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Merge pull request #22840 from zihaomu:optimze_conv_memory_usage

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DNN: reduce the memory used in convolution layer

* reduce the memory in winograd and disabel the test when usage memory is larger than 2gb.

* remove VERY_LOG tag
This commit is contained in:
Zihao Mu 2022-12-08 20:57:13 +08:00 committed by GitHub
parent ab912329b6
commit 0a650b573b
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 105 additions and 92 deletions
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2
modules/dnn/perf/perf_net.cpp
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@ -198,6 +198,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
@ -220,6 +221,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
PERF_TEST_P_(DNNTestNetwork, YOLOv4)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
if (target == DNN_TARGET_MYRIAD) // not enough resources

5
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -2112,8 +2112,11 @@ public:
int dilation_h = dilations[dilations.size() - 2];
int dilation_w = dilations.back();
// Winograd only works well on input h and w >12.
bool canUseWinograd = useWinograd && inputs[0].size[2] >= 12 && inputs[0].size[3] >= 12;
fastConv2dImpl = initFastConv2d(ngroups, K, C, Hk, Wk, stride_w, stride_h, dilation_w,
dilation_h, pads_begin, pads_end, weightsMat, &biasvec[0], useWinograd);
dilation_h, pads_begin, pads_end, weightsMat, &biasvec[0], canUseWinograd);
}
if (fastConv2dImpl)

