weights,
const float* wptr = weights + i*wstep;
__m256 vs0 = _mm256_setzero_ps();
for( int k = 0; k < vecsize; k += 8, wptr += 8 )
int k = 0;
for( ; k <= vecsize-8; k += 8, wptr += 8 )
{
__m256 v = _mm256_load_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_load_ps(wptr), v, vs0);
__m256 v = _mm256_loadu_ps(vec + k);
vs0 = _mm256_fmadd_ps(_mm256_loadu_ps(wptr), v, vs0);
}
if (k != vecsize) {
// Tail
k = vecsize - 8;
wptr = weights + i * wstep + k;
__m256 v = _mm256_loadu_ps(vec + k);
v = _mm256_and_ps(v, tailMask);
vs0 = _mm256_fmadd_ps(_mm256_and_ps(_mm256_loadu_ps(wptr), tailMask), v, vs0);
}
__m256 s0 = _mm256_hadd_ps(_mm256_hadd_ps(vs0, vs0), vs0);
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
View File
@@ -87,7 +87,7 @@ public:
if (size % 2 != 1 || size <= 0)
CV_Error(Error::StsBadArg, "LRN layer supports only positive odd values for local_size");
alpha = params.get<double>("alpha", 1);
alpha = params.get<double>("alpha", 0.0001);
beta = params.get<double>("beta", 0.75);
bias = params.get<double>("bias", 1);
normBySize = params.get<bool>("norm_by_size", true);
modules/dnn/src/layers/recurrent_layers.cpp
+100 -3
View File
@@ -46,6 +46,8 @@
#include <cmath>
#include <opencv2/dnn/shape_utils.hpp>
#include "layers_common.hpp"
namespace cv
{
namespace dnn
@@ -118,10 +120,23 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer
ActivationFunction g_activation;
ActivationFunction h_activation;
#if CV_TRY_AVX
bool useAVX;
#endif
#if CV_TRY_AVX2
bool useAVX2;
#endif
public:
LSTMLayerImpl(const LayerParams& params)
: numTimeStamps(0), numSamples(0)
#if CV_TRY_AVX
, useAVX(checkHardwareSupport(CPU_AVX))
#endif
#if CV_TRY_AVX2
, useAVX2(checkHardwareSupport(CPU_AVX2))
#endif
{
setParamsFrom(params);
@@ -343,6 +358,15 @@ public:
hOutTs = hOutTs.colRange(i * hOutTs.cols / numDirs, (i + 1) * hOutTs.cols / numDirs);
Mat cOutTs = produceCellOutput ? output[1].reshape(1, numSamplesTotal) : Mat();
#if CV_TRY_AVX2 || CV_TRY_AVX
bool canUseAvx = gates.isContinuous() && bias.isContinuous()
&& Wx.depth() == CV_32F && gates.depth() == CV_32F
&& bias.depth() == CV_32F && Wx.cols >= 8;
bool canUseAvx_hInternal = hInternal.isContinuous() && gates.isContinuous() && bias.isContinuous()
&& Wh.depth() == CV_32F && hInternal.depth() == CV_32F && gates.depth() == CV_32F
&& Wh.cols >= 8;
#endif
int tsStart, tsEnd, tsInc;
if (reverse || i == 1) {
tsStart = numTimeStamps - 1;
@@ -359,9 +383,82 @@ public:
Range curRowRange(ts*numSamples, (ts + 1)*numSamples);
Mat xCurr = xTs.rowRange(curRowRange);
gemm(xCurr, Wx, 1, gates, 0, gates, GEMM_2_T); // Wx * x_t
gemm(hInternal, Wh, 1, gates, 1, gates, GEMM_2_T); //+Wh * h_{t-1}
gemm(dummyOnes, bias, 1, gates, 1, gates); //+b
#if CV_TRY_AVX2
if (useAVX2 && canUseAvx && xCurr.isContinuous())
{
for (int n = 0; n < xCurr.rows; n++) {
opt_AVX2::fastGEMM1T(
xCurr.ptr<float>(n),
Wx.ptr<float>(),
Wx.step1(),
bias.ptr<float>(),
gates.ptr<float>(n),
Wx.rows,
Wx.cols
);
}
}
else
#endif
#if CV_TRY_AVX
if (useAVX && canUseAvx && xCurr.isContinuous())
{
for (int n = 0; n < xCurr.rows; n++) {
opt_AVX::fastGEMM1T(
xCurr.ptr<float>(n),
Wx.ptr<float>(),
Wx.step1(),
bias.ptr<float>(),
gates.ptr<float>(n),
Wx.rows,
Wx.cols
);
}
}
else
#endif
{
gemm(xCurr, Wx, 1, gates, 0, gates, GEMM_2_T); // Wx * x_t
gemm(dummyOnes, bias, 1, gates, 1, gates); //+b
}
#if CV_TRY_AVX2
if (useAVX2 && canUseAvx_hInternal)
{
for (int n = 0; n < hInternal.rows; n++) {
opt_AVX2::fastGEMM1T(
hInternal.ptr<float>(n),
Wh.ptr<float>(),
Wh.step1(),
gates.ptr<float>(n),
