--- /dev/null
+// 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 Conv3DParam_t {
+ int kernel[3];
+ struct BlobShape { int dims[5]; } shapeIn;
+ int outCN;
+ int groups;
+ int stride[3];
+ int dilation[3];
+ int pad[6];
+ const char* padMode;
+ bool hasBias;
+ double declared_flops;
+};
+// Details: #12142
+static const Conv3DParam_t testConvolution3DConfigs[] = {
+ {{3, 3, 3}, {{1, 6, 10, 38, 50}}, 6, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 26956800.},
+ {{3, 3, 3}, {{1, 2, 19, 19, 19}}, 2, 2, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 218000.},
+ {{3, 3, 3}, {{1, 2, 25, 19, 19}}, 2, 2, {1, 2, 2}, {1, 1, 1}, {2, 2, 2, 2, 2, 2}, "SAME", false, 545000.},
+ {{3, 3, 3}, {{1, 11, 9, 150, 200}}, 11, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 1342562760.},
+ {{3, 3, 3}, {{1, 10, 98, 10, 10}}, 10, 1, {1, 1, 1}, {1, 1, 1}, {1, 0, 1, 1, 0,1}, "SAME", false, 53018000.},
+ {{5, 5, 5}, {{1, 6, 19, 19, 19}}, 6, 2, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 30395250.},
+ {{5, 5, 5}, {{1, 4, 50, 19, 19}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 5893888.},
+ {{5, 5, 5}, {{1, 3, 75, 75, 100}}, 3, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", true, 1267312500.},
+ {{5, 5, 5}, {{1, 2, 21, 75, 100}}, 2, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 116103744.},
+ {{5, 5, 5}, {{1, 4, 40, 75, 75}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 93405312.},
+ {{7, 7, 7}, {{1, 6, 15, 19, 19}}, 6, 1, {2, 1, 1}, {1, 1, 1}, {3, 3, 3, 3, 3, 3}, "SAME", true, 71339376.},
+ {{7, 7, 7}, {{1, 2, 38, 38, 38}}, 2, 1, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 44990464.},
+ {{1, 1, 1}, {{1, 4, 9, 10, 10}}, 4, 1, {1, 1, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 16200.},
+ {{3, 1, 4}, {{1, 14, 5, 10, 10}}, 14, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", false, 2359000.},
+ {{1, 1, 1}, {{1, 8, 1, 10, 10}}, 8, 8, {1, 1, 1}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 58752.},
+ {{3, 4, 2}, {{1, 4, 8, 10, 10}}, 4, 4, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 166752.}
+};
+
+struct Conv3DParamID
+{
+ enum {
+ CONV_0 = 0,
+ CONV_100 = 16,
+ CONV_LAST = sizeof(testConvolution3DConfigs) / sizeof(testConvolution3DConfigs[0])
+ };
+ int val_; \
+ Conv3DParamID(int val = 0) : val_(val) {}
+ operator int() const { return val_; }
+ static ::testing::internal::ParamGenerator<Conv3DParamID> all()
+ {
+#if 0
+ enum { NUM = (int)CONV_LAST };
+#else
+ enum { NUM = (int)CONV_100 };
+#endif
+ Conv3DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv3DParamID(i); } // reduce generated code size
+ return ::testing::ValuesIn(v_, v_ + NUM);
+ }
+}; \
+static inline void PrintTo(const Conv3DParamID& v, std::ostream* os)
+{
+ CV_Assert((int)v >= 0); CV_Assert((int)v < Conv3DParamID::CONV_LAST);
+ const Conv3DParam_t& p = testConvolution3DConfigs[(int)v];
+
+ *os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9)
+ << ", K=[" << p.kernel[0] << " x " << p.kernel[1] << " x " << p.kernel[2] << "]"
+ << ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << ", " << p.shapeIn.dims[3] << ", " << p.shapeIn.dims[4] << "}"
+ << ", OCN=" << p.outCN;
+ if (p.groups > 1)
+ *os << ", G=" << p.groups;
+ if (p.stride[0] * p.stride[1] * p.stride[2] != 1)
+ *os << ", S=[" << p.stride[0] << " x " << p.stride[1] << " x " << p.stride[2] << "]";