147
modules/dnn/src/layers/fast_convolution/fast_convolution.cpp
View File

@ -83,86 +83,85 @@ Ptr<FastConv2d> initFastConv2d(
weightsBufPtr[c*padded_ksize + k] = srcWeights[c*wstep + k];
}});
}
else
else if(conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // winograd
{
if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // winograd
static const float ktm[8][3] = {
{1.0f, 0.0f, 0.0f},
{-2.0f / 9, -2.0f / 9, -2.0f / 9},
{-2.0f / 9, 2.0f / 9, -2.0f / 9},
{1.0f / 90, 1.0f / 45, 2.0f / 45},
{1.0f / 90, -1.0f / 45, 2.0f / 45},
{32.f/45, 16.f/45, 8.f/45},
{32.f/45, -16.f/45, 8.f/45},
{0.0f, 0.0f, 1.0f}
};
// the weights are packed as 6-dim tensor:
// ngroups * ceil((K/ngroups)/KBLOCK) * (W*W/ATOM_SIZE) * (C/ngroups) * KBLOCK * ATOM_SIZE,
// where W is the size of Winograd-transformed kernel (8x8),
// ATOM_SIZE is number of lanes in SIMD register (4 for NEON and FP32),
// KBLOCK is some platform-dependent constant dependent on the number of SIMD registers.
int ksize = _FX_WINO_KSIZE * _FX_WINO_KSIZE;
int Cg = C/ngroups;
int Kg = K/ngroups;
int Kg_nblocks = (Kg + _FX_WINO_KBLOCK - 1)/_FX_WINO_KBLOCK;
size_t nweights = ngroups*Kg_nblocks*Cg*_FX_WINO_KBLOCK*_FX_WINO_AREA;
conv->weightsWinoBuf.reserve(nweights + VEC_ALIGN);
conv->weightsWinoBufPtr = alignPtr(conv->weightsWinoBuf.data(), VEC_ALIGN);
float* wptrWino = conv->weightsWinoBufPtr;
memset(wptrWino, 0, nweights * sizeof(wptrWino[0]));
parallel_for_(Range(0, K), [&](const Range& r0){
float kernelTm[_FX_WINO_AREA];
for (int k = r0.start; k < r0.end; k++)
{
static const float ktm[8][3] = {
{1.0f, 0.0f, 0.0f},
{-2.0f / 9, -2.0f / 9, -2.0f / 9},
{-2.0f / 9, 2.0f / 9, -2.0f / 9},
{1.0f / 90, 1.0f / 45, 2.0f / 45},
{1.0f / 90, -1.0f / 45, 2.0f / 45},
{32.f/45, 16.f/45, 8.f/45},
{32.f/45, -16.f/45, 8.f/45},
{0.0f, 0.0f, 1.0f}
};
int g = k / Kg;
int k_ = k - g*Kg;
int ki = k_ / _FX_WINO_KBLOCK;
int dk = k_ - ki*_FX_WINO_KBLOCK;
// the weights are packed as 6-dim tensor:
// ngroups * ceil((K/ngroups)/KBLOCK) * (W*W/ATOM_SIZE) * (C/ngroups) * KBLOCK * ATOM_SIZE,
// where W is the size of Winograd-transformed kernel (8x8),
// ATOM_SIZE is number of lanes in SIMD register (4 for NEON and FP32),
// KBLOCK is some platform-dependent constant dependent on the number of SIMD registers.
int ksize = _FX_WINO_KSIZE * _FX_WINO_KSIZE;
int Cg = C/ngroups;
int Kg = K/ngroups;
int Kg_nblocks = (Kg + _FX_WINO_KBLOCK - 1)/_FX_WINO_KBLOCK;
size_t nweights = ngroups*Kg_nblocks*Cg*_FX_WINO_KBLOCK*_FX_WINO_AREA;
conv->weightsWinoBuf.reserve(nweights + VEC_ALIGN);
conv->weightsWinoBufPtr = alignPtr(conv->weightsWinoBuf.data(), VEC_ALIGN);
float* wptrWino = conv->weightsWinoBufPtr;
memset(wptrWino, 0, nweights * sizeof(wptrWino[0]));
parallel_for_(Range(0, K), [&](const Range& r0){
float kernelTm[_FX_WINO_AREA];
for (int k = r0.start; k < r0.end; k++)
for (int c = 0; c < Cg; c++)
{
int g = k / Kg;
int k_ = k - g*Kg;
int ki = k_ / _FX_WINO_KBLOCK;
int dk = k_ - ki*_FX_WINO_KBLOCK;
// wstep = Hk*Wk*Cg
const float *kernel0 = srcWeights + k * wstep + c * ksize;
for (int c = 0; c < Cg; c++)
// transform kernel, transposed
const float *k0 = kernel0;
