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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 2020-11-13 22:29:14 +00:00
commit a7c150ec66
9 changed files with 439 additions and 106 deletions
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4
modules/dnn/perf/perf_convolution.cpp
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@ -533,7 +533,7 @@ struct ConvParamID
CONV_100 = 100, CONV_100 = 100,
CONV_LAST = sizeof(testConvolutionConfigs) / sizeof(testConvolutionConfigs[0]) CONV_LAST = sizeof(testConvolutionConfigs) / sizeof(testConvolutionConfigs[0])
}; };
int val_; \ int val_;
ConvParamID(int val = 0) : val_(val) {} ConvParamID(int val = 0) : val_(val) {}
operator int() const { return val_; } operator int() const { return val_; }
static ::testing::internal::ParamGenerator<ConvParamID> all() static ::testing::internal::ParamGenerator<ConvParamID> all()
@ -546,7 +546,7 @@ struct ConvParamID
ConvParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = ConvParamID(i); } // reduce generated code size ConvParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = ConvParamID(i); } // reduce generated code size
return ::testing::ValuesIn(v_, v_ + NUM); return ::testing::ValuesIn(v_, v_ + NUM);
} }
}; \ };
static inline void PrintTo(const ConvParamID& v, std::ostream* os) static inline void PrintTo(const ConvParamID& v, std::ostream* os)
{ {
CV_Assert((int)v >= 0); CV_Assert((int)v < ConvParamID::CONV_LAST); CV_Assert((int)v >= 0); CV_Assert((int)v < ConvParamID::CONV_LAST);

163
modules/dnn/perf/perf_convolution1d.cpp Normal file
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@ -0,0 +1,163 @@
// 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"
#include <opencv2/dnn/shape_utils.hpp>
namespace opencv_test {
struct Conv1DParam_t {
int kernel;
struct BlobShape { int dims[3]; } shapeIn;
int outCN;
int groups;
int stride;
int dilation;
int pad[2];
const char* padMode;
bool hasBias;
double declared_flops;
};
// Details: #12142
static const Conv1DParam_t testConvolution1DConfigs[] = {
{3, {{1, 6, 10}}, 6, 1, 1, 1, {0, 0}, "VALID", true, 1776.},
{3, {{1, 2, 19}}, 2, 2, 2, 1, {1, 1}, "", true, 260.},
{3, {{1, 2, 25}}, 2, 2, 1, 1, {2, 2}, "SAME", false, 650.},
};
struct Conv1DParamID
{
enum {
CONV_0 = 0,
CONV_LAST = sizeof(testConvolution1DConfigs) / sizeof(testConvolution1DConfigs[0])
};
int val_;
Conv1DParamID(int val = 0) : val_(val) {}
operator int() const { return val_; }
static ::testing::internal::ParamGenerator<Conv1DParamID> all()
{
enum { NUM = (int)CONV_LAST };
Conv1DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv1DParamID(i); } // reduce generated code size
return ::testing::ValuesIn(v_, v_ + NUM);
}
};
static inline void PrintTo(const Conv1DParamID& v, std::ostream* os)
{
CV_Assert((int)v >= 0); CV_Assert((int)v < Conv1DParamID::CONV_LAST);
const Conv1DParam_t& p = testConvolution1DConfigs[(int)v];
*os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9)
<< ", K=[" << p.kernel << "]"
<< ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << "}"
<< ", OCN=" << p.outCN;
if (p.groups > 1)
*os << ", G=" << p.groups;
if (p.stride != 1)
*os << ", S=" << p.stride;
if (p.dilation != 1)
*os << ", D=" << p.dilation;
if (p.pad[0] != 0 && p.pad[1] != 0 )
*os << ", P=(" << p.pad[0] << ", " << p.pad[1] << ")";
if (!((std::string)p.padMode).empty())
*os << ", PM=" << ((std::string)p.padMode);
if (p.hasBias)
*os << ", BIAS";
}
typedef tuple<Conv1DParamID, tuple<Backend, Target> > Conv1DTestParam_t;
typedef TestBaseWithParam<Conv1DTestParam_t> Conv1D;
PERF_TEST_P_(Conv1D, conv1d)
{
int test_id = (int)get<0>(GetParam());
ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv1DParamID::CONV_LAST);
const Conv1DParam_t& params = testConvolution1DConfigs[test_id];
double declared_flops = params.declared_flops;
DictValue kernel = DictValue::arrayInt(&params.kernel, 1);
DictValue stride = DictValue::arrayInt(&params.stride, 1);
DictValue pad = DictValue::arrayInt(&params.pad[0], 2);