gates.ptr<float>(n),
Wh.rows,
Wh.cols
);
}
}
else
#endif
#if CV_TRY_AVX
if (useAVX && canUseAvx_hInternal)
{
for (int n = 0; n < hInternal.rows; n++) {
opt_AVX::fastGEMM1T(
hInternal.ptr<float>(n),
Wh.ptr<float>(),
Wh.step1(),
gates.ptr<float>(n),
gates.ptr<float>(n),
Wh.rows,
Wh.cols
);
}
}
else
#endif
{
gemm(hInternal, Wh, 1, gates, 1, gates, GEMM_2_T); //+Wh * h_{t-1}
}
Mat gateI = gates.colRange(0*numOut, 1*numOut);
Mat gateF = gates.colRange(1*numOut, 2*numOut);
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
-1
View File
@@ -48,7 +48,6 @@
#include <vector>
#include <fstream>
#include <sys/stat.h>
#include <assert.h>
#include "../include/common.hpp"
#include "../include/ocl4dnn.hpp"
#include "opencl_kernels_dnn.hpp"
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
+59 -11
View File
@@ -110,17 +110,10 @@ private:
opencv_onnx::GraphProto& net;
};
class SoftMaxSubgraph : public Subgraph
class SoftMaxSubgraphBase : public Subgraph
{
public:
SoftMaxSubgraph() : axis(1)
{
int input = addNodeToMatch("");
int inpExp = addNodeToMatch("Exp", input);
int sum = addNodeToMatch("ReduceSum", inpExp);
addNodeToMatch("Div", inpExp, sum);
setFusedNode("Softmax", input);
}
SoftMaxSubgraphBase() : axis(1), id(-1) {}
virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
std::vector<int>& matchedNodesIds,
@@ -128,7 +121,8 @@ public:
{
if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds))
{
Ptr<ImportNodeWrapper> sum = net->getNode(matchedNodesIds[1]);
CV_Assert(id >= 0 && id < matchedNodesIds.size());
Ptr<ImportNodeWrapper> sum = net->getNode(matchedNodesIds[id]);
opencv_onnx::NodeProto* node = sum.dynamicCast<ONNXNodeWrapper>()->node;
for (int i = 0; i < node->attribute_size(); i++)
@@ -156,8 +150,60 @@ public:
attr->set_i(axis);
}
private:
protected:
int axis;
int id;
};
class SoftMaxSubgraph : public SoftMaxSubgraphBase
{
public:
SoftMaxSubgraph()
{
int input = addNodeToMatch("");
int inpExp = addNodeToMatch("Exp", input);
int sum = addNodeToMatch("ReduceSum", inpExp);
id = 1;
addNodeToMatch("Div", inpExp, sum);
setFusedNode("Softmax", input);
}
};
class SoftMaxSubgraph2 : public SoftMaxSubgraphBase {
public:
SoftMaxSubgraph2() {
int input = addNodeToMatch("");
int reducemax = addNodeToMatch("ReduceMax", input);
id = 0;
int sub = addNodeToMatch("Sub", input, reducemax);
int exp = addNodeToMatch("Exp", sub);
int reducesum = addNodeToMatch("ReduceSum", exp, addNodeToMatch(""));
addNodeToMatch("Div", exp, reducesum);
setFusedNode("Softmax", input);
}
};
class LogSoftMaxSubgraph : public SoftMaxSubgraphBase
{
public:
LogSoftMaxSubgraph()
{
int input = addNodeToMatch("");
int reducemax = addNodeToMatch("ReduceMax", input);
id = 0;
int sub_1 = addNodeToMatch("Sub", input, reducemax);
int exp = addNodeToMatch("Exp", sub_1);
int reducesum = addNodeToMatch("ReduceSum", exp, addNodeToMatch(""));
int log = addNodeToMatch("Log", reducesum);
addNodeToMatch("Sub", sub_1, log);
setFusedNode("LogSoftmax", input);
}
};
class NormalizeSubgraphBase : public Subgraph
@@ -577,6 +623,8 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
subgraphs.push_back(makePtr<ResizeSubgraph1>());
subgraphs.push_back(makePtr<ResizeSubgraph2>());
subgraphs.push_back(makePtr<SoftMaxSubgraph>());
subgraphs.push_back(makePtr<SoftMaxSubgraph2>());
subgraphs.push_back(makePtr<LogSoftMaxSubgraph>());
subgraphs.push_back(makePtr<NormalizeSubgraph1>());
subgraphs.push_back(makePtr<NormalizeSubgraph2>());
subgraphs.push_back(makePtr<NormalizeSubgraph2_2>());
modules/dnn/src/onnx/onnx_importer.cpp