const float *k1 = kernel0 + 3;
const float *k2 = kernel0 + 6;
// h
float tmp[8][3];
for (int i = 0; i < 8; i++)
{
// wstep = Hk*Wk*Cg
const float *kernel0 = srcWeights + k * wstep + c * ksize;
// transform kernel, transposed
const float *k0 = kernel0;
const float *k1 = kernel0 + 3;
const float *k2 = kernel0 + 6;
// h
float tmp[8][3];
for (int i = 0; i < 8; i++)
{
tmp[i][0] = k0[0] * ktm[i][0] + k0[1] * ktm[i][1] + k0[2] * ktm[i][2];
tmp[i][1] = k1[0] * ktm[i][0] + k1[1] * ktm[i][1] + k1[2] * ktm[i][2];
tmp[i][2] = k2[0] * ktm[i][0] + k2[1] * ktm[i][1] + k2[2] * ktm[i][2];
}
// v
for (int j = 0; j < 8; j++)
{
float *tmpp = &tmp[j][0];
for (int i = 0; i < 8; i++)
kernelTm[j * 8 + i] = tmpp[0] * ktm[i][0] + tmpp[1] * ktm[i][1] + tmpp[2] * ktm[i][2];
}
// repack the data.
float* wptr = wptrWino + (g*Kg_nblocks + ki) * Cg *_FX_WINO_KBLOCK*_FX_WINO_AREA +
(c*_FX_WINO_KBLOCK + dk)*_FX_WINO_ATOM_F32;
for (int i = 0; i < _FX_WINO_NATOMS_F32; i++,
wptr += Cg * _FX_WINO_KBLOCK * _FX_WINO_ATOM_F32)
{
CV_Assert(conv->weightsWinoBufPtr <= wptr && wptr + _FX_WINO_ATOM_F32 <= conv->weightsWinoBufPtr + nweights);
memcpy(wptr, kernelTm + i * _FX_WINO_ATOM_F32, _FX_WINO_ATOM_F32*sizeof (wptr[0]));
}
tmp[i][0] = k0[0] * ktm[i][0] + k0[1] * ktm[i][1] + k0[2] * ktm[i][2];
tmp[i][1] = k1[0] * ktm[i][0] + k1[1] * ktm[i][1] + k1[2] * ktm[i][2];
tmp[i][2] = k2[0] * ktm[i][0] + k2[1] * ktm[i][1] + k2[2] * ktm[i][2];
}
}});
}
// v
for (int j = 0; j < 8; j++)
{
float *tmpp = &tmp[j][0];
for (int i = 0; i < 8; i++)
kernelTm[j * 8 + i] = tmpp[0] * ktm[i][0] + tmpp[1] * ktm[i][1] + tmpp[2] * ktm[i][2];
}
// repack the data.
float* wptr = wptrWino + (g*Kg_nblocks + ki) * Cg *_FX_WINO_KBLOCK*_FX_WINO_AREA +
(c*_FX_WINO_KBLOCK + dk)*_FX_WINO_ATOM_F32;
for (int i = 0; i < _FX_WINO_NATOMS_F32; i++,
wptr += Cg * _FX_WINO_KBLOCK * _FX_WINO_ATOM_F32)
{
CV_Assert(conv->weightsWinoBufPtr <= wptr && wptr + _FX_WINO_ATOM_F32 <= conv->weightsWinoBufPtr + nweights);
memcpy(wptr, kernelTm + i * _FX_WINO_ATOM_F32, _FX_WINO_ATOM_F32*sizeof (wptr[0]));
}
}
}});
}
else if (conv->conv_type == _FX_CONV_TYPE_GENERIC)
{
// The weights are packed as
// ngroups x (ceil((K/ngroups)/CONV_MR)*CONV_MR) x (Cg*Hk*Wk) x CONV_MR tensor
int Kg = K/ngroups, Cg = max(C/ngroups, 1);
@ -202,6 +201,8 @@ Ptr<FastConv2d> initFastConv2d(
}
}});
}
else
CV_Error(CV_StsUnsupportedFormat, "Unknown convolution type.");
// store bias; append some zero's to make sure that
// we can always read MR elements starting from any valid index
@ -271,7 +272,7 @@ void runFastConv2d(InputArray _input, OutputArray _output, const Ptr<FastConv2d>
CV_Assert(fusedAddMat.empty()); // Depthwise-Convolution layer should not be followed by Add layer.
return runDepthwise(input, output, conv, minval, maxval, activ, ifMinMaxAct);
}
else if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3 && inputShape[2] >= 12 && inputShape[3] >= 12) // winograd
else if (conv->conv_type == _FX_CONV_TYPE_WINOGRAD3X3) // winograd
{
CV_Assert(conv->weightsWinoBufPtr);
if (runWinograd63(input, fusedAddMat, output, conv, ntasks, minval, maxval, activ, ifMinMaxAct))