DictValue dilation = DictValue::arrayInt(&params.dilation, 1);
MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 3);
int outChannels = params.outCN;
int groups = params.groups;
std::string padMode(params.padMode);
bool hasBias = params.hasBias;
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
if (targetId != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
int inChannels = inputShape[1];
int sz[] = {outChannels, inChannels / groups, params.kernel};
Mat weights(3, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_size", kernel);
lp.set("pad", pad);
if (!padMode.empty())
lp.set("pad_mode", padMode);
lp.set("stride", stride);
lp.set("dilation", dilation);
lp.set("num_output", outChannels);
lp.set("group", groups);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inputShape[2]};
Mat input(3, &inpSz[0], CV_32F);
randu(input, -1.0f, 1.0f);
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(input);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
// warmup
Mat output = net.forward();
MatShape netInputShape = shape(input);
size_t weightsMemory = 0, blobsMemory = 0;
net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
int64 flops = net.getFLOPS(netInputShape);
CV_Assert(flops > 0);
std::cout
<< "IN=" << divUp(input.total() * input.elemSize(), 1u<<10) << " Kb " << netInputShape
<< " OUT=" << divUp(output.total() * output.elemSize(), 1u<<10) << " Kb " << shape(output)
<< " Weights(parameters): " << divUp(weightsMemory, 1u<<10) << " Kb"
<< " MFLOPS=" << flops * 1e-6 << std::endl;
TEST_CYCLE()
{
Mat res = net.forward();
}
EXPECT_NEAR(flops, declared_flops, declared_flops * 1e-6);
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/**/, Conv1D, Combine(
Conv1DParamID::all(),
dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
));
} // namespace

4
modules/dnn/perf/perf_convolution3d.cpp
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@ -46,7 +46,7 @@ struct Conv3DParamID
CONV_100 = 16, CONV_100 = 16,
CONV_LAST = sizeof(testConvolution3DConfigs) / sizeof(testConvolution3DConfigs[0]) CONV_LAST = sizeof(testConvolution3DConfigs) / sizeof(testConvolution3DConfigs[0])
}; };
int val_; \ int val_;
Conv3DParamID(int val = 0) : val_(val) {} Conv3DParamID(int val = 0) : val_(val) {}
operator int() const { return val_; } operator int() const { return val_; }
static ::testing::internal::ParamGenerator<Conv3DParamID> all() static ::testing::internal::ParamGenerator<Conv3DParamID> all()
@ -59,7 +59,7 @@ struct Conv3DParamID
Conv3DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv3DParamID(i); } // reduce generated code size Conv3DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv3DParamID(i); } // reduce generated code size
return ::testing::ValuesIn(v_, v_ + NUM); return ::testing::ValuesIn(v_, v_ + NUM);
} }
}; \ };
static inline void PrintTo(const Conv3DParamID& v, std::ostream* os) static inline void PrintTo(const Conv3DParamID& v, std::ostream* os)
{ {
CV_Assert((int)v >= 0); CV_Assert((int)v < Conv3DParamID::CONV_LAST); CV_Assert((int)v >= 0); CV_Assert((int)v < Conv3DParamID::CONV_LAST);

245
modules/dnn/src/layers/convolution_layer.cpp
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@ -121,17 +121,22 @@ public:
MatSize weightShape = blobs.empty() ? inputs[1].size : blobs[0].size; MatSize weightShape = blobs.empty() ? inputs[1].size : blobs[0].size;
CV_Assert(inputs[0].dims == outputs[0].dims); CV_Assert(inputs[0].dims == outputs[0].dims);
if (weightShape.dims() == 3)
{
kernel_size.assign(1, kernel_size[0]);
strides.assign(1, strides[0]);
}
CV_Assert(weightShape.dims() == kernel_size.size() + 2); CV_Assert(weightShape.dims() == kernel_size.size() + 2);
for (int i = 0; i < kernel_size.size(); i++) { for (int i = 0; i < kernel_size.size(); i++) {
CV_Assert(weightShape[i + 2] == kernel_size[i]); CV_Assert(weightShape[i + 2] == kernel_size[i]);
} }
const Mat &input = inputs[0]; const Mat &input = inputs[0];