+37 -5
View File
@@ -768,12 +768,25 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
}
}
void setCeilMode(LayerParams& layerParams)
{
// auto_pad attribute is deprecated and uses ceil
if (layerParams.has("pad_mode"))
{
layerParams.set("ceil_mode", true);
}
else if (!layerParams.has("ceil_mode"))
{
layerParams.set("ceil_mode", false);
}
}
void ONNXImporter::parseMaxPool(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
int depth = layerParams.get<int>("depth", CV_32F);
layerParams.type = (depth == CV_8S) ? "PoolingInt8" : "Pooling";
layerParams.set("pool", "MAX");
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
setCeilMode(layerParams);
addLayer(layerParams, node_proto);
}
@@ -781,7 +794,7 @@ void ONNXImporter::parseAveragePool(LayerParams& layerParams, const opencv_onnx:
{
layerParams.type = "Pooling";
layerParams.set("pool", "AVE");
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
setCeilMode(layerParams);
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
addLayer(layerParams, node_proto);
}
@@ -1077,6 +1090,7 @@ void ONNXImporter::parseSplit(LayerParams& layerParams, const opencv_onnx::NodeP
}
int depth = layerParams.get<int>("depth", CV_32F);
layerParams.type = (depth == CV_8S) ? "SliceInt8" : "Slice";
layerParams.set("axis", layerParams.get<float>("axis", 0));
addLayer(layerParams, node_proto);
}
@@ -1085,6 +1099,14 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr
opencv_onnx::NodeProto node_proto = node_proto_;
const std::string& layer_type = node_proto.op_type();
bool isSub = layer_type == "Sub";
if (layer_type == "Sum" && node_proto.input_size() == 1)
{
layerParams.type = "Identity";
addLayer(layerParams, node_proto);
return;
}
CV_Assert((node_proto.input_size() == 2) || (layer_type == "Sum" && node_proto.input_size() > 2));
if (layer_type == "Sum" && node_proto.input_size() > 2)
@@ -1382,15 +1404,15 @@ void ONNXImporter::parseImageScaler(LayerParams& layerParams, const opencv_onnx:
void ONNXImporter::parseClip(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
layerParams.type = "ReLU6";
replaceLayerParam(layerParams, "min", "min_value");
replaceLayerParam(layerParams, "max", "max_value");
layerParams.set("min_value", layerParams.get<float>("min", -FLT_MAX));
layerParams.set("max_value", layerParams.get<float>("max", FLT_MAX));
addLayer(layerParams, node_proto);
}
void ONNXImporter::parseLeakyRelu(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
layerParams.type = "ReLU";
replaceLayerParam(layerParams, "alpha", "negative_slope");
layerParams.set("negative_slope", layerParams.get<float>("alpha", 0.01));
addLayer(layerParams, node_proto);
}
@@ -1875,6 +1897,16 @@ void ONNXImporter::parseTranspose(LayerParams& layerParams, const opencv_onnx::N
int depth = layerParams.get<int>("depth", CV_32F);
layerParams.type = (depth == CV_8S) ? "PermuteInt8" : "Permute";
replaceLayerParam(layerParams, "perm", "order");
if (!layerParams.has("order")) {
MatShape inpShape = outShapes[node_proto.input(0)];
size_t dims = inpShape.size();
std::vector<int> perm(dims);
for (size_t d = 0; d < dims; ++d)
{
perm[d] = static_cast<int>(dims - 1 - d);
}
layerParams.set("order", DictValue::arrayInt(perm.data(), perm.size()));
}
CV_Assert(node_proto.input_size() == 1);
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
modules/dnn/src/opencl/activations.cl
+3 -2
View File
@@ -131,13 +131,14 @@ __kernel void PowForward(const int n, __global const T* in, __global T* out,
out[index] = pow(shift + scale * in[index], power);
