mirror of
https://github.com/opencv/opencv.git
synced 2025-06-07 17:44:04 +08:00
628 lines
28 KiB
C++
628 lines
28 KiB
C++
/*M ///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
|
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "../precomp.hpp"
|
|
#include "../op_cuda.hpp"
|
|
#include <opencv2/dnn/shape_utils.hpp>
|
|
#include <opencv2/dnn/all_layers.hpp>
|
|
#include "../nms.inl.hpp"
|
|
|
|
#ifdef HAVE_OPENCL
|
|
#include "opencl_kernels_dnn.hpp"
|
|
#endif
|
|
|
|
#ifdef HAVE_DNN_NGRAPH
|
|
#include "../ie_ngraph.hpp"
|
|
#endif
|
|
|
|
#ifdef HAVE_CUDA
|
|
#include "../cuda4dnn/primitives/region.hpp"
|
|
using namespace cv::dnn::cuda4dnn;
|
|
#endif
|
|
|
|
|
|
namespace cv
|
|
{
|
|
namespace dnn
|
|
{
|
|
|
|
class RegionLayerImpl CV_FINAL : public RegionLayer
|
|
{
|
|
public:
|
|
int coords, classes, anchors, classfix;
|
|
float thresh, scale_x_y;
|
|
bool useSoftmax, useLogistic;
|
|
#ifdef HAVE_OPENCL
|
|
UMat blob_umat;
|
|
#endif
|
|
|
|
RegionLayerImpl(const LayerParams& params)
|
|
{
|
|
setParamsFrom(params);
|
|
CV_Assert(blobs.size() == 1);
|
|
|
|
thresh = params.get<float>("thresh", 0.2);
|
|
coords = params.get<int>("coords", 4);
|
|
classes = params.get<int>("classes", 0);
|
|
anchors = params.get<int>("anchors", 5);
|
|
classfix = params.get<int>("classfix", 0);
|
|
useSoftmax = params.get<bool>("softmax", false);
|
|
useLogistic = params.get<bool>("logistic", false);
|
|
nmsThreshold = params.get<float>("nms_threshold", 0.4);
|
|
scale_x_y = params.get<float>("scale_x_y", 1.0); // Yolov4
|
|
|
|
CV_Assert(nmsThreshold >= 0.);
|
|
CV_Assert(coords == 4);
|
|
CV_Assert(classes >= 1);
|
|
CV_Assert(anchors >= 1);
|
|
CV_Assert(useLogistic || useSoftmax);
|
|
if (params.get<bool>("softmax_tree", false))
|
|
CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
|
|
}
|
|
|
|
bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<MatShape> &outputs,
|
|
std::vector<MatShape> &internals) const CV_OVERRIDE
|
|
{
|
|
CV_Assert(inputs.size() > 0);
|
|
// channels == cell_size*anchors
|
|
CV_Assert(inputs[0][3] == (1 + coords + classes)*anchors);
|
|
int batch_size = inputs[0][0];
|
|
if(batch_size > 1)
|
|
outputs = std::vector<MatShape>(1, shape(batch_size, inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
|
|
else
|
|
outputs = std::vector<MatShape>(1, shape(inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
|
|
return false;
|
|
}
|
|
|
|
virtual bool supportBackend(int backendId) CV_OVERRIDE
|
|
{
|
|
#ifdef HAVE_DNN_NGRAPH
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) && preferableTarget != DNN_TARGET_MYRIAD;
|
|
#endif
|
|
#ifdef HAVE_CUDA
|
|
if (backendId == DNN_BACKEND_CUDA)
|
|
return true;
|
|
#endif
|
|
return backendId == DNN_BACKEND_OPENCV;
|
|
}
|
|
|
|
float logistic_activate(float x) { return 1.F / (1.F + exp(-x)); }
|
|
|
|
void softmax_activate(const float* input, const int n, const float temp, float* output)
|
|
{
|
|
int i;
|
|
float sum = 0;
|
|
float largest = -FLT_MAX;
|
|
for (i = 0; i < n; ++i) {
|
|
if (input[i] > largest) largest = input[i];
|
|
}
|
|
for (i = 0; i < n; ++i) {
|
|
float e = exp((input[i] - largest) / temp);
|
|
sum += e;
|
|
output[i] = e;
|
|
}
|
|
for (i = 0; i < n; ++i) {
|
|
output[i] /= sum;
|
|
}
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
|
|
{
|
|
if (blob_umat.empty())
|
|
blobs[0].copyTo(blob_umat);
|
|
|
|
std::vector<UMat> inputs;
|
|
std::vector<UMat> outputs;
|
|
|
|
// TODO: implement a logistic activation to classification scores.