+ if (p.dilation[0] * p.dilation[1] * p.dilation[2] != 1)
+ *os << ", D=[" << p.dilation[0] << " x " << p.dilation[1] << " x " << p.dilation[2] << "]";
+ if (p.pad[0] != 0 && p.pad[1] != 0 && p.pad[2] != 0 &&
+ p.pad[3] != 0 && p.pad[4] != 0 && p.pad[5] != 0)
+ *os << ", P=(" << p.pad[0] << ", " << p.pad[3] << ") x ("
+ << p.pad[1] << ", " << p.pad[4] << ") x ("
+ << p.pad[2] << ", " << p.pad[5] << ")";
+ if (!((std::string)p.padMode).empty())
+ *os << ", PM=" << ((std::string)p.padMode);
+ if (p.hasBias)
+ *os << ", BIAS";
+}
+
+
+typedef tuple<Conv3DParamID, tuple<Backend, Target> > Conv3DTestParam_t;
+typedef TestBaseWithParam<Conv3DTestParam_t> Conv3D;
+
+PERF_TEST_P_(Conv3D, conv3d)
+{
+ int test_id = (int)get<0>(GetParam());
+ ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv3DParamID::CONV_LAST);
+ const Conv3DParam_t& params = testConvolution3DConfigs[test_id];
+ double declared_flops = params.declared_flops;
+
+ DictValue kernel = DictValue::arrayInt(¶ms.kernel[0], 3);
+ DictValue stride = DictValue::arrayInt(¶ms.stride[0], 3);
+ DictValue pad = DictValue::arrayInt(¶ms.pad[0], 6);
+ DictValue dilation = DictValue::arrayInt(¶ms.dilation[0], 3);
+
+ MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 5);
+ 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[0], params.kernel[1], params.kernel[2]};
+ Mat weights(5, &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], inputShape[3], inputShape[4]};
+ Mat input(5, &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);
+
+ 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(/**/, Conv3D, Combine(
+ Conv3DParamID::all(),
+ dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
+));
+
+} // namespace
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
+#include <numeric>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
}
else
#endif
- return (kernel_size.size() == 2) && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE);
+ return (kernel_size.size() == 3 && preferableTarget == DNN_TARGET_CPU && backendId == DNN_BACKEND_OPENCV) ||
+ (kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const Mat* input_;
const Mat* weights_;
Mat* output_;
- int outShape[4];
- Size kernel_, pad_, stride_, dilation_;
+ int outShape[4]; // used only for conv2d
+ std::vector<size_t> kernel_size, pads_begin, pads_end, strides, dilations;
int ngroups_, nstripes_;
std::vector<int> ofstab_;
const std::vector<float>* biasvec_;
static void run( const Mat& input, Mat& output, const Mat& weights,
const std::vector<float>& biasvec,
const std::vector<float>& reluslope,
- Size kernel, Size pad, Size stride, Size dilation,
+ const std::vector<size_t>& kernel_size, const std::vector<size_t>& strides,
+ const std::vector<size_t>& pads_begin, const std::vector<size_t>& pads_end,
+ const std::vector<size_t>& dilations,
const ActivationLayer* activ, int ngroups, int nstripes )
{
+ size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
+ 1, std::multiplies<size_t>());
CV_Assert_N(
- input.dims == 4 && output.dims == 4,
+ (input.dims == 4 || input.dims == 5) && (input.dims == output.dims),
input.size[0] == output.size[0],
weights.rows == output.size[1],
- weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
+ weights.cols == (input.size[1]/ngroups)*karea,
input.type() == output.type(),
input.type() == weights.type(),