}
__kernel void ELUForward(const int n, __global const T* in, __global T* out)
__kernel void ELUForward(const int n, __global const T* in, __global T* out,
const KERNEL_ARG_DTYPE alpha)
{
int index = get_global_id(0);
if (index < n)
{
T src = in[index];
out[index] = (src >= 0.f) ? src : exp(src) - 1;
out[index] = (src >= 0.f) ? src : alpha * (exp(src) - 1);
}
}
modules/dnn/test/test_backends.cpp
+1 -1
View File
@@ -274,7 +274,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
{
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
modules/dnn/test/test_caffe_importer.cpp
+22 -2
View File
@@ -112,10 +112,12 @@ TEST(Test_Caffe, read_googlenet)
TEST_P(Test_Caffe_nets, Axpy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
String proto = _tf("axpy.prototxt");
Net net = readNetFromCaffe(proto);
@@ -156,7 +158,12 @@ TEST_P(Test_Caffe_nets, Axpy)
l1 = 2e-4;
lInf = 1e-3;
}
else if(target == DNN_TARGET_CUDA_FP16)
if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.001;
lInf = 0.001;
}
if(target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0002;
lInf = 0.0007;
@@ -688,7 +695,7 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
CV_TEST_TAG_DEBUG_VERYLONG
);
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
@@ -699,6 +706,19 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Ngraph operation Reshape with name rpn_cls_score_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:390:
// While validating node 'v1::Reshape bbox_pred_reshape (bbox_pred[0]:f32{1,84}, Constant_241202[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape':
// Requested output shape {1,6300,4,1} is incompatible with input shape Shape{1, 84}
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166);
modules/dnn/test/test_darknet_importer.cpp
+61 -11
View File
@@ -121,7 +121,7 @@ public:
{
SCOPED_TRACE("batch size 2");
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (target == DNN_TARGET_MYRIAD && name == "shortcut")
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
@@ -442,22 +442,31 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
{
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
std::string prefix = get<0>(GetParam());
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (INF_ENGINE_VER_MAJOR_LT(2019020000) && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
std::string prefix = get<0>(GetParam());
if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4") // NC_OUT_OF_MEMORY
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_GE(2021040000)
if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov3") // NC_OUT_OF_MEMORY
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#else
if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4") // NC_OUT_OF_MEMORY
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif
const int numInputs = 2;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {1, 3, 416, 416};
@@ -485,6 +494,34 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
double l1 = 0.0;
double lInf = 0.0;
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (targetId == DNN_TARGET_MYRIAD && prefix == "yolo-voc")
{
l1 = 0.02;
lInf = 0.15;
}
if (targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolo-voc")
{
l1 = 0.02;
lInf = 0.1;
}
if (targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov3")
{
l1 = 0.001;
lInf = 0.007;
}
if (targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4")
{
l1 = 0.001;
lInf = 0.005;
}
}
#endif
// Run asynchronously. To make test more robust, process inputs in the reversed order.