|
|
if (useLogistic || inps.depth() == CV_16S)
|
|
return false;
|
|
|
|
inps.getUMatVector(inputs);
|
|
outs.getUMatVector(outputs);
|
|
|
|
CV_Assert(inputs.size() >= 1);
|
|
int const cell_size = classes + coords + 1;
|
|
|
|
for (size_t ii = 0; ii < outputs.size(); ii++)
|
|
{
|
|
UMat& inpBlob = inputs[ii];
|
|
UMat& outBlob = outputs[ii];
|
|
|
|
int batch_size = inpBlob.size[0];
|
|
int rows = inpBlob.size[1];
|
|
int cols = inpBlob.size[2];
|
|
|
|
// channels == cell_size*anchors, see l. 94
|
|
int sample_size = cell_size*rows*cols*anchors;
|
|
|
|
ocl::Kernel logistic_kernel("logistic_activ", ocl::dnn::region_oclsrc);
|
|
size_t nanchors = rows*cols*anchors*batch_size;
|
|
logistic_kernel.set(0, (int)nanchors);
|
|
logistic_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
|
|
logistic_kernel.set(2, (int)cell_size);
|
|
logistic_kernel.set(3, ocl::KernelArg::PtrWriteOnly(outBlob));
|
|
logistic_kernel.run(1, &nanchors, NULL, false);
|
|
|
|
if (useSoftmax)
|
|
{
|
|
// Yolo v2
|
|
// softmax activation for Probability, for each grid cell (X x Y x Anchor-index)
|
|
ocl::Kernel softmax_kernel("softmax_activ", ocl::dnn::region_oclsrc);
|
|
size_t nanchors = rows*cols*anchors*batch_size;
|
|
softmax_kernel.set(0, (int)nanchors);
|
|
softmax_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
|
|
softmax_kernel.set(2, ocl::KernelArg::PtrReadOnly(blob_umat));
|
|
softmax_kernel.set(3, (int)cell_size);
|
|
softmax_kernel.set(4, (int)classes);
|
|
softmax_kernel.set(5, (int)classfix);
|
|
softmax_kernel.set(6, (int)rows);
|
|
softmax_kernel.set(7, (int)cols);
|
|
softmax_kernel.set(8, (int)anchors);
|
|
softmax_kernel.set(9, (float)thresh);
|
|
softmax_kernel.set(10, ocl::KernelArg::PtrWriteOnly(outBlob));
|
|
if (!softmax_kernel.run(1, &nanchors, NULL, false))
|
|
return false;
|
|
}
|
|
|
|
if (nmsThreshold > 0) {
|
|
Mat mat = outBlob.getMat(ACCESS_WRITE);
|
|
float *dstData = mat.ptr<float>();
|
|
for (int b = 0; b < batch_size; ++b)
|
|
do_nms_sort(dstData + b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
|
|
}
|
|
|
|
}
|
|
|
|
return true;
|
|
}
|
|
#endif
|
|
|
|
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
|
|
{
|
|
CV_TRACE_FUNCTION();
|
|
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
|
|
|
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
|
|
forward_ocl(inputs_arr, outputs_arr, internals_arr))
|
|
|
|
if (inputs_arr.depth() == CV_16S)
|
|
{
|
|
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> inputs, outputs, internals;
|
|
inputs_arr.getMatVector(inputs);
|
|
outputs_arr.getMatVector(outputs);
|
|
internals_arr.getMatVector(internals);
|
|
|
|
CV_Assert(inputs.size() >= 1);
|
|
CV_Assert(outputs.size() == 1);
|
|
int const cell_size = classes + coords + 1;
|
|
|
|
const float* biasData = blobs[0].ptr<float>();
|
|
|
|
for (size_t ii = 0; ii < outputs.size(); ii++)
|
|
{
|
|
Mat &inpBlob = inputs[ii];
|
|
Mat &outBlob = outputs[ii];
|
|
|
|
int batch_size = inpBlob.size[0];
|
|
int rows = inpBlob.size[1];
|
|
int cols = inpBlob.size[2];
|
|
|
|
// address length for one image in batch, both for input and output
|
|
int sample_size = cell_size*rows*cols*anchors;
|
|
|
|
// assert that the comment above is true
|
|
CV_Assert(sample_size*batch_size == inpBlob.total());
|
|
CV_Assert(sample_size*batch_size == outBlob.total());
|
|
|
|
CV_Assert(inputs.size() < 2 || inputs[1].dims == 4);
|
|
int hNorm = inputs.size() > 1 ? inputs[1].size[2] : rows;
|
|
int wNorm = inputs.size() > 1 ? inputs[1].size[3] : cols;
|
|
|
|
const float *srcData = inpBlob.ptr<float>();