input.type() == CV_32FC1,
p.output_ = &output;
for( int i = 0; i < 4; i++ ) p.outShape[i] = output.size[i];
p.outShape[1] /= ngroups;
- p.kernel_ = kernel; p.pad_ = pad; p.stride_ = stride; p.dilation_ = dilation;
+
+ p.kernel_size = kernel_size; p.strides = strides; p.dilations = dilations;
+ p.pads_begin = pads_begin; p.pads_end = pads_end;
+
p.ngroups_ = ngroups;
p.nstripes_ = nstripes;
- int inpCnAll = input.size[1], width = input.size[3], height = input.size[2];
+ int inpCnAll = input.size[1];
+ int depth = (input.dims == 5) ? input.size[2] : 1;
+ int width = input.size[input.dims - 1];
+ int height = input.size[input.dims - 2];
int inpCn = inpCnAll / ngroups;
- p.is1x1_ = kernel == Size(1,1) && pad == Size(0, 0);
- p.useAVX = checkHardwareSupport(CPU_AVX);
- p.useAVX2 = checkHardwareSupport(CPU_AVX2);
- p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
+
+ 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.useAVX = checkHardwareSupport(CPU_AVX) && isConv2D;
+ p.useAVX2 = checkHardwareSupport(CPU_AVX2) && isConv2D;
+ p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX && isConv2D;
int ncn = std::min(inpCn, (int)BLK_SIZE_CN);
- p.ofstab_.resize(kernel.width*kernel.height*ncn);
+
+ int kernel_d = !isConv2D? kernel_size[0] : 1;
+ int kernel_h = kernel_size[kernel_size.size() - 2];
+ int kernel_w = kernel_size.back();
+
+ int dil_d = !isConv2D? dilations[0] : 1;
+ int dil_h = dilations[dilations.size() - 2];
+ int dil_w = dilations.back();
+
+ p.ofstab_.resize(karea * ncn);
int* ofstab = &p.ofstab_[0];
- for( int k = 0; k < ncn; k++ )
- for( int k_r = 0; k_r < kernel.height; k_r++ )
- for( int k_c = 0; k_c < kernel.width; k_c++ )
- ofstab[(k*kernel.height + k_r)*kernel.width + k_c] =
- (k*height + k_r*dilation.height)*width + k_c*dilation.width;
+ if (isConv2D)
+ {
+ for( int k = 0; k < ncn; k++ )
+ for( int k_r = 0; k_r < kernel_h; k_r++ )
+ for( int k_c = 0; k_c < kernel_w; k_c++ )
+ ofstab[(k*kernel_h + k_r)*kernel_w + k_c] =
+ (k*height + k_r*dil_h)*width + k_c*dil_w;
+ }
+ else
+ {
+ for( int k = 0; k < ncn; k++ )
+ for (int k_d = 0; k_d < kernel_d; k_d++)
+ for( int k_r = 0; k_r < kernel_h; k_r++ )
+ for( int k_c = 0; k_c < kernel_w; k_c++ )
+ ofstab[(k*kernel_d*kernel_h + k_d*kernel_h + k_r)*kernel_w + k_c] =
+ (k*depth*height + k_d*dil_d*height + k_r*dil_h)*width + k_c*dil_w;
+ }
p.biasvec_ = &biasvec;
p.reluslope_ = &reluslope;
{
const int valign = ConvolutionLayerImpl::VEC_ALIGN;
int ngroups = ngroups_, batchSize = input_->size[0]*ngroups;
- int outW = output_->size[3], outH = output_->size[2], outCn = output_->size[1]/ngroups;
- int width = input_->size[3], height = input_->size[2], inpCn = input_->size[1]/ngroups;
+ bool isConv2D = input_->dims == 4;
+
+ int outW = output_->size[output_->dims - 1];
+ int outH = output_->size[output_->dims - 2];
+ int outCn = output_->size[1]/ngroups;
+
+ int depth = !isConv2D? input_->size[2] : 1;
+ int height = input_->size[input_->dims - 2];
+ int width = input_->size[input_->dims - 1];
+ int inpCn = input_->size[1]/ngroups;
+
const int nstripes = nstripes_;
- int kernel_w = kernel_.width, kernel_h = kernel_.height;
- int pad_w = pad_.width, pad_h = pad_.height;
- int stride_w = stride_.width, stride_h = stride_.height;
- int dilation_w = dilation_.width, dilation_h = dilation_.height;
- int karea = kernel_w*kernel_h;