for (int i = numInputs - 1; i >= 0; --i)
{
@@ -494,7 +531,7 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
ASSERT_TRUE(out.valid());
Mat result;
EXPECT_TRUE(out.get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
normAssert(refs[i], result, format("Index: %d", i).c_str(), l1, lInf);
}
}
@@ -870,10 +907,23 @@ TEST_P(Test_Darknet_layers, avgpool_softmax)
TEST_P(Test_Darknet_layers, region)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && INF_ENGINE_VER_MAJOR_GE(2020020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && INF_ENGINE_VER_MAJOR_GE(2020020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy on CPU, OpenCL
// Expected: (normInf) <= (lInf), actual: 0.763223 vs 0.0001
// |ref| = 1.207319974899292
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
testDarknetLayer("region");
}
modules/dnn/test/test_halide_layers.cpp
+21 -4
View File
@@ -243,9 +243,11 @@ TEST_P(LRN, Accuracy)
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((inSize.width == 5 || inSize.height == 5) && targetId == DNN_TARGET_MYRIAD &&
nrmType == "ACROSS_CHANNELS")
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
LayerParams lp;
lp.set("norm_region", nrmType);
@@ -410,12 +412,14 @@ TEST_P(FullyConnected, Accuracy)
bool hasBias = get<3>(GetParam());
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 ||
(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) {
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
randu(weights, -1.0f, 1.0f);
@@ -435,9 +439,22 @@ TEST_P(FullyConnected, Accuracy)
Mat input(4, &sz[0], CV_32F);
double l1 = 0.0;
double lInf = 0.0;
#if defined(INF_ENGINE_RELEASE)
if (targetId == DNN_TARGET_MYRIAD)
{
l1 = 0.015;
lInf = 0.025;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.01;
}
#endif
if (targetId == DNN_TARGET_CUDA_FP16)
l1 = 0.015;
test(lp, input, backendId, targetId, false, true, l1);
test(lp, input, backendId, targetId, false, l1, lInf);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
@@ -819,18 +836,18 @@ TEST_P(Eltwise, Accuracy)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_OPENCL &&
op == "sum" && numConv == 1 && !weighted)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
modules/dnn/test/test_layers.cpp
+12 -56
View File
@@ -196,13 +196,23 @@ TEST_P(Test_Caffe_layers, DeConvolution)
TEST_P(Test_Caffe_layers, InnerProduct)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Ngraph operation Reshape with name Reshape_4219609 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("layer_inner_product", true);
}
@@ -300,10 +310,12 @@ TEST_P(Test_Caffe_layers, Concat)
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif
testLayerUsingCaffeModels("layer_concat");
@@ -1434,62 +1446,6 @@ INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs_3dim, Combine(
testing::ValuesIn(list_sizes)
));
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_DLDT_two_inputs;
TEST_P(Test_DLDT_two_inputs, as_backend)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
Net net;
LayerParams lp;
lp.type = "Eltwise";
lp.name = "testLayer";
lp.set("operation", "sum");
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
net.connect(0, 1, eltwiseId, 1); // connect to a second input
int inpSize[] = {1, 2, 3, 4};
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 255);
randu(secondInp, 0, 255);
net.setInputsNames({"data", "second_input"});
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
// Output values are in range [0, 637.5].
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
if (targetId == DNN_TARGET_CUDA_FP16)
{
l1 = 0.06;
lInf = 0.3;
}
normAssert(out, ref, "", l1, lInf);
if (cvtest::debugLevel > 0 || HasFailure())
{
std::cout << "input1 scale=" << kScale << " input2 scale=" << kScaleInv << std::endl;
std::cout << "input1: " << firstInp.size << " " << firstInp.reshape(1, 1) << std::endl;
std::cout << "input2: " << secondInp.size << " " << secondInp.reshape(1, 1) << std::endl;
std::cout << "ref: " << ref.reshape(1, 1) << std::endl;
std::cout << "out: " << out.reshape(1, 1) << std::endl;
}
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
dnnBackendsAndTargets()
));
class UnsupportedLayer : public Layer
{
public:
modules/dnn/test/test_misc.cpp
+62 -1
View File
@@ -582,7 +582,8 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all)
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
// Exception: Default implementation fallbacks in asynchronous mode
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -832,4 +833,64 @@ INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
#endif // HAVE_INF_ENGINE
typedef testing::TestWithParam<tuple<MatDepth, MatDepth, tuple<Backend, Target> > > Test_two_inputs;
TEST_P(Test_two_inputs, basic)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
Net net;
LayerParams lp;
lp.type = "Eltwise";
lp.name = "testLayer";
lp.set("operation", "sum");
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