|
|
float *dstData = outBlob.ptr<float>();
|
|
|
|
// logistic activation for t0, for each grid cell (X x Y x Anchor-index)
|
|
for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
|
|
int index = cell_size*i;
|
|
float x = srcData[index + 4];
|
|
dstData[index + 4] = logistic_activate(x); // logistic activation
|
|
}
|
|
|
|
if (useSoftmax) { // Yolo v2
|
|
for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
|
|
int index = cell_size*i;
|
|
softmax_activate(srcData + index + 5, classes, 1, dstData + index + 5);
|
|
}
|
|
}
|
|
else if (useLogistic) { // Yolo v3
|
|
for (int i = 0; i < batch_size*rows*cols*anchors; ++i){
|
|
int index = cell_size*i;
|
|
const float* input = srcData + index + 5;
|
|
float* output = dstData + index + 5;
|
|
for (int c = 0; c < classes; ++c)
|
|
output[c] = logistic_activate(input[c]);
|
|
}
|
|
}
|
|
for (int b = 0; b < batch_size; ++b)
|
|
for (int x = 0; x < cols; ++x)
|
|
for(int y = 0; y < rows; ++y)
|
|
for (int a = 0; a < anchors; ++a) {
|
|
// relative start address for image b within the batch data
|
|
int index_sample_offset = sample_size*b;
|
|
int index = (y*cols + x)*anchors + a; // index for each grid-cell & anchor
|
|
int p_index = index_sample_offset + index * cell_size + 4;
|
|
float scale = dstData[p_index];
|
|
if (classfix == -1 && scale < .5) scale = 0; // if(t0 < 0.5) t0 = 0;
|
|
int box_index = index_sample_offset + index * cell_size;
|
|
|
|
float x_tmp = (logistic_activate(srcData[box_index + 0]) - 0.5f) * scale_x_y + 0.5f;
|
|
float y_tmp = (logistic_activate(srcData[box_index + 1]) - 0.5f) * scale_x_y + 0.5f;
|
|
dstData[box_index + 0] = (x + x_tmp) / cols;
|
|
dstData[box_index + 1] = (y + y_tmp) / rows;
|
|
dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / wNorm;
|
|
dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / hNorm;
|
|
|
|
int class_index = index_sample_offset + index * cell_size + 5;
|
|
for (int j = 0; j < classes; ++j) {
|
|
float prob = scale*dstData[class_index + j]; // prob = IoU(box, object) = t0 * class-probability
|
|
dstData[class_index + j] = (prob > thresh) ? prob : 0; // if (IoU < threshold) IoU = 0;
|
|
}
|
|
}
|
|
if (nmsThreshold > 0) {
|
|
for (int b = 0; b < batch_size; ++b){
|
|
do_nms_sort(dstData+b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void do_nms_sort(float *detections, int total, float score_thresh, float nms_thresh)
|
|
{
|
|
std::vector<Rect2d> boxes(total);
|
|
std::vector<float> scores(total);
|
|
|
|
for (int i = 0; i < total; ++i)
|
|
{
|
|
Rect2d &b = boxes[i];
|
|
int box_index = i * (classes + coords + 1);
|
|
b.width = detections[box_index + 2];
|
|
b.height = detections[box_index + 3];
|
|
b.x = detections[box_index + 0] - b.width / 2;
|
|
b.y = detections[box_index + 1] - b.height / 2;
|
|
}
|
|
|
|
std::vector<int> indices;
|
|
for (int k = 0; k < classes; ++k)
|
|
{
|
|
for (int i = 0; i < total; ++i)
|
|
{
|
|
int box_index = i * (classes + coords + 1);
|
|
int class_index = box_index + 5;
|
|
scores[i] = detections[class_index + k];
|
|
detections[class_index + k] = 0;
|
|
}
|
|
NMSBoxes(boxes, scores, score_thresh, nms_thresh, indices);
|
|
for (int i = 0, n = indices.size(); i < n; ++i)
|
|
{
|
|
int box_index = indices[i] * (classes + coords + 1);
|
|
int class_index = box_index + 5;
|
|
detections[class_index + k] = scores[indices[i]];
|
|
}
|
|
}
|
|
}
|
|
|
|
#ifdef HAVE_CUDA
|
|
Ptr<BackendNode> initCUDA(
|
|
void *context_,
|
|
const std::vector<Ptr<BackendWrapper>>& inputs,
|
|
const std::vector<Ptr<BackendWrapper>>& outputs
|
|
) override