- int i, j, k;
- size_t inpPlaneSize = width*height;
- size_t outPlaneSize = outW*outH;
+
+ int kernel_d = !isConv2D? kernel_size[0] : 1;
+ int kernel_h = kernel_size[kernel_size.size() - 2];
+ int kernel_w = kernel_size.back();
+ int karea = kernel_w*kernel_h*kernel_d;
+
+ int pad_d = !isConv2D? pads_begin[0] : 0;
+ int pad_t = pads_begin[pads_begin.size() - 2];
+ int pad_l = pads_begin.back();
+
+ int stride_d = !isConv2D? strides[0] : 0;
+ int stride_h = strides[strides.size() - 2];
+ int stride_w = strides.back();
+
+ int dilation_d = !isConv2D? dilations[0] : 1;
+ int dilation_h = dilations[dilations.size() - 2];
+ int dilation_w = dilations.back();
+
+ int i, j, k, d;
+ size_t inpPlaneSize = input_->total(2);
+ size_t outPlaneSize = output_->total(2);
bool is1x1 = is1x1_;
int stripesPerSample;
for( int ofs0 = stripeStart; ofs0 < stripeEnd; ofs0 += BLK_SIZE )
{
int ofs, ofs1 = std::min(ofs0 + BLK_SIZE, stripeEnd);
- int out_i = ofs0 / outW;
- int out_j = ofs0 - out_i * outW;
+
+ int out_d = ofs0 / (outH * outW);
+ int out_i = (ofs0 - out_d * outH * outW) / outW;
+ int out_j = ofs0 % outW;
// do im2row for a part of input tensor
float* rowbuf = rowbuf0;
- for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
+
+ if (isConv2D)
{
- int delta = std::min(ofs1 - ofs, outW - out_j);
- int out_j1 = out_j + delta;
- int in_i = out_i * stride_h - pad_h;
- int in_j = out_j * stride_w - pad_w;
- const float* imgptr = data_inp0 + (cn0*height + in_i)*width + in_j;
- ofs += delta;
-
- // do im2row for a part of input tensor
- if( is1x1 )
+ for( ofs = ofs0; ofs < ofs1; out_j = 0, ++out_i )
{
- for( ; out_j < out_j1; out_j++, rowbuf += vsz_a, imgptr += stride_w )
+ int delta = std::min(ofs1 - ofs, outW - out_j);
+ int out_j1 = out_j + delta;
+
+ int in_i = out_i * stride_h - pad_t;
+ int in_j = out_j * stride_w - pad_l;
+ const float* imgptr = data_inp0 + (cn0*height + in_i)*width + in_j;
+ ofs += delta;
+
+ // do im2row for a part of input tensor
+ if( is1x1 )
{
- for( k = 0; k < vsz; k++ )
- rowbuf[k] = imgptr[k*inpPlaneSize];
+ 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
+ {
+ bool ok_i = 0 <= in_i && in_i < height - (kernel_h-1)*dilation_h;
+ int i0 = std::max(0, (-in_i + dilation_h-1)/dilation_h);
+ int i1 = std::min(kernel_h, (height - in_i + dilation_h-1)/dilation_h);
+
+ 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( ok_i && 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 j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
+ int j1 = 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++ )
+ {
+ for( j = j0; j < j1; j++ )
+ {
+ int imgofs = k*(width*height) + i*(dilation_h*width) + j*dilation_w;
+ rowbuf[(k*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
+ }
+ }
+ }
+ }
+ }
}
}
- else
+ }
+ else
+ {
+ for( ofs = ofs0; ofs < ofs1; out_d += (out_i + 1) / outH, out_i = (out_i + 1) % outH, out_j = 0 )
{
- bool ok_i = 0 <= in_i && in_i < height - (kernel_h-1)*dilation_h;
+ int delta = std::min(ofs1 - ofs, outW - out_j);
+ int out_j1 = out_j + delta;
+
+ int in_d = out_d * stride_d - pad_d;
+ int in_i = out_i * stride_h - pad_t;
+ int in_j = out_j * stride_w - pad_l;
+ const float* imgptr = data_inp0 + (cn0*depth*height + in_d*height + in_i)*width + in_j;
+ ofs += delta;
+
+ int d0 = std::max(0, (-in_d + dilation_d - 1) / dilation_d);