net.connect(0, 1, eltwiseId, 1); // connect to a second input
int inpSize[] = {1, 2, 3, 4};
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 100);
randu(secondInp, 0, 100);
#ifndef CV_CXX11
std::vector<String> input_names;
input_names.push_back("data");
input_names.push_back("second_input");
net.setInputsNames(input_names);
#else
net.setInputsNames({"data", "second_input"});
#endif
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
normAssert(out, ref, "", l1, lInf);
if (cvtest::debugLevel > 0 || HasFailure())
{
std::cout << "input1 scale=" << kScale << " input2 scale=" << kScaleInv << std::endl;
std::cout << "input1: " << firstInp.size << " " << firstInp.reshape(1, 1) << std::endl;
std::cout << "input2: " << secondInp.size << " " << secondInp.reshape(1, 1) << std::endl;
std::cout << "ref: " << ref.reshape(1, 1) << std::endl;
std::cout << "out: " << out.reshape(1, 1) << std::endl;
}
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_two_inputs, Combine(
Values(CV_32F, CV_8U),
Values(CV_32F, CV_8U),
dnnBackendsAndTargets()
));
}} // namespace
modules/dnn/test/test_onnx_importer.cpp
+146 -64
View File
@@ -197,17 +197,11 @@ TEST_P(Test_ONNX_layers, Gather)
TEST_P(Test_ONNX_layers, Convolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("conv3d");
}
TEST_P(Test_ONNX_layers, Convolution3D_bias)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("conv3d_bias");
}
@@ -235,18 +229,73 @@ TEST_P(Test_ONNX_layers, Deconvolution)
TEST_P(Test_ONNX_layers, Deconvolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// ok
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
else if (backend == DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
testONNXModels("deconv3d");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_bias)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
testONNXModels("deconv3d_bias");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_pad)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
testONNXModels("deconv3d_pad");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_adjpad)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
testONNXModels("deconv3d_adjpad");
}
@@ -317,16 +366,14 @@ TEST_P(Test_ONNX_layers, Scale)
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
testONNXModels("reduce_mean3d");
}
@@ -443,13 +490,12 @@ TEST_P(Test_ONNX_layers, Concatenation)
TEST_P(Test_ONNX_layers, Eltwise3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
testONNXModels("eltwise3d");
}
@@ -460,55 +506,56 @@ TEST_P(Test_ONNX_layers, AveragePooling)
TEST_P(Test_ONNX_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// ok
// accuracy
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
testONNXModels("max_pool3d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
testONNXModels("ave_pool3d");
}
TEST_P(Test_ONNX_layers, PoolConv3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
testONNXModels("pool_conv_3d");
}
@@ -1011,6 +1058,7 @@ TEST_P(Test_ONNX_layers, DynamicAxes)
TEST_P(Test_ONNX_layers, MaxPool1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
@@ -1019,11 +1067,20 @@ TEST_P(Test_ONNX_layers, MaxPool1d)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
{
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !expired()
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("maxpooling_1d");
}
TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
@@ -1032,11 +1089,13 @@ TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("maxpooling_sigmoid_1d");
}
TEST_P(Test_ONNX_layers, MaxPool1d_Twise)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
@@ -1045,11 +1104,13 @@ TEST_P(Test_ONNX_layers, MaxPool1d_Twise)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("two_maxpooling_1d");
}
TEST_P(Test_ONNX_layers, AvePool1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
@@ -1058,11 +1119,13 @@ TEST_P(Test_ONNX_layers, AvePool1d)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("average_pooling_1d");
}
TEST_P(Test_ONNX_layers, PoolConv1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
@@ -1071,6 +1134,7 @@ TEST_P(Test_ONNX_layers, PoolConv1d)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("pool_conv_1d");
}
@@ -1257,11 +1321,18 @@ TEST_P(Test_ONNX_nets, Squeezenet)
TEST_P(Test_ONNX_nets, Googlenet)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
const String model = _tf("models/googlenet.onnx", false);
@@ -1523,7 +1594,7 @@ TEST_P(Test_ONNX_nets, DenseNet121)
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
@@ -1533,30 +1604,35 @@ TEST_P(Test_ONNX_nets, Inception_v1)
TEST_P(Test_ONNX_nets, Shufflenet)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#endif
testONNXModels("shufflenet", pb);
}
TEST_P(Test_ONNX_nets, Resnet34_kinetics)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// IE exception: Function contains several inputs and outputs with one friendly name!