CV_Assert((input.dims == 4 || input.dims == 5) && (input.type() == CV_32F || input.type() == CV_16S)); CV_Assert(((input.dims == 3 && kernel_size.size() == 1) || input.dims == 4 || input.dims == 5) && (input.type() == CV_32F || input.type() == CV_16S));
for (size_t i = 0; i < outputs.size(); i++) for (size_t i = 0; i < outputs.size(); i++)
{ {
CV_Assert(inputs[i].type() == input.type()); CV_Assert(inputs[i].type() == input.type());
CV_Assert((inputs[i].dims == 4 || inputs[i].dims == 5) && inputs[i].size[1] == input.size[1]); CV_Assert(((input.dims == 3 && kernel_size.size() == 1) || inputs[i].dims == 4 || inputs[i].dims == 5) && inputs[i].size[1] == input.size[1]);
for (int j = 0; j < inputs[i].dims; j++) { for (int j = 0; j < inputs[i].dims; j++) {
CV_Assert(inputs[i].size[j] == input.size[j]); CV_Assert(inputs[i].size[j] == input.size[j]);
} }
@ -302,36 +307,40 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE virtual bool supportBackend(int backendId) CV_OVERRIDE
{ {
size_t ksize = kernel_size.size();
#ifdef HAVE_CUDA
if (backendId == DNN_BACKEND_CUDA) if (backendId == DNN_BACKEND_CUDA)
{ {
/* only convolution 2d and 3d supported */ /* only convolution 2d and 3d supported */
if(kernel_size.size() == 2 || kernel_size.size() == 3) if (ksize == 2 || ksize == 3)
return true; return true;
return false; return false;
} }
#endif
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{ {
if (kernel_size.size() == 3) if (ksize == 1)
return false;
if (ksize == 3)
return preferableTarget == DNN_TARGET_CPU; return preferableTarget == DNN_TARGET_CPU;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableTarget != DNN_TARGET_MYRIAD) && blobs.empty()) if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableTarget != DNN_TARGET_MYRIAD) && blobs.empty())
return false; return false;
return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height); return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
} }
else
#endif #endif
{ if (backendId == DNN_BACKEND_OPENCV)
if (kernel_size.size() == 3) return ksize >= 1 && ksize <= 3;
return (preferableTarget == DNN_TARGET_CPU && backendId == DNN_BACKEND_OPENCV); #ifdef HAVE_HALIDE
else if (kernel_size.size() == 2) if (backendId == DNN_BACKEND_HALIDE)
return backendId == DNN_BACKEND_OPENCV || return ksize == 2 && !blobs.empty();
(backendId == DNN_BACKEND_HALIDE && !blobs.empty()) || #endif
(backendId == DNN_BACKEND_VKCOM && haveVulkan()); #ifdef HAVE_VULKAN
else if (backendId == DNN_BACKEND_VKCOM)
return false; return ksize == 2;
} #endif
return false;
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -383,18 +392,27 @@ public:
inputs_arr.getMatVector(inputs); inputs_arr.getMatVector(inputs);
// prepare weightsMat where each row is aligned and has enough zero padding on the right to // prepare weightsMat where each row is aligned and has enough zero padding on the right to
// use vectorized (i.e. with intrinsics) loops without tail processing // use vectorized (i.e. with intrinsics) loops without tail processing
Mat wm = blobs.empty() ? inputs[1].reshape(1, numOutput) : blobs[0].reshape(1, numOutput); if (!blobs.empty())
if( wm.step1() % VEC_ALIGN != 0 )
{ {
int newcols = (int)alignSize(wm.step1(), VEC_ALIGN); Mat wm = blobs[0].reshape(1, numOutput);
Mat wm_buffer = Mat(numOutput, newcols, wm.type()); if( wm.step1() % VEC_ALIGN != 0 )
Mat wm_padding = wm_buffer.colRange(wm.cols, newcols); {
wm_padding.setTo(Scalar::all(0.)); int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_aligned = wm_buffer.colRange(0, wm.cols); Mat wm_buffer = Mat(numOutput, newcols, wm.type());
wm.copyTo(wm_aligned); Mat wm_padding = wm_buffer.colRange(wm.cols, newcols);
wm = wm_aligned; wm_padding.setTo(Scalar::all(0.));
Mat wm_aligned = wm_buffer.colRange(0, wm.cols);
wm.copyTo(wm_aligned);
wm = wm_aligned;
}
weightsMat = wm;
} }
weightsMat = wm; else
{
// initialized in .forward()
weightsMat.release();
}
weightsMultipliers.assign(numOutput, 1.0); weightsMultipliers.assign(numOutput, 1.0);
Mat biasMat = hasBias() ? blobs[1].reshape(1, numOutput) : Mat(); Mat biasMat = hasBias() ? blobs[1].reshape(1, numOutput) : Mat();
