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Merge pull request #26127 from alexlyulkov:al/blob-from-images
Faster implementation of blobFromImages for cpu nchw output #26127 Faster implementation of blobFromImage and blobFromImages for HWC cv::Mat images -> NCHW cv::Mat case Running time on my pc in ms: **blobFromImage** ``` image size old new speed-up 32x32x3 0.008 0.002 4.0x 64x64x3 0.021 0.009 2.3x 128x128x3 0.164 0.037 4.4x 256x256x3 0.728 0.158 4.6x 512x512x3 3.310 0.628 5.2x 1024x1024x3 14.503 3.124 4.6x 2048x2048x3 61.647 28.049 2.2x ``` **blobFromImages** ``` image size old new speed-up 16x32x32x3 0.122 0.041 3.0x 16x64x64x3 0.790 0.165 4.8x 16x128x128x3 3.313 0.652 5.1x 16x256x256x3 13.495 3.127 4.3x 16x512x512x3 58.795 28.127 2.1x 16x1024x1024x3 251.135 121.955 2.1x 16x2048x2048x3 1023.570 487.188 2.1x ``` ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
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alexlyulkov
GitHub
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66
modules/dnn/perf/perf_utils.cpp
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66
modules/dnn/perf/perf_utils.cpp
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@ -0,0 +1,66 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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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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#include "perf_precomp.hpp"
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namespace opencv_test {
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using Utils_blobFromImage = TestBaseWithParam<std::vector<int>>;
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PERF_TEST_P_(Utils_blobFromImage, HWC_TO_NCHW) {
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std::vector<int> input_shape = GetParam();
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Mat input(input_shape, CV_32FC3);
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randu(input, -10.0f, 10.f);
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TEST_CYCLE() {
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Mat blob = blobFromImage(input);
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}
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SANITY_CHECK_NOTHING();
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}
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INSTANTIATE_TEST_CASE_P(/**/, Utils_blobFromImage,
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Values(std::vector<int>{ 32, 32},
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std::vector<int>{ 64, 64},
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std::vector<int>{ 128, 128},
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std::vector<int>{ 256, 256},
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std::vector<int>{ 512, 512},
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std::vector<int>{1024, 1024},
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std::vector<int>{2048, 2048})
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);
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using Utils_blobFromImages = TestBaseWithParam<std::vector<int>>;
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PERF_TEST_P_(Utils_blobFromImages, HWC_TO_NCHW) {
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std::vector<int> input_shape = GetParam();
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int batch = input_shape.front();
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std::vector<int> input_shape_no_batch(input_shape.begin()+1, input_shape.end());
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std::vector<Mat> inputs;
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for (int i = 0; i < batch; i++) {
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Mat input(input_shape_no_batch, CV_32FC3);
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randu(input, -10.0f, 10.f);
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inputs.push_back(input);
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}
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TEST_CYCLE() {
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Mat blobs = blobFromImages(inputs);
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}
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SANITY_CHECK_NOTHING();
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}
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INSTANTIATE_TEST_CASE_P(/**/, Utils_blobFromImages,
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Values(std::vector<int>{16, 32, 32},
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std::vector<int>{16, 64, 64},
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std::vector<int>{16, 128, 128},
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std::vector<int>{16, 256, 256},
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std::vector<int>{16, 512, 512},
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std::vector<int>{16, 1024, 1024},
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std::vector<int>{16, 2048, 2048})
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);
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}
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@ -126,6 +126,111 @@ Mat blobFromImagesWithParams(InputArrayOfArrays images, const Image2BlobParams&
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return blob;
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}
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template<typename Tinp, typename Tout>
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void blobFromImagesNCHWImpl(const std::vector<Mat>& images, Mat& blob_, const Image2BlobParams& param)
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{
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int w = images[0].cols;
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int h = images[0].rows;
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int wh = w * h;
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int nch = images[0].channels();
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CV_Assert(nch == 1 || nch == 3 || nch == 4);
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int sz[] = { (int)images.size(), nch, h, w};
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blob_.create(4, sz, param.ddepth);
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for (size_t k = 0; k < images.size(); ++k)
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{
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CV_Assert(images[k].depth() == images[0].depth());
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CV_Assert(images[k].channels() == images[0].channels());
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CV_Assert(images[k].size() == images[0].size());
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Tout* p_blob = blob_.ptr<Tout>() + k * nch * wh;
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Tout* p_blob_r = p_blob;
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Tout* p_blob_g = p_blob + wh;
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Tout* p_blob_b = p_blob + 2 * wh;
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Tout* p_blob_a = p_blob + 3 * wh;
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if (param.swapRB)
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std::swap(p_blob_r, p_blob_b);
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for (size_t i = 0; i < h; ++i)
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{
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const Tinp* p_img_row = images[k].ptr<Tinp>(i);
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if (nch == 1)
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{
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for (size_t j = 0; j < w; ++j)
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{
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p_blob[i * w + j] = p_img_row[j];
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}
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}
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else if (nch == 3)
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{
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for (size_t j = 0; j < w; ++j)
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{
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p_blob_r[i * w + j] = p_img_row[j * 3 ];
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p_blob_g[i * w + j] = p_img_row[j * 3 + 1];
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p_blob_b[i * w + j] = p_img_row[j * 3 + 2];
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}
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}
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else // if (nch == 4)
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{
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for (size_t j = 0; j < w; ++j)
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{
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p_blob_r[i * w + j] = p_img_row[j * 4 ];
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p_blob_g[i * w + j] = p_img_row[j * 4 + 1];
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p_blob_b[i * w + j] = p_img_row[j * 4 + 2];
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p_blob_a[i * w + j] = p_img_row[j * 4 + 3];
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}
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}
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}
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}
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if (param.mean == Scalar() && param.scalefactor == Scalar::all(1.0))
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return;
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CV_CheckTypeEQ(param.ddepth, CV_32F, "Scaling and mean substraction is supported only for CV_32F blob depth");
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for (size_t k = 0; k < images.size(); ++k)
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{
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for (size_t ch = 0; ch < nch; ++ch)
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{
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float cur_mean = param.mean[ch];
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float cur_scale = param.scalefactor[ch];
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Tout* p_blob = blob_.ptr<Tout>() + k * nch * wh + ch * wh;