|
|
{
|
|
auto context = reinterpret_cast<csl::CSLContext*>(context_);
|
|
|
|
if (coords != 4)
|
|
CV_Error(Error::StsNotImplemented, "Only upright rectangular boxes are supported in RegionLayer.");
|
|
|
|
std::size_t height_norm, width_norm;
|
|
if (inputs.size() == 1)
|
|
{
|
|
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
|
|
auto input_shape = input_wrapper->getShape();
|
|
height_norm = input_shape[1];
|
|
width_norm = input_shape[2];
|
|
}
|
|
else
|
|
{
|
|
auto input_wrapper = inputs[1].dynamicCast<CUDABackendWrapper>();
|
|
auto input_shape = input_wrapper->getShape();
|
|
CV_Assert(input_shape.size() == 4);
|
|
height_norm = input_shape[2];
|
|
width_norm = input_shape[3];
|
|
}
|
|
|
|
cuda4dnn::SquashMethod squash_method;
|
|
if(useLogistic)
|
|
squash_method = cuda4dnn::SquashMethod::SIGMOID;
|
|
else if (useSoftmax)
|
|
squash_method = cuda4dnn::SquashMethod::SOFTMAX;
|
|
|
|
/* exactly one must be true */
|
|
CV_Assert((useLogistic || useSoftmax) && !(useLogistic && useSoftmax));
|
|
|
|
cuda4dnn::RegionConfiguration<float> config;
|
|
config.squash_method = squash_method;
|
|
config.classes = classes;
|
|
config.boxes_per_cell = anchors;
|
|
|
|
config.height_norm = height_norm;
|
|
config.width_norm = width_norm;
|
|
|
|
config.scale_x_y = scale_x_y;
|
|
|
|
config.object_prob_cutoff = (classfix == -1) ? 0.5 : 0.0;
|
|
config.class_prob_cutoff = thresh;
|
|
|
|
config.nms_iou_threshold = nmsThreshold;
|
|
|
|
return make_cuda_node<cuda4dnn::RegionOp>(preferableTarget, std::move(context->stream), blobs[0], config);
|
|
}
|
|
#endif
|
|
|
|
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
|
|
const std::vector<MatShape> &outputs) const CV_OVERRIDE
|
|
{
|
|
CV_UNUSED(outputs); // suppress unused variable warning
|
|
|
|
int64 flops = 0;
|
|
for(int i = 0; i < inputs.size(); i++)
|
|
{
|
|
flops += 60*total(inputs[i]);
|
|
}
|
|
return flops;
|
|
}
|
|
|
|
#ifdef HAVE_DNN_NGRAPH
|
|
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
|
|
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
|
|
{
|
|
auto& input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
|
|
auto parent_shape = input->get_shape();
|
|
int64_t b = parent_shape[0];
|
|
int64_t h = parent_shape[1];
|
|
int64_t w = parent_shape[2];
|
|
int64_t c = parent_shape[3];
|
|
|
|
int64_t cols = b * h * w * anchors;
|
|
int64_t rows = c / anchors;
|
|
auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{cols, rows});
|
|
auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 0});
|
|
|
|
std::shared_ptr<ngraph::Node> input2d;
|
|
{
|
|
input2d = std::make_shared<ngraph::op::v1::Reshape>(input, shape_node, true);
|
|
input2d = std::make_shared<ngraph::op::Transpose>(input2d, tr_axes);
|
|
}
|
|
|
|
std::shared_ptr<ngraph::Node> region;
|
|
{
|
|
auto new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 3, 1, 2});
|
|
auto tr_input = std::make_shared<ngraph::op::Transpose>(input, new_axes);
|
|
|
|
std::vector<float> anchors_vec(blobs[0].ptr<float>(), blobs[0].ptr<float>() + blobs[0].total());
|
|
std::vector<int64_t> mask(anchors, 1);
|
|
region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
|
|
|
|
auto shape_as_inp = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
|
|
ngraph::Shape{tr_input->get_shape().size()}, tr_input->get_shape().data());
|
|
|
|
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
|
|
new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
|
|
region = std::make_shared<ngraph::op::Transpose>(region, new_axes);
|
|
|
|
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_node, true);