+ int d1 = std::min(kernel_d, (depth - in_d + dilation_d - 1) / dilation_d);
+
int i0 = std::max(0, (-in_i + dilation_h-1)/dilation_h);
int i1 = std::min(kernel_h, (height - in_i + dilation_h-1)/dilation_h);
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( ok_i && out_j + 2 <= out_j1 && 0 <= in_j && in_j + stride_w*2 <= width - (kernel_w-1)*dilation_w )
+ int j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
+ int j1 = 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( 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 j0 = std::max(0, (-in_j + dilation_w-1)/dilation_w);
- int j1 = 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 ( d = d0; d < d1; d++)
{
for( i = i0; i < i1; i++ )
{
for( j = j0; j < j1; j++ )
{
- int imgofs = k*(width*height) + i*(dilation_h*width) + j*dilation_w;
- rowbuf[(k*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
+ int imgofs = k*(depth*width*height) + d*dilation_d*width*height + i*(dilation_h*width) + j*dilation_w;
+ rowbuf[(k*kernel_d*kernel_h + d*kernel_h + i)*kernel_w + j] = imgptr[imgofs];
}
}
}
CV_Assert_N(inputs.size() == (size_t)1, inputs[0].size[1] % blobs[0].size[1] == 0,
outputs.size() == 1, inputs[0].data != outputs[0].data);
- if (inputs[0].dims == 5) {
- CV_Error(Error::StsNotImplemented, "Convolution3D layer is not supported on OCV backend");
- }
-
int ngroups = inputs[0].size[1]/blobs[0].size[1];
CV_Assert(outputs[0].size[1] % ngroups == 0);
int outCn = blobs[0].size[0];
int nstripes = std::max(getNumThreads(), 1);
ParallelConv::run(inputs[0], outputs[0], weightsMat, biasvec, reluslope,
- kernel, pad, stride, dilation, activ.get(), ngroups, nstripes);
+ kernel_size, strides, pads_begin, pads_end, dilations, activ.get(), ngroups, nstripes);
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
CV_Assert(inputs.size() == outputs.size());
int64 flops = 0;
+ int karea = std::accumulate(kernel_size.begin(), kernel_size.end(), 1, std::multiplies<size_t>());
for (int i = 0; i < inputs.size(); i++)
{
- flops += total(outputs[i])*(CV_BIG_INT(2)*kernel.area()*inputs[i][1] + 1);
+ flops += total(outputs[i])*(CV_BIG_INT(2)*karea*inputs[i][1] + 1);
}
return flops;
#include "../op_inf_engine.hpp"
#include <float.h>
#include <algorithm>
+#include <numeric>
using std::max;
using std::min;
#endif
}
else
- return (kernel_size.empty() || kernel_size.size() == 2) && (backendId == DNN_BACKEND_OPENCV ||
+ return (kernel_size.size() == 3 && backendId == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU) ||
+ ((kernel_size.empty() || kernel_size.size() == 2) && (backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() &&
- (type == MAX || (type == AVE && !pad_t && !pad_l && !pad_b && !pad_r))));
+ (type == MAX || (type == AVE && !pad_t && !pad_l && !pad_b && !pad_r)))));
}
#ifdef HAVE_OPENCL
int poolingType;
float spatialScale;
+ std::vector<size_t> pads_begin, pads_end;
+ std::vector<size_t> kernel_size;
+ std::vector<size_t> strides;
+
PoolingInvoker() : src(0), rois(0), dst(0), mask(0), avePoolPaddedArea(false), nstripes(0),
computeMaxIdx(0), poolingType(MAX), spatialScale(0) {}
- static void run(const Mat& src, const Mat& rois, Mat& dst, Mat& mask, Size kernel,
- Size stride, int pad_l, int pad_t, int pad_r, int pad_b, bool avePoolPaddedArea, int poolingType, float spatialScale,
+ static void run(const Mat& src, const Mat& rois, Mat& dst, Mat& mask,