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

@ -29,7 +29,7 @@ public:
void processNet(std::string weights, std::string proto,
Mat inp, const std::string& outputLayer = "",
std::string halideScheduler = "",
double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2)
double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2, bool useWinograd = true)
{
checkBackend();
l1 = l1 ? l1 : default_l1;
@ -49,6 +49,7 @@ public:
net.setInput(inp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.enableWinograd(useWinograd);
if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
{
halideScheduler = findDataFile(halideScheduler);
@ -347,7 +348,8 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
}
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, iouDiff);
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff,
iouDiff, 0.2, false);
expectNoFallbacksFromIE(net);
}

37
modules/dnn/test/test_darknet_importer.cpp
View File

@ -81,6 +81,7 @@ TEST(Test_Darknet, read_yolo_voc_stream)
Net net = readNetFromDarknet(cfgFile, weightsFile);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.enableWinograd(false);
ref = net.forward();
}
// Import from bytes array.
@ -92,6 +93,7 @@ TEST(Test_Darknet, read_yolo_voc_stream)
Net net = readNetFromDarknet(cfg.data(), cfg.size(), weights.data(), weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.enableWinograd(false);
Mat out = net.forward();
normAssert(ref, out);
}
@ -178,7 +180,8 @@ public:
const std::vector<std::vector<int> >& refClassIds,
const std::vector<std::vector<float> >& refConfidences,
const std::vector<std::vector<Rect2d> >& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
double scoreDiff, double iouDiff, float confThreshold = 0.24,
float nmsThreshold = 0.4, bool useWinograd = true)
{
checkBackend();
@ -198,6 +201,7 @@ public:
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.enableWinograd(useWinograd);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
@ -280,18 +284,19 @@ public:
const std::vector<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
double scoreDiff, double iouDiff, float confThreshold = 0.24,
float nmsThreshold = 0.4, bool useWinograd = true)
{
testDarknetModel(cfg, weights,
std::vector<std::vector<int> >(1, refClassIds),
std::vector<std::vector<float> >(1, refConfidences),
std::vector<std::vector<Rect2d> >(1, refBoxes),
scoreDiff, iouDiff, confThreshold, nmsThreshold);
scoreDiff, iouDiff, confThreshold, nmsThreshold, useWinograd);
}
void testDarknetModel(const std::string& cfg, const std::string& weights,
const cv::Mat& ref, double scoreDiff, double iouDiff,
float confThreshold = 0.24, float nmsThreshold = 0.4)
float confThreshold = 0.24, float nmsThreshold = 0.4, bool useWinograd = true)
{
CV_Assert(ref.cols == 7);
std::vector<std::vector<int> > refClassIds;
@ -318,7 +323,7 @@ public:
refBoxes[batchId].push_back(box);
}
testDarknetModel(cfg, weights, refClassIds, refScores, refBoxes,
scoreDiff, iouDiff, confThreshold, nmsThreshold);
scoreDiff, iouDiff, confThreshold, nmsThreshold, useWinograd);
}
};
@ -396,7 +401,7 @@ TEST_P(Test_Darknet_nets, YoloVoc)
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff, 0.24, 0.4, false);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
@ -410,7 +415,7 @@ TEST_P(Test_Darknet_nets, YoloVoc)
#endif
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, nmsThreshold);
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, nmsThreshold, false);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
@ -599,7 +604,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
{
applyTestTag(
CV_TEST_TAG_LONG,
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_DEBUG_VERYLONG
);
@ -656,7 +661,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, 0.24, 0.4, false);
}
#if defined(INF_ENGINE_RELEASE)
@ -674,7 +679,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
{
SCOPED_TRACE("batch size 2");
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, 0.4, false);
}
}
@ -682,7 +687,7 @@ TEST_P(Test_Darknet_nets, YOLOv4)
{
applyTestTag(
CV_TEST_TAG_LONG,
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_DEBUG_VERYLONG
);
@ -756,7 +761,7 @@ TEST_P(Test_Darknet_nets, YOLOv4)
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, 0.24, 0.4, false);
}
{
@ -792,7 +797,7 @@ TEST_P(Test_Darknet_nets, YOLOv4)
}
#endif
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, 0.4, false);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
@ -877,7 +882,7 @@ TEST_P(Test_Darknet_nets, YOLOv4x_mish)
{
applyTestTag(
CV_TEST_TAG_LONG,
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_DEBUG_VERYLONG
);
@ -939,7 +944,7 @@ TEST_P(Test_Darknet_nets, YOLOv4x_mish)
{
SCOPED_TRACE("batch size 1");
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, 0.24, 0.4, false);
}
{
@ -958,7 +963,7 @@ TEST_P(Test_Darknet_nets, YOLOv4x_mish)
}
#endif
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, 0.4, false);
}
}