356 lines
14 KiB
C++
356 lines
14 KiB
C++
/*M ///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "../precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include <opencv2/dnn/all_layers.hpp>
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#include "../nms.inl.hpp"
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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#endif
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namespace cv
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{
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namespace dnn
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{
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class RegionLayerImpl CV_FINAL : public RegionLayer
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{
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public:
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int coords, classes, anchors, classfix;
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float thresh, nmsThreshold;
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bool useSoftmax, useLogistic;
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#ifdef HAVE_OPENCL
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UMat blob_umat;
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#endif
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RegionLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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CV_Assert(blobs.size() == 1);
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thresh = params.get<float>("thresh", 0.2);
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coords = params.get<int>("coords", 4);
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classes = params.get<int>("classes", 0);
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anchors = params.get<int>("anchors", 5);
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classfix = params.get<int>("classfix", 0);
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useSoftmax = params.get<bool>("softmax", false);
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useLogistic = params.get<bool>("logistic", false);
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nmsThreshold = params.get<float>("nms_threshold", 0.4);
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CV_Assert(nmsThreshold >= 0.);
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CV_Assert(coords == 4);
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CV_Assert(classes >= 1);
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CV_Assert(anchors >= 1);
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CV_Assert(useLogistic || useSoftmax);
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if (params.get<bool>("softmax_tree", false))
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CV_Error(cv::Error::StsNotImplemented, "Yolo9000 is not implemented");
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}
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bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const CV_OVERRIDE
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{
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CV_Assert(inputs.size() > 0);
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// channels == cell_size*anchors
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CV_Assert(inputs[0][3] == (1 + coords + classes)*anchors);
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int batch_size = inputs[0][0];
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if(batch_size > 1)
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outputs = std::vector<MatShape>(1, shape(batch_size, inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
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else
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outputs = std::vector<MatShape>(1, shape(inputs[0][1] * inputs[0][2] * anchors, inputs[0][3] / anchors));
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return false;
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}
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float logistic_activate(float x) { return 1.F / (1.F + exp(-x)); }
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void softmax_activate(const float* input, const int n, const float temp, float* output)
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{
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int i;
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float sum = 0;
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float largest = -FLT_MAX;
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for (i = 0; i < n; ++i) {
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if (input[i] > largest) largest = input[i];
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}
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for (i = 0; i < n; ++i) {
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float e = exp((input[i] - largest) / temp);
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sum += e;
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output[i] = e;
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}
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for (i = 0; i < n; ++i) {
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output[i] /= sum;
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}
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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if (blob_umat.empty())
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blobs[0].copyTo(blob_umat);
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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// TODO: implement a logistic activation to classification scores.
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if (useLogistic || inps.depth() == CV_16S)
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return false;
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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CV_Assert(inputs.size() >= 1);
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int const cell_size = classes + coords + 1;
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for (size_t ii = 0; ii < outputs.size(); ii++)
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{
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UMat& inpBlob = inputs[ii];
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UMat& outBlob = outputs[ii];
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int batch_size = inpBlob.size[0];
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int rows = inpBlob.size[1];
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int cols = inpBlob.size[2];
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// channels == cell_size*anchors, see l. 94
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int sample_size = cell_size*rows*cols*anchors;
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ocl::Kernel logistic_kernel("logistic_activ", ocl::dnn::region_oclsrc);
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size_t nanchors = rows*cols*anchors*batch_size;
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logistic_kernel.set(0, (int)nanchors);
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logistic_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
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logistic_kernel.set(2, (int)cell_size);
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logistic_kernel.set(3, ocl::KernelArg::PtrWriteOnly(outBlob));
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logistic_kernel.run(1, &nanchors, NULL, false);
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if (useSoftmax)
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{
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// Yolo v2
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// softmax activation for Probability, for each grid cell (X x Y x Anchor-index)
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ocl::Kernel softmax_kernel("softmax_activ", ocl::dnn::region_oclsrc);
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size_t nanchors = rows*cols*anchors*batch_size;
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softmax_kernel.set(0, (int)nanchors);
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softmax_kernel.set(1, ocl::KernelArg::PtrReadOnly(inpBlob));
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softmax_kernel.set(2, ocl::KernelArg::PtrReadOnly(blob_umat));
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softmax_kernel.set(3, (int)cell_size);
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softmax_kernel.set(4, (int)classes);
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softmax_kernel.set(5, (int)classfix);
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softmax_kernel.set(6, (int)rows);
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softmax_kernel.set(7, (int)cols);
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softmax_kernel.set(8, (int)anchors);
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softmax_kernel.set(9, (float)thresh);
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softmax_kernel.set(10, ocl::KernelArg::PtrWriteOnly(outBlob));
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if (!softmax_kernel.run(1, &nanchors, NULL, false))
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return false;
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}
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if (nmsThreshold > 0) {
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Mat mat = outBlob.getMat(ACCESS_WRITE);
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float *dstData = mat.ptr<float>();
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for (int b = 0; b < batch_size; ++b)
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do_nms_sort(dstData + b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
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}
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}
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return true;
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}
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#endif
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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if (inputs_arr.depth() == CV_16S)
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{
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forward_fallback(inputs_arr, outputs_arr, internals_arr);
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return;
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}
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std::vector<Mat> inputs, outputs, internals;
