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/*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 <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include "../nms.inl.hpp"
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
namespace cv
{
namespace dnn
{
class RegionLayerImpl CV_FINAL : public RegionLayer
{
public:
int coords, classes, anchors, classfix;
float thresh, nmsThreshold;
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);
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;
}
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;
dstData[box_index + 0] = (x + logistic_activate(srcData[box_index + 0])) / cols;
dstData[box_index + 1] = (y + logistic_activate(srcData[box_index + 1])) / rows;
dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / hNorm;
dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / wNorm;
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]];
}
}
}
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;
}
};
Ptr<RegionLayer> RegionLayer::create(const LayerParams& params)
{
return Ptr<RegionLayer>(new RegionLayerImpl(params));
}
} // namespace dnn
} // namespace cv