@ -907,8 +925,11 @@ public:
{ {
size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(), size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
1, std::multiplies<size_t>()); 1, std::multiplies<size_t>());
CV_Assert_N( bool isConv1D = input.dims == 3;
(input.dims == 4 || input.dims == 5) && (input.dims == output.dims), bool isConv2D = input.dims == 4;
bool isConv3D = input.dims == 5;
CV_CheckEQ(static_cast<int>(kernel_size.size()), input.dims - 2, "");
CV_Assert_N(input.dims == output.dims,
input.size[0] == output.size[0], input.size[0] == output.size[0],
weights.rows == output.size[1], weights.rows == output.size[1],
weights.cols == (input.size[1]/ngroups)*karea, weights.cols == (input.size[1]/ngroups)*karea,
@ -918,12 +939,15 @@ public:
input.isContinuous(), input.isContinuous(),
output.isContinuous(), output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2); biasvec.size() == (size_t)output.size[1]+2);
CV_Check(weights.step1(), weights.step1() % VEC_ALIGN == 0, "");
CV_CheckType(weights.type(), CV_32FC1, "");
ParallelConv p; ParallelConv p;
p.input_ = &input; p.input_ = &input;
p.weights_ = &weights; p.weights_ = &weights;
p.output_ = &output; p.output_ = &output;
for( int i = 0; i < 4; i++ ) p.outShape[i] = output.size[i]; int max_ind = isConv1D? 3: 4;
for( int i = 0; i < max_ind; i++ ) p.outShape[i] = output.size[i];
p.outShape[1] /= ngroups; p.outShape[1] /= ngroups;
p.kernel_size = kernel_size; p.strides = strides; p.dilations = dilations; p.kernel_size = kernel_size; p.strides = strides; p.dilations = dilations;
@ -935,20 +959,19 @@ public:
int inpCnAll = input.size[1]; int inpCnAll = input.size[1];
int depth = (input.dims == 5) ? input.size[2] : 1; int depth = (input.dims == 5) ? input.size[2] : 1;
int width = input.size[input.dims - 1]; int width = input.size[input.dims - 1];
int height = input.size[input.dims - 2]; int height = isConv1D? 1 : input.size[input.dims - 2];
int inpCn = inpCnAll / ngroups; int inpCn = inpCnAll / ngroups;
bool isConv2D = kernel_size.size() == 2; p.is1x1_ = (isConv2D && kernel_size[0] == 1 && kernel_size[1] == 1 &&
pads_begin[0] == 0 && pads_begin[1] == 0) ||
p.is1x1_ = isConv2D && kernel_size[0] == 1 && kernel_size[1] == 1 && (isConv1D && pads_begin[0] == 0 && kernel_size[0] == 1);
pads_begin[0] == 0 && pads_begin[1] == 0;
p.useAVX = checkHardwareSupport(CPU_AVX) && isConv2D; p.useAVX = checkHardwareSupport(CPU_AVX) && isConv2D;
p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D; p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D;
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D; p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D;
int kernel_d = !isConv2D? kernel_size[0] : 1; int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = kernel_size[kernel_size.size() - 2]; int kernel_h = isConv1D? 1 : kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back(); int kernel_w = kernel_size.back();
int blk_size_cn0 = cvCeil(800./(kernel_w*kernel_h)); int blk_size_cn0 = cvCeil(800./(kernel_w*kernel_h));
@ -958,14 +981,20 @@ public:
ncn = std::min(ncn, inpCn); ncn = std::min(ncn, inpCn);
p.blk_size_cn = ncn; p.blk_size_cn = ncn;
int dil_d = !isConv2D? dilations[0] : 1; int dil_d = isConv3D? dilations[0] : 1;
int dil_h = dilations[dilations.size() - 2]; int dil_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dil_w = dilations.back(); int dil_w = dilations.back();
p.ofstab_.resize(karea * ncn); p.ofstab_.resize(karea * ncn);
int* ofstab = &p.ofstab_[0]; int* ofstab = &p.ofstab_[0];
if (isConv2D) if (isConv1D)
{
for( int k = 0; k < ncn; k++ )
for( int k_c = 0; k_c < kernel_w; k_c++ )
ofstab[k*kernel_w + k_c] = k*width + k_c*dil_w;
}
else if (isConv2D)
{ {
for( int k = 0; k < ncn; k++ ) for( int k = 0; k < ncn; k++ )
for( int k_r = 0; k_r < kernel_h; k_r++ ) for( int k_r = 0; k_r < kernel_h; k_r++ )
@ -994,34 +1023,36 @@ public:
{ {
const int valign = ConvolutionLayerImpl::VEC_ALIGN; const int valign = ConvolutionLayerImpl::VEC_ALIGN;