|
|
region = std::make_shared<ngraph::op::Transpose>(region, tr_axes);
|
|
}
|
|
|
|
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 1});
|
|
std::vector<int64_t> boxes_shape{b, anchors, h, w};
|
|
auto shape_3d = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{boxes_shape.size()}, boxes_shape.data());
|
|
|
|
ngraph::Shape box_broad_shape{1, (size_t)anchors, (size_t)h, (size_t)w};
|
|
auto scale_x_y_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &scale_x_y);
|
|
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{0.5});
|
|
|
|
auto axis = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{}, {0});
|
|
auto splits = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{5}, {1, 1, 1, 1, rows - 4});
|
|
auto split = std::make_shared<ngraph::op::v1::VariadicSplit>(input2d, axis, splits);
|
|
std::shared_ptr<ngraph::Node> box_x;
|
|
{
|
|
box_x = std::make_shared<ngraph::op::Sigmoid>(split->output(0));
|
|
box_x = std::make_shared<ngraph::op::v1::Subtract>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_x = std::make_shared<ngraph::op::v1::Multiply>(box_x, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_x = std::make_shared<ngraph::op::v1::Add>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, shape_3d, true);
|
|
|
|
std::vector<float> x_indices(w * h * anchors);
|
|
auto begin = x_indices.begin();
|
|
for (int i = 0; i < h; i++)
|
|
{
|
|
std::fill(begin + i * anchors, begin + (i + 1) * anchors, i);
|
|
}
|
|
|
|
for (int j = 1; j < w; j++)
|
|
{
|
|
std::copy(begin, begin + h * anchors, begin + j * h * anchors);
|
|
}
|
|
auto horiz = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, x_indices.data());
|
|
box_x = std::make_shared<ngraph::op::v1::Add>(box_x, horiz, ngraph::op::AutoBroadcastType::NUMPY);
|
|
|
|
auto cols_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(w)});
|
|
box_x = std::make_shared<ngraph::op::v1::Divide>(box_x, cols_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
}
|
|
|
|
std::shared_ptr<ngraph::Node> box_y;
|
|
{
|
|
box_y = std::make_shared<ngraph::op::Sigmoid>(split->output(1));
|
|
box_y = std::make_shared<ngraph::op::v1::Subtract>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_y = std::make_shared<ngraph::op::v1::Multiply>(box_y, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_y = std::make_shared<ngraph::op::v1::Add>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, shape_3d, true);
|
|
|
|
std::vector<float> y_indices(h * anchors);
|
|
for (int i = 0; i < h; i++)
|
|
{
|
|
std::fill(y_indices.begin() + i * anchors, y_indices.begin() + (i + 1) * anchors, i);
|
|
}
|
|
|
|
auto vert = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data());
|
|
box_y = std::make_shared<ngraph::op::v1::Add>(box_y, vert, ngraph::op::AutoBroadcastType::NUMPY);
|
|
auto rows_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(h)});
|
|
box_y = std::make_shared<ngraph::op::v1::Divide>(box_y, rows_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
}
|
|
|
|
std::shared_ptr<ngraph::Node> box_w, box_h;
|
|
{
|
|
int hNorm, wNorm;
|
|
if (nodes.size() > 1)
|
|
{
|
|
auto node_1_shape = nodes[1].dynamicCast<InfEngineNgraphNode>()->node->get_shape();
|
|
hNorm = node_1_shape[2];
|
|
wNorm = node_1_shape[3];
|
|
}
|
|
else
|
|
{
|
|
hNorm = h;
|
|
wNorm = w;
|
|
}
|
|
|
|
std::vector<float> anchors_w(anchors), anchors_h(anchors);
|
|
for (size_t a = 0; a < anchors; ++a)
|
|
{
|
|
anchors_w[a] = blobs[0].at<float>(0, 2 * a) / wNorm;
|
|
anchors_h[a] = blobs[0].at<float>(0, 2 * a + 1) / hNorm;
|
|