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
Mat image0 = imread(findDataFile("dnn/dog416.png"));
@@ -1595,7 +1671,13 @@ TEST_P(Test_ONNX_nets, Resnet34_kinetics)
net.setPreferableTarget(target);
// output range [-5, 11]
float l1 = 0.0013, lInf = 0.009;
float l1 = 0.0013;
float lInf = 0.009;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.02;
lInf = 0.07;
}
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.01;
modules/dnn/test/test_tf_importer.cpp
+77 -26
View File
@@ -216,13 +216,12 @@ TEST_P(Test_TensorFlow_layers, conv_pool_nchw)
TEST_P(Test_TensorFlow_layers, Convolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
runTensorFlowNet("conv3d");
}
@@ -231,7 +230,7 @@ TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("padding_valid");
runTensorFlowNet("spatial_padding");
runTensorFlowNet("mirror_pad");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (target == DNN_TARGET_MYRIAD)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -344,6 +343,7 @@ TEST_P(Test_TensorFlow_layers, concat_axis_1)
TEST_P(Test_TensorFlow_layers, concat_3d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
@@ -353,6 +353,7 @@ TEST_P(Test_TensorFlow_layers, concat_3d)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
runTensorFlowNet("concat_3d");
}
@@ -430,22 +431,32 @@ TEST_P(Test_TensorFlow_layers, batch_norm3D)
TEST_P(Test_TensorFlow_layers, slim_batch_norm)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
// Output values range: [-40.0597, 207.827]
double l1 = default_l1, lInf = default_lInf;
double l1 = default_l1;
double lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
l1 = 0.041;
lInf = 0.33;
}
#if defined(INF_ENGINE_RELEASE)
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
{
lInf = 0.0002;
}
#endif
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.005;
lInf = 0.33;
}
runTensorFlowNet("slim_batch_norm", false, l1, lInf);
}
@@ -572,7 +583,7 @@ TEST_P(Test_TensorFlow_layers, max_pool_grad)
TEST_P(Test_TensorFlow_layers, ave_pool_same)
{
// Reference output values are in range [-0.519531, 0.112976]
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -586,38 +597,41 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same)
TEST_P(Test_TensorFlow_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// ok
// accuracy
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
runTensorFlowNet("max_pool3d");
}
TEST_P(Test_TensorFlow_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
runTensorFlowNet("ave_pool3d");
}
@@ -650,10 +664,12 @@ TEST_P(Test_TensorFlow_layers, matmul)
TEST_P(Test_TensorFlow_layers, reshape)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
runTensorFlowNet("shift_reshape_no_reorder");
runTensorFlowNet("reshape_no_reorder");
runTensorFlowNet("reshape_reduce");
@@ -1319,18 +1335,35 @@ TEST_P(Test_TensorFlow_layers, lstm)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* not supported */
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// Exception: Ngraph operation Reshape with name Reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// Xlink
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
runTensorFlowNet("lstm", true);
runTensorFlowNet("lstm", true, 0.0, 0.0, true);
}
TEST_P(Test_TensorFlow_layers, split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@@ -1360,8 +1393,10 @@ TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_align_corners)
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_half_pixel)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
runTensorFlowNet("resize_nearest_neighbor", false, 0.0, 0.0, false, "_half_pixel");
}
@@ -1500,12 +1535,28 @@ TEST_P(Test_TensorFlow_layers, clip_by_value)
TEST_P(Test_TensorFlow_layers, tf2_prelu)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported; only across channels is supported
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported; only across channels is supported
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// IE exception: Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Eltwise node with name `StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add` has invalid input/output dims configuration
if (target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
runTensorFlowNet("tf2_prelu");
}
modules/dnn/test/test_torch_importer.cpp
+10 -1
View File
@@ -236,23 +236,32 @@ TEST_P(Test_Torch_layers, net_lp_pooling_square)
}
TEST_P(Test_Torch_layers, net_lp_pooling_power)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTorchNet("net_lp_pooling_power", "", false, true);
}
TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
double l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.046;
lInf = 0.023;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.02;
lInf = 0.05;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0042;