int ngroups = ngroups_, batchSize = input_->size[0]*ngroups; int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
bool isConv1D = input_->dims == 3;
bool isConv2D = input_->dims == 4; bool isConv2D = input_->dims == 4;
bool isConv3D = input_->dims == 5;
int outW = output_->size[output_->dims - 1]; int outW = output_->size[output_->dims - 1];
int outH = output_->size[output_->dims - 2]; int outH = isConv1D? 1 : output_->size[output_->dims - 2];
int outCn = output_->size[1]/ngroups; int outCn = output_->size[1]/ngroups;
int depth = !isConv2D? input_->size[2] : 1; int depth = isConv3D? input_->size[2] : 1;
int height = input_->size[input_->dims - 2]; int height = isConv1D? 1 : input_->size[input_->dims - 2];
int width = input_->size[input_->dims - 1]; int width = input_->size[input_->dims - 1];
int inpCn = input_->size[1]/ngroups; int inpCn = input_->size[1]/ngroups;
const int nstripes = nstripes_; const int nstripes = nstripes_;
int kernel_d = !isConv2D? kernel_size[0] : 1; int kernel_d = isConv3D? kernel_size[0] : 1;
int kernel_h = kernel_size[kernel_size.size() - 2]; int kernel_h = isConv1D? 1 : kernel_size[kernel_size.size() - 2];
int kernel_w = kernel_size.back(); int kernel_w = kernel_size.back();
int karea = kernel_w*kernel_h*kernel_d; int karea = kernel_w*kernel_h*kernel_d;
int pad_d = !isConv2D? pads_begin[0] : 0; int pad_d = isConv3D? pads_begin[0] : 0;
int pad_t = pads_begin[pads_begin.size() - 2]; int pad_t = isConv1D? 0 : pads_begin[pads_begin.size() - 2];
int pad_l = pads_begin.back(); int pad_l = pads_begin.back();
int stride_d = !isConv2D? strides[0] : 0; int stride_d = isConv3D? strides[0] : 0;
int stride_h = strides[strides.size() - 2]; int stride_h = isConv1D? 0 : strides[strides.size() - 2];
int stride_w = strides.back(); int stride_w = strides.back();
int dilation_d = !isConv2D? dilations[0] : 1; int dilation_d = isConv3D? dilations[0] : 1;
int dilation_h = dilations[dilations.size() - 2]; int dilation_h = isConv1D? 1 : dilations[dilations.size() - 2];
int dilation_w = dilations.back(); int dilation_w = dilations.back();
int i, j, k, d; int i, j, k, d;
@ -1261,7 +1292,71 @@ public:
// do im2row for a part of input tensor // do im2row for a part of input tensor
float* rowbuf = rowbuf0; float* rowbuf = rowbuf0;
if (isConv2D) if (isConv1D)
{
for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
{
int delta = std::min(ofs1 - ofs, outW - out_j);
int out_j1 = out_j + delta;
int in_j = out_j * stride_w - pad_l;
const float* imgptr = data_inp0 + cn0*width + in_j;
ofs += delta;
// do im2row for a part of input tensor
if( is1x1 )
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w )
{
for( k = 0; k < vsz; k++ )
rowbuf[k] = imgptr[k*inpPlaneSize];
}
}
else
{
for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w, in_j += stride_w )
{
// this condition should be true for most of the tensor elements, i.e.
// most of the time the kernel aperture is inside the tensor X-Y plane.
if( out_j + 2 <= out_j1 && 0 <= in_j && in_j + stride_w*2 <= width - (kernel_w-1)*dilation_w )
{
for( k = 0; k < vsz; k++ )
{
int k1 = ofstab[k];
float v0 = imgptr[k1];
float v1 = imgptr[k1 + stride_w];
rowbuf[k] = v0;
rowbuf[k+vsz_a] = v1;
}
out_j++;
rowbuf += vsz_a;
imgptr += stride_w;
in_j += stride_w;
}
else
{
int i0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
int i1 = std::min(kernel_w, (width - in_j + dilation_w-1)/dilation_w);
// here some non-continuous sub-row of the row will not be
// filled from the tensor; we need to make sure that the uncovered
// elements are explicitly set to 0's. the easiest way is to
// set all the elements to 0's before the loop.
memset(rowbuf, 0, vsz*sizeof(rowbuf[0]));
for( k = 0; k < ncn; k++ )
{
for( i = i0; i < i1; i++ )
{
int imgofs = k*width + i*dilation_w;
rowbuf[k*kernel_w + i] = imgptr[imgofs];
}
}
}
}
}
}
}
else if (isConv2D)
{ {
if( is1x1 && stride_w == 1 && stride_h == 1 ) if( is1x1 && stride_w == 1 && stride_h == 1 )
{ {
@ -1494,9 +1589,12 @@ public:
vs12 = v_setzero_f32(), vs13 = v_setzero_f32(); vs12 = v_setzero_f32(), vs13 = v_setzero_f32();
for( k = 0; k < vsz; k += 4, rptr += 4 ) for( k = 0; k < vsz; k += 4, rptr += 4 )
{ {
v_float32x4 w0 = v_load_aligned(wptr0 + k), w1 = v_load_aligned(wptr1 + k); v_float32x4 w0 = v_load_aligned(wptr0 + k);
v_float32x4 r0 = v_load_aligned(rptr), r1 = v_load_aligned(rptr + vsz_a), v_float32x4 w1 = v_load_aligned(wptr1 + k);
r2 = v_load_aligned(rptr + vsz_a*2), r3 = v_load_aligned(rptr + vsz_a*3); v_float32x4 r0 = v_load_aligned(rptr);
v_float32x4 r1 = v_load_aligned(rptr + vsz_a);
v_float32x4 r2 = v_load_aligned(rptr + vsz_a*2);
v_float32x4 r3 = v_load_aligned(rptr + vsz_a*3);
vs00 += w0*r0; vs00 += w0*r0;
vs01 += w0*r1; vs01 += w0*r1;
@ -1566,6 +1664,12 @@ public:
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals) bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{ {
if (kernel_size.size() != 2)
{
// no OpenCL optimizations, see .supportedBacked()
return false;
}
std::vector<UMat> inputs; std::vector<UMat> inputs;
std::vector<UMat> outputs; std::vector<UMat> outputs;
@ -1749,26 +1853,35 @@ public:
if (blobs.empty()) if (blobs.empty())
{ {
Mat wm = inputs[1].reshape(1, outCn); Mat wm = inputs[1].reshape(1, outCn);
if( wm.step1() % VEC_ALIGN != 0 ) if (wm.data != weightsMat.data)
{ {
wm.copyTo(weightsMat); int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_buffer = Mat(numOutput, newcols, wm.type());
Mat wm_padding = wm_buffer.colRange(wm.cols, newcols);
wm_padding.setTo(Scalar::all(0.));
weightsMat = wm_buffer.colRange(0, wm.cols);
wm.copyTo((const Mat&)weightsMat);
if (inputs.size() > 2) if (inputs.size() > 2)
{ {
Mat biasMat = inputs[2].reshape(1, outCn); Mat biasMat = inputs[2].reshape(1, outCn);
biasMat.col(0).copyTo(biasvec); biasMat.col(0).copyTo(biasvec);
biasvec.resize(outCn + 2);
}
else
{
biasvec.resize(outCn + 2, 0);
} }
biasvec.resize(outCn + 2, 0);
} }
} }
/*if (inputs[0].dims > 3) {
/*printf("conv %s: input (%d x %d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n", printf("conv %s: input (%d x %d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n",
name.c_str(), inputs[0].size[0], inputs[0].size[1], inputs[0].size[2], inputs[0].size[3], name.c_str(), inputs[0].size[0], inputs[0].size[1], inputs[0].size[2], inputs[0].size[3],
kernel.width, kernel.height, pad.width, pad.height, kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);*/ stride.width, stride.height, dilation.width, dilation.height);
}
else {
printf("conv %s: input (%d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n",
name.c_str(), inputs[0].size[0], inputs[0].size[1], inputs[0].size[2],
kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);
}*/
int inpGroupCn = blobs.empty() ? inputs[1].size[1] : blobs[0].size[1]; int inpGroupCn = blobs.empty() ? inputs[1].size[1] : blobs[0].size[1];
CV_Assert_N(inputs.size() >= (size_t)1, inputs[0].size[1] % inpGroupCn == 0, CV_Assert_N(inputs.size() >= (size_t)1, inputs[0].size[1] % inpGroupCn == 0,
outputs.size() == 1, inputs[0].data != outputs[0].data); outputs.size() == 1, inputs[0].data != outputs[0].data);

8
modules/dnn/src/onnx/onnx_importer.cpp
View File

@ -200,12 +200,12 @@ LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_prot
if(attribute_name == "kernel_shape") if(attribute_name == "kernel_shape")
{ {
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3); CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("kernel_size", parse(attribute_proto.ints())); lp.set("kernel_size", parse(attribute_proto.ints()));
} }
else if(attribute_name == "strides") else if(attribute_name == "strides")
{ {
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3); CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("stride", parse(attribute_proto.ints())); lp.set("stride", parse(attribute_proto.ints()));
} }
else if(attribute_name == "pads") else if(attribute_name == "pads")
@ -229,7 +229,7 @@ LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_prot
else else
{ {
// Convolution or pooling. // Convolution or pooling.