}
|
|
|
|
std::vector<float> bias_w(w * h * anchors), bias_h(w * h * anchors);
|
|
for (int j = 0; j < h; j++)
|
|
{
|
|
std::copy(anchors_w.begin(), anchors_w.end(), bias_w.begin() + j * anchors);
|
|
std::copy(anchors_h.begin(), anchors_h.end(), bias_h.begin() + j * anchors);
|
|
}
|
|
|
|
for (int i = 1; i < w; i++)
|
|
{
|
|
std::copy(bias_w.begin(), bias_w.begin() + h * anchors, bias_w.begin() + i * h * anchors);
|
|
std::copy(bias_h.begin(), bias_h.begin() + h * anchors, bias_h.begin() + i * h * anchors);
|
|
}
|
|
|
|
box_w = std::make_shared<ngraph::op::v0::Exp>(split->output(2));
|
|
box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, shape_3d, true);
|
|
auto anchor_w_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_w.data());
|
|
box_w = std::make_shared<ngraph::op::v1::Multiply>(box_w, anchor_w_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
|
|
box_h = std::make_shared<ngraph::op::v0::Exp>(split->output(3));
|
|
box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, shape_3d, true);
|
|
auto anchor_h_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_h.data());
|
|
box_h = std::make_shared<ngraph::op::v1::Multiply>(box_h, anchor_h_node, ngraph::op::AutoBroadcastType::NUMPY);
|
|
}
|
|
|
|
auto region_splits = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{3}, {4, 1, rows - 5});
|
|
auto region_split = std::make_shared<ngraph::op::v1::VariadicSplit>(region, axis, region_splits);
|
|
|
|
std::shared_ptr<ngraph::Node> scale;
|
|
{
|
|
float thr = classfix == -1 ? 0.5 : 0;
|
|
auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{thr});
|
|
auto mask = std::make_shared<ngraph::op::v1::Less>(region_split->output(1), thresh_node);
|
|
auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>(cols, 0));
|
|
scale = std::make_shared<ngraph::op::v1::Select>(mask, zero_node, region_split->output(1));
|
|
}
|
|
|
|
std::shared_ptr<ngraph::Node> probs;
|
|
{
|
|
probs = std::make_shared<ngraph::op::v1::Multiply>(region_split->output(2), scale, ngraph::op::AutoBroadcastType::NUMPY);
|
|
auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &thresh);
|
|
auto mask = std::make_shared<ngraph::op::v1::Greater>(probs, thresh_node);
|
|
auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>((rows - 5) * cols, 0));
|
|
probs = std::make_shared<ngraph::op::v1::Select>(mask, probs, zero_node);
|
|
}
|
|
|
|
|
|
auto concat_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, cols});
|
|
box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, concat_shape, true);
|
|
box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, concat_shape, true);
|
|
box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, concat_shape, true);
|
|
box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, concat_shape, true);
|
|
|
|
ngraph::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs};
|
|
std::shared_ptr<ngraph::Node> result = std::make_shared<ngraph::op::Concat>(inp_nodes, 0);
|
|
result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
|
|
if (b > 1)
|
|
{
|
|
std::vector<size_t> sizes = {(size_t)b, result->get_shape()[0] / b, result->get_shape()[1]};
|
|
auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
|
|
result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
|
|
}
|
|
|
|
return Ptr<BackendNode>(new InfEngineNgraphNode(result));
|
|
}
|
|
#endif // HAVE_DNN_NGRAPH
|
|
|
|
};
|
|
|
|
Ptr<RegionLayer> RegionLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<RegionLayer>(new RegionLayerImpl(params));
|
|
}
|
|
|
|
} // namespace dnn
|
|
} // namespace cv
|