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inputs_arr.getMatVector(inputs);
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outputs_arr.getMatVector(outputs);
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internals_arr.getMatVector(internals);
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CV_Assert(inputs.size() >= 1);
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CV_Assert(outputs.size() == 1);
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int const cell_size = classes + coords + 1;
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const float* biasData = blobs[0].ptr<float>();
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for (size_t ii = 0; ii < outputs.size(); ii++)
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{
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Mat &inpBlob = inputs[ii];
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Mat &outBlob = outputs[ii];
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int batch_size = inpBlob.size[0];
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int rows = inpBlob.size[1];
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int cols = inpBlob.size[2];
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// address length for one image in batch, both for input and output
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int sample_size = cell_size*rows*cols*anchors;
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// assert that the comment above is true
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CV_Assert(sample_size*batch_size == inpBlob.total());
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CV_Assert(sample_size*batch_size == outBlob.total());
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CV_Assert(inputs.size() < 2 || inputs[1].dims == 4);
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int hNorm = inputs.size() > 1 ? inputs[1].size[2] : rows;
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int wNorm = inputs.size() > 1 ? inputs[1].size[3] : cols;
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const float *srcData = inpBlob.ptr<float>();
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float *dstData = outBlob.ptr<float>();
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// logistic activation for t0, for each grid cell (X x Y x Anchor-index)
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for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
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int index = cell_size*i;
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float x = srcData[index + 4];
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dstData[index + 4] = logistic_activate(x); // logistic activation
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}
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if (useSoftmax) { // Yolo v2
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for (int i = 0; i < batch_size*rows*cols*anchors; ++i) {
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int index = cell_size*i;
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softmax_activate(srcData + index + 5, classes, 1, dstData + index + 5);
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}
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}
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else if (useLogistic) { // Yolo v3
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for (int i = 0; i < batch_size*rows*cols*anchors; ++i){
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int index = cell_size*i;
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const float* input = srcData + index + 5;
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float* output = dstData + index + 5;
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for (int c = 0; c < classes; ++c)
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output[c] = logistic_activate(input[c]);
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}
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}
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for (int b = 0; b < batch_size; ++b)
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for (int x = 0; x < cols; ++x)
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for(int y = 0; y < rows; ++y)
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for (int a = 0; a < anchors; ++a) {
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// relative start address for image b within the batch data
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int index_sample_offset = sample_size*b;
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int index = (y*cols + x)*anchors + a; // index for each grid-cell & anchor
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int p_index = index_sample_offset + index * cell_size + 4;
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float scale = dstData[p_index];
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if (classfix == -1 && scale < .5) scale = 0; // if(t0 < 0.5) t0 = 0;
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int box_index = index_sample_offset + index * cell_size;
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dstData[box_index + 0] = (x + logistic_activate(srcData[box_index + 0])) / cols;
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dstData[box_index + 1] = (y + logistic_activate(srcData[box_index + 1])) / rows;
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dstData[box_index + 2] = exp(srcData[box_index + 2]) * biasData[2 * a] / wNorm;
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dstData[box_index + 3] = exp(srcData[box_index + 3]) * biasData[2 * a + 1] / hNorm;
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int class_index = index_sample_offset + index * cell_size + 5;
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for (int j = 0; j < classes; ++j) {
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float prob = scale*dstData[class_index + j]; // prob = IoU(box, object) = t0 * class-probability
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dstData[class_index + j] = (prob > thresh) ? prob : 0; // if (IoU < threshold) IoU = 0;
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}
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}
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if (nmsThreshold > 0) {
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for (int b = 0; b < batch_size; ++b){
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do_nms_sort(dstData+b*sample_size, rows*cols*anchors, thresh, nmsThreshold);
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}
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}
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}
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}
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void do_nms_sort(float *detections, int total, float score_thresh, float nms_thresh)
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{
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std::vector<Rect2d> boxes(total);
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std::vector<float> scores(total);
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for (int i = 0; i < total; ++i)
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{
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Rect2d &b = boxes[i];
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int box_index = i * (classes + coords + 1);
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b.width = detections[box_index + 2];
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b.height = detections[box_index + 3];
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b.x = detections[box_index + 0] - b.width / 2;
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b.y = detections[box_index + 1] - b.height / 2;
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}
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std::vector<int> indices;
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for (int k = 0; k < classes; ++k)
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{
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for (int i = 0; i < total; ++i)
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{
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int box_index = i * (classes + coords + 1);
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int class_index = box_index + 5;
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scores[i] = detections[class_index + k];
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detections[class_index + k] = 0;
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}
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NMSBoxes(boxes, scores, score_thresh, nms_thresh, indices);
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for (int i = 0, n = indices.size(); i < n; ++i)
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{
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int box_index = indices[i] * (classes + coords + 1);
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int class_index = box_index + 5;
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detections[class_index + k] = scores[indices[i]];
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}
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}
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}
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virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
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const std::vector<MatShape> &outputs) const CV_OVERRIDE
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{
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CV_UNUSED(outputs); // suppress unused variable warning
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int64 flops = 0;
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for(int i = 0; i < inputs.size(); i++)
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{
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flops += 60*total(inputs[i]);
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}
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return flops;
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}
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};
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Ptr<RegionLayer> RegionLayer::create(const LayerParams& params)
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{
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return Ptr<RegionLayer>(new RegionLayerImpl(params));
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}
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} // namespace dnn
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} // namespace cv
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