+ std::vector<size_t> kernel_size, std::vector<size_t> strides,
+ std::vector<size_t> pads_begin, std::vector<size_t> pads_end,
+ bool avePoolPaddedArea, int poolingType, float spatialScale,
bool computeMaxIdx, int nstripes)
{
CV_Assert_N(
src.isContinuous(), dst.isContinuous(),
src.type() == CV_32F, src.type() == dst.type(),
- src.dims == 4, dst.dims == 4,
- (((poolingType == ROI || poolingType == PSROI) && dst.size[0] == rois.size[0]) || src.size[0] == dst.size[0]),
+ src.dims == 4 || src.dims == 5, dst.dims == 4 || dst.dims == 5,
+ (((poolingType == ROI || poolingType == PSROI) &&
+ dst.size[0] == rois.size[0]) || src.size[0] == dst.size[0]),
poolingType == PSROI || src.size[1] == dst.size[1],
(mask.empty() || (mask.type() == src.type() && mask.size == dst.size)));
p.src = &src;
p.rois = &rois;
p.dst = &dst;
+
+ p.kernel_size = kernel_size;
+ p.strides = strides;
+ p.pads_begin = pads_begin;
+ p.pads_end = pads_end;
+
p.mask = &mask;
- p.kernel = kernel;
- p.stride = stride;
- p.pad_l = pad_l;
- p.pad_t = pad_t;
- p.pad_r = pad_r;
- p.pad_b = pad_b;
+ p.kernel = Size(kernel_size[1], kernel_size[0]);
+ p.stride = Size(strides[1], strides[0]);
+ p.pad_l = pads_begin.back();
+ p.pad_t = pads_begin[pads_begin.size() - 2];
+ p.pad_r = pads_end.back();
+ p.pad_b = pads_end[pads_end.size() - 2];
+
p.avePoolPaddedArea = avePoolPaddedArea;
p.nstripes = nstripes;
p.computeMaxIdx = computeMaxIdx;
if( !computeMaxIdx )
{
- p.ofsbuf.resize(kernel.width*kernel.height);
- for( int i = 0; i < kernel.height; i++ )
- for( int j = 0; j < kernel.width; j++ )
- p.ofsbuf[i*kernel.width + j] = src.size[3]*i + j;
+ int height = src.size[src.dims - 2];
+ int width = src.size[src.dims - 1];
+
+ int kernel_d = (kernel_size.size() == 3) ? kernel_size[0] : 1;
+ int kernel_h = kernel_size[kernel_size.size() - 2];
+ int kernel_w = kernel_size.back();
+
+ p.ofsbuf.resize(kernel_d * kernel_h * kernel_w);
+ for (int i = 0; i < kernel_d; ++i) {
+ for (int j = 0; j < kernel_h; ++j) {
+ for (int k = 0; k < kernel_w; ++k) {
+ p.ofsbuf[i * kernel_h * kernel_w + j * kernel_w + k] = width * height * i + width * j + k;
+ }
+ }
+ }
}
parallel_for_(Range(0, nstripes), p, nstripes);
void operator()(const Range& r) const CV_OVERRIDE
{
- int channels = dst->size[1], width = dst->size[3], height = dst->size[2];
- int inp_width = src->size[3], inp_height = src->size[2];
+ int channels = dst->size[1];
+
+ bool isPool2D = src->dims == 4;
+ int depth = !isPool2D? dst->size[2] : 1;
+ int height = dst->size[dst->dims - 2];
+ int width = dst->size[dst->dims - 1];
+
+ int inp_depth = !isPool2D? src->size[2] : 1;
+ int inp_height = src->size[src->dims - 2];
+ int inp_width = src->size[src->dims - 1];
+
size_t total = dst->total();
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = std::min(r.end*stripeSize, total);
- int kernel_w = kernel.width, kernel_h = kernel.height;
- int stride_w = stride.width, stride_h = stride.height;
+
+ int kernel_d = !isPool2D? kernel_size[0] : 1;
+ int kernel_h = kernel_size[kernel_size.size() - 2];
+ int kernel_w = kernel_size.back();
+
+ int stride_d = !isPool2D? strides[0] : 0;
+ int stride_h = strides[strides.size() - 2];
+ int stride_w = strides.back();
bool compMaxIdx = computeMaxIdx;
#if CV_SIMD128
ofs /= width;
int y0 = (int)(ofs % height);
ofs /= height;
+
+ int d0 = (int)(ofs % depth);