CV_Assert(attribute_proto.ints_size() == 4 || attribute_proto.ints_size() == 6); CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 4 || attribute_proto.ints_size() == 6);
lp.set("pad", parse(attribute_proto.ints())); lp.set("pad", parse(attribute_proto.ints()));
} }
} }
@ -244,7 +244,7 @@ LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_prot
} }
else if(attribute_name == "dilations") else if(attribute_name == "dilations")
{ {
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3); CV_Assert(attribute_proto.ints_size() == 1 || attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("dilation", parse(attribute_proto.ints())); lp.set("dilation", parse(attribute_proto.ints()));
} }
else if (attribute_proto.has_i()) else if (attribute_proto.has_i())

61
modules/dnn/test/test_onnx_importer.cpp
View File

@ -192,9 +192,14 @@ TEST_P(Test_ONNX_layers, Convolution3D)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif #endif
if (target != DNN_TARGET_CPU && backend != DNN_BACKEND_CUDA)
throw SkipTestException("Only CPU and CUDA is supported");
testONNXModels("conv3d"); 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"); testONNXModels("conv3d_bias");
} }
@ -681,6 +686,58 @@ TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6)
testONNXModels("resize_opset11_torch1.6"); testONNXModels("resize_opset11_torch1.6");
} }
TEST_P(Test_ONNX_layers, Conv1d)
{
testONNXModels("conv1d");
}
TEST_P(Test_ONNX_layers, Conv1d_bias)
{
testONNXModels("conv1d_bias");
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight)
{
String basename = "conv1d_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
{
String basename = "conv1d_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets()); INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers class Test_ONNX_nets : public Test_ONNX_layers

11
modules/dnn/test/test_tf_importer.cpp
View File

@ -169,17 +169,10 @@ TEST_P(Test_TensorFlow_layers, Convolution3D)
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000) #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION); applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif #endif
if (backend == DNN_BACKEND_CUDA) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
{
// ok
}
else 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 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) 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 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");
runTensorFlowNet("conv3d"); runTensorFlowNet("conv3d");
} }

45
modules/java/generator/gen_java.py
View File

@ -123,7 +123,7 @@ T_CPP_MODULE = Template(read_contents(os.path.join(SCRIPT_DIR, 'templates/cpp_mo
class GeneralInfo(): class GeneralInfo():
def __init__(self, type, decl, namespaces): def __init__(self, type, decl, namespaces):
self.namespace, self.classpath, self.classname, self.name = self.parseName(decl[0], namespaces) self.symbol_id, self.namespace, self.classpath, self.classname, self.name = self.parseName(decl[0], namespaces)
# parse doxygen comments # parse doxygen comments
self.params={} self.params={}
@ -159,13 +159,13 @@ class GeneralInfo():
break break
pieces = localName.split(".") pieces = localName.split(".")
if len(pieces) > 2: # <class>.<class>.<class>.<name> if len(pieces) > 2: # <class>.<class>.<class>.<name>
return spaceName, ".".join(pieces[:-1]), pieces[-2], pieces[-1] return name, spaceName, ".".join(pieces[:-1]), pieces[-2], pieces[-1]
elif len(pieces) == 2: # <class>.<name> elif len(pieces) == 2: # <class>.<name>
return spaceName, pieces[0], pieces[0], pieces[1] return name, spaceName, pieces[0], pieces[0], pieces[1]
elif len(pieces) == 1: # <name> elif len(pieces) == 1: # <name>
return spaceName, "", "", pieces[0] return name, spaceName, "", "", pieces[0]
else: else:
return spaceName, "", "" # error?! return name, spaceName, "", "" # error?!
def fullName(self, isCPP=False): def fullName(self, isCPP=False):
result = ".".join([self.fullClass(), self.name]) result = ".".join([self.fullClass(), self.name])
@ -267,8 +267,8 @@ class ClassInfo(GeneralInfo):
def getAllMethods(self): def getAllMethods(self):
result = [] result = []
result.extend([fi for fi in sorted(self.methods) if fi.isconstructor]) result += [fi for fi in self.methods if fi.isconstructor]
result.extend([fi for fi in sorted(self.methods) if not fi.isconstructor]) result += [fi for fi in self.methods if not fi.isconstructor]
return result return result
def addMethod(self, fi): def addMethod(self, fi):
@ -387,7 +387,7 @@ class JavaWrapperGenerator(object):
self.clear() self.clear()
def clear(self): def clear(self):
self.namespaces = set(["cv"]) self.namespaces = ["cv"]
self.classes = { "Mat" : ClassInfo([ 'class Mat', '', [], [] ], self.namespaces) } self.classes = { "Mat" : ClassInfo([ 'class Mat', '', [], [] ], self.namespaces) }
self.module = "" self.module = ""
self.Module = "" self.Module = ""
@ -530,9 +530,9 @@ class JavaWrapperGenerator(object):
includes.append('#include "' + hdr + '"') includes.append('#include "' + hdr + '"')
for hdr in srcfiles: for hdr in srcfiles:
decls = parser.parse(hdr) decls = parser.parse(hdr)
self.namespaces = parser.namespaces self.namespaces = sorted(parser.namespaces)
logging.info("\n\n===== Header: %s =====", hdr) logging.info("\n\n===== Header: %s =====", hdr)
logging.info("Namespaces: %s", parser.namespaces) logging.info("Namespaces: %s", sorted(parser.namespaces))
if decls: if decls:
includes.append('#include "' + hdr + '"') includes.append('#include "' + hdr + '"')
else: else:
@ -554,7 +554,7 @@ class JavaWrapperGenerator(object):
moduleCppCode = StringIO() moduleCppCode = StringIO()
package_path = os.path.join(output_java_path, module) package_path = os.path.join(output_java_path, module)
mkdir_p(package_path) mkdir_p(package_path)
for ci in self.classes.values(): for ci in sorted(self.classes.values(), key=lambda x: x.symbol_id):
if ci.name == "Mat": if ci.name == "Mat":
continue continue
ci.initCodeStreams(self.Module) ci.initCodeStreams(self.Module)
@ -578,7 +578,7 @@ class JavaWrapperGenerator(object):
report.write("\n".join(self.ported_func_list)) report.write("\n".join(self.ported_func_list))
report.write("\n\nSKIPPED FUNCs LIST (%i of %i):\n\n" % (len(self.skipped_func_list), total_count)) report.write("\n\nSKIPPED FUNCs LIST (%i of %i):\n\n" % (len(self.skipped_func_list), total_count))
report.write("".join(self.skipped_func_list)) report.write("".join(self.skipped_func_list))
for i in self.def_args_hist.keys(): for i in sorted(self.def_args_hist.keys()):
report.write("\n%i def args - %i funcs" % (i, self.def_args_hist[i])) report.write("\n%i def args - %i funcs" % (i, self.def_args_hist[i]))
return report.getvalue() return report.getvalue()
@ -1048,10 +1048,11 @@ JNIEXPORT $rtype JNICALL Java_org_opencv_${module}_${clazz}_$fname
if ci.consts: if ci.consts:
enumTypes = set(map(lambda c: c.enumType, ci.consts)) enumTypes = set(map(lambda c: c.enumType, ci.consts))
grouped_consts = {enumType: [c for c in ci.consts if c.enumType == enumType] for enumType in enumTypes} grouped_consts = {enumType: [c for c in ci.consts if c.enumType == enumType] for enumType in enumTypes}
for typeName, consts in grouped_consts.items(): for typeName in sorted(grouped_consts.keys(), key=lambda x: str(x) if x is not None else ""):
consts = grouped_consts[typeName]
logging.info("%s", consts) logging.info("%s", consts)
if typeName: if typeName:
typeName = typeName.rsplit(".", 1)[-1] typeNameShort = typeName.rsplit(".", 1)[-1]
###################### Utilize Java enums ###################### ###################### Utilize Java enums ######################
# ci.j_code.write(""" # ci.j_code.write("""
# public enum {1} {{ # public enum {1} {{
@ -1065,9 +1066,9 @@ JNIEXPORT $rtype JNICALL Java_org_opencv_${module}_${clazz}_$fname
# ) # )
################################################################ ################################################################
ci.j_code.write(""" ci.j_code.write("""
// C++: enum {1} // C++: enum {1} ({2})
public static final int public static final int
{0};\n\n""".format((",\n"+" "*12).join(["%s = %s" % (c.name, c.value) for c in consts]), typeName) {0};\n\n""".format((",\n"+" "*12).join(["%s = %s" % (c.name, c.value) for c in consts]), typeNameShort, typeName)
) )
else: else:
ci.j_code.write(""" ci.j_code.write("""
@ -1092,10 +1093,12 @@ JNIEXPORT $rtype JNICALL Java_org_opencv_${module}_${clazz}_$fname
# manual ports # manual ports
if ci.name in ManualFuncs: if ci.name in ManualFuncs:
for func in ManualFuncs[ci.name].keys(): for func in sorted(ManualFuncs[ci.name].keys()):
ci.j_code.write ( "\n".join(ManualFuncs[ci.name][func]["j_code"]) ) logging.info("manual function: %s", func)
ci.jn_code.write( "\n".join(ManualFuncs[ci.name][func]["jn_code"]) ) fn = ManualFuncs[ci.name][func]
ci.cpp_code.write( "\n".join(ManualFuncs[ci.name][func]["cpp_code"]) ) ci.j_code.write("\n".join(fn["j_code"]))
ci.jn_code.write("\n".join(fn["jn_code"]))
ci.cpp_code.write("\n".join(fn["cpp_code"]))
if ci.name != self.Module or ci.base: if ci.name != self.Module or ci.base:
# finalize() # finalize()
@ -1323,7 +1326,7 @@ if __name__ == "__main__":
# initialize logger # initialize logger
logging.basicConfig(filename='gen_java.log', format=None, filemode='w', level=logging.INFO) logging.basicConfig(filename='gen_java.log', format=None, filemode='w', level=logging.INFO)
handler = logging.StreamHandler() handler = logging.StreamHandler()
handler.setLevel(logging.WARNING) handler.setLevel(os.environ.get('LOG_LEVEL', logging.WARNING))
logging.getLogger().addHandler(handler) logging.getLogger().addHandler(handler)
# parse command line parameters # parse command line parameters

4
modules/python/src2/hdr_parser.py
View File

@ -663,6 +663,10 @@ class CppHeaderParser(object):
stack_top = self.block_stack[-1] stack_top = self.block_stack[-1]
context = stack_top[self.BLOCK_TYPE] context = stack_top[self.BLOCK_TYPE]
if stmt.startswith('inline namespace'):
# emulate anonymous namespace
return "namespace", "", True, None
stmt_type = "" stmt_type = ""
if end_token == "{": if end_token == "{":
stmt_type = "block" stmt_type = "block"