+ ofs /= depth;
+
int c = (int)(ofs % channels);
int n = (int)(ofs / channels);
int ystart, yend;
+ int dstart = 0, dend = 1;
const float *srcData = 0;
if (poolingType == ROI)
}
else
{
+ int pad_d_begin = (pads_begin.size() == 3) ? pads_begin[0] : 0;
+ dstart = d0 * stride_d - pad_d_begin;
+ dend = min(dstart + kernel_d, (int)(inp_depth + pads_end[0]));
+
ystart = y0 * stride_h - pad_t;
yend = min(ystart + kernel_h, inp_height + pad_b);
srcData = src->ptr<float>(n, c);
}
+ int ddelta = dend - dstart;
+ dstart = max(dstart, 0);
+ dend = min(dend, inp_depth);
int ydelta = yend - ystart;
ystart = max(ystart, 0);
yend = min(yend, inp_height);
- float *dstData = dst->ptr<float>(n, c, y0);
- float *dstMaskData = mask->data ? mask->ptr<float>(n, c, y0) : 0;
+ float *dstData = &dst->ptr<float>(n, c, d0)[y0 * width];
+ float *dstMaskData = mask->data ? &mask->ptr<float>(n, c, d0)[y0 * width] : 0;
int delta = std::min((int)(stripeEnd - ofs0), width - x0);
ofs0 += delta;
continue;
}
#if CV_SIMD128
- if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width )
+ if( isPool2D && xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width )
{
if( compMaxIdx )
{
if( compMaxIdx )
{
int max_index = -1;
- for (int y = ystart; y < yend; ++y)
- for (int x = xstart; x < xend; ++x)
- {
- const int index = y * inp_width + x;
- float val = srcData[index];
- if (val > max_val)
+ for (int d = dstart; d < dend; ++d)
+ for (int y = ystart; y < yend; ++y)
+ for (int x = xstart; x < xend; ++x)
{
- max_val = val;
- max_index = index;
+ const int index = d * inp_width * inp_height + y * inp_width + x;
+ float val = srcData[index];
+ if (val > max_val)
+ {
+ max_val = val;
+ max_index = index;
+ }
}
- }
-
dstData[x0] = max_val;
if (dstMaskData)
dstMaskData[x0] = max_index;
}
else
{
- for (int y = ystart; y < yend; ++y)
- for (int x = xstart; x < xend; ++x)
- {
- const int index = y * inp_width + x;
- float val = srcData[index];
- max_val = std::max(max_val, val);
+ for (int d = dstart; d < dend; ++d) {
+ for (int y = ystart; y < yend; ++y) {
+ for (int x = xstart; x < xend; ++x) {
+ const int index = d * inp_width * inp_height + y * inp_width + x;
+ float val = srcData[index];
+ max_val = std::max(max_val, val);
+ }
}
-
+ }
dstData[x0] = max_val;
}
}
}
else if (poolingType == AVE)
{
- for( ; x0 < x1; x0++ )
+ for( ; x0 < x1; ++x0)
{
int xstart = x0 * stride_w - pad_l;
int xend = min(xstart + kernel_w, inp_width + pad_r);
int xdelta = xend - xstart;
xstart = max(xstart, 0);
xend = min(xend, inp_width);
- float inv_kernel_area = avePoolPaddedArea ? xdelta * ydelta : ((yend - ystart) * (xend - xstart));
+ float inv_kernel_area = avePoolPaddedArea ? xdelta * ydelta * ddelta :
+ ((dend - dstart) * (yend - ystart) * (xend - xstart));
inv_kernel_area = 1.0 / inv_kernel_area;
#if CV_SIMD128
- if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width )
+ if( isPool2D && xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width )
{
v_float32x4 sum_val0 = v_setzero_f32(), sum_val1 = v_setzero_f32();
v_float32x4 ikarea = v_setall_f32(inv_kernel_area);
#endif
{
float sum_val = 0.f;
- for (int y = ystart; y < yend; ++y)
- for (int x = xstart; x < xend; ++x)
- {
- const int index = y * inp_width + x;
- float val = srcData[index];
- sum_val += val;
+ for (int d = dstart; d < dend; ++d) {
+ for (int y = ystart; y < yend; ++y) {
+ for (int x = xstart; x < xend; ++x) {
+ const int index = d * inp_width * inp_height + y * inp_width + x;
+ float val = srcData[index];
+ sum_val += val;
+ }
}
-
+ }
dstData[x0] = sum_val*inv_kernel_area;
}
}
{
const int nstripes = getNumThreads();
Mat rois;
- PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad_l, pad_t, pad_r, pad_b, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
+ PoolingInvoker::run(src, rois, dst, mask, kernel_size, strides, pads_begin, pads_end, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
}
void avePooling(Mat &src, Mat &dst)
{
const int nstripes = getNumThreads();
Mat rois, mask;
- PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad_l, pad_t, pad_r, pad_b, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
+ PoolingInvoker::run(src, rois, dst, mask, kernel_size, strides, pads_begin, pads_end, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
}
void roiPooling(const Mat &src, const Mat &rois, Mat &dst)
{
const int nstripes = getNumThreads();
Mat mask;
- PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad_l, pad_t, pad_r, pad_b, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
+ kernel_size.resize(2);
+ strides.resize(2);
+ pads_begin.resize(2);
+ pads_end.resize(2);
+ PoolingInvoker::run(src, rois, dst, mask, kernel_size, strides, pads_begin, pads_end, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
}
virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
{
CV_UNUSED(inputs); // suppress unused variable warning
long flops = 0;
-
+ size_t karea = std::accumulate(kernel_size.begin(), kernel_size.end(),
+ 1, std::multiplies<size_t>());
for(int i = 0; i < outputs.size(); i++)
{
if (type == MAX)
{
if (i%2 == 0)
- flops += total(outputs[i])*kernel.area();
+ flops += total(outputs[i])*karea;
}
else
{
- flops += total(outputs[i])*(kernel.area() + 1);
+ flops += total(outputs[i])*(karea + 1);
}
}
return flops;
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
testONNXModels("conv3d");
testONNXModels("conv3d_bias");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
testONNXModels("max_pool3d");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
testONNXModels("ave_pool3d");
}
+TEST_P(Test_ONNX_layers, PoolConv3D)
+{
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
+ throw SkipTestException("Test is enabled starts from 2019R1");
+#endif
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
+ testONNXModels("pool_conv_3d");
+}
+
TEST_P(Test_ONNX_layers, BatchNormalization)
{
testONNXModels("batch_norm");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
- String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
+ String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx");
Mat image0 = imread(findDataFile("dnn/dog416.png"));
Mat image1 = imread(findDataFile("dnn/street.png"));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
runTensorFlowNet("conv3d");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
runTensorFlowNet("max_pool3d");
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
#endif
- if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
- throw SkipTestException("Only DLIE backend on CPU is supported");
+ if (target != DNN_TARGET_CPU)
+ throw SkipTestException("Only CPU is supported");
runTensorFlowNet("ave_pool3d");
}
projects / platform / upstream / opencv.git / commitdiff