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for (size_t i = 0; i < wh; ++i)
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{
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p_blob[i] = (p_blob[i] - cur_mean) * cur_scale;
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}
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}
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}
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}
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template<typename Tout>
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void blobFromImagesNCHW(const std::vector<Mat>& images, Mat& blob_, const Image2BlobParams& param)
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{
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if (images[0].depth() == CV_8U)
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blobFromImagesNCHWImpl<uint8_t, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_8S)
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blobFromImagesNCHWImpl<int8_t, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_16U)
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blobFromImagesNCHWImpl<uint16_t, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_16S)
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blobFromImagesNCHWImpl<int16_t, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_32S)
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blobFromImagesNCHWImpl<int32_t, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_32F)
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blobFromImagesNCHWImpl<float, Tout>(images, blob_, param);
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else if (images[0].depth() == CV_64F)
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blobFromImagesNCHWImpl<double, Tout>(images, blob_, param);
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else
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CV_Error(Error::BadDepth, "Unsupported input image depth for blobFromImagesNCHW");
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}
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template<typename Tout>
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void blobFromImagesNCHW(const std::vector<UMat>& images, UMat& blob_, const Image2BlobParams& param)
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{
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CV_Error(Error::StsNotImplemented, "");
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}
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template<class Tmat>
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void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const Image2BlobParams& param)
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{
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@ -154,19 +259,6 @@ void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const
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Scalar scalefactor = param.scalefactor;
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Scalar mean = param.mean;
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if (param.swapRB)
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{
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if (nch > 2)
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{
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std::swap(mean[0], mean[2]);
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std::swap(scalefactor[0], scalefactor[2]);
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}
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else
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{
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CV_LOG_WARNING(NULL, "Red/blue color swapping requires at least three image channels.");
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}
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}
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for (size_t i = 0; i < images.size(); i++)
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{
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Size imgSize = images[i].size();
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@ -203,18 +295,35 @@ void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const
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resize(images[i], images[i], size, 0, 0, INTER_LINEAR);
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}
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}
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if (images[i].depth() == CV_8U && param.ddepth == CV_32F)
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images[i].convertTo(images[i], CV_32F);
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subtract(images[i], mean, images[i]);
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multiply(images[i], scalefactor, images[i]);
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}
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size_t nimages = images.size();
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Tmat image0 = images[0];
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CV_Assert(image0.dims == 2);
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if (std::is_same<Tmat, Mat>::value && param.datalayout == DNN_LAYOUT_NCHW)
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{
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// Fast implementation for HWC cv::Mat images -> NCHW cv::Mat blob
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if (param.ddepth == CV_8U)
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blobFromImagesNCHW<uint8_t>(images, blob_, param);
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else
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blobFromImagesNCHW<float>(images, blob_, param);
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return;
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}
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if (param.swapRB)
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{
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if (nch > 2)
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{
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std::swap(mean[0], mean[2]);
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std::swap(scalefactor[0], scalefactor[2]);
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}
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else
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{
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CV_LOG_WARNING(NULL, "Red/blue color swapping requires at least three image channels.");
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}
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}
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if (param.datalayout == DNN_LAYOUT_NCHW)
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{
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if (nch == 3 || nch == 4)
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@ -225,7 +334,14 @@ void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const
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for (size_t i = 0; i < nimages; i++)
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{
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const Tmat& image = images[i];
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Tmat& image = images[i];
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if (image.depth() == CV_8U && param.ddepth == CV_32F)
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image.convertTo(image, CV_32F);
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if (mean != Scalar())
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subtract(image, mean, image);
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if (scalefactor != Scalar::all(1.0))
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multiply(image, scalefactor, image);
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CV_Assert(image.depth() == blob_.depth());
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nch = image.channels();
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CV_Assert(image.dims == 2 && (nch == 3 || nch == 4));
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@ -250,7 +366,14 @@ void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const
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for (size_t i = 0; i < nimages; i++)
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{
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const Tmat& image = images[i];
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Tmat& image = images[i];
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if (image.depth() == CV_8U && param.ddepth == CV_32F)
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image.convertTo(image, CV_32F);
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if (mean != Scalar())
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subtract(image, mean, image);
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if (scalefactor != Scalar::all(1.0))
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multiply(image, scalefactor, image);
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CV_Assert(image.depth() == blob_.depth());
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nch = image.channels();
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CV_Assert(image.dims == 2 && (nch == 1));
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@ -269,7 +392,14 @@ void blobFromImagesWithParamsImpl(InputArrayOfArrays images_, Tmat& blob_, const
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int subMatType = CV_MAKETYPE(param.ddepth, nch);
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for (size_t i = 0; i < nimages; i++)
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{
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const Tmat& image = images[i];
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Tmat& image = images[i];
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if (image.depth() == CV_8U && param.ddepth == CV_32F)
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image.convertTo(image, CV_32F);
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if (mean != Scalar())
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subtract(image, mean, image);
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if (scalefactor != Scalar::all(1.0))
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multiply(image, scalefactor, image);
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CV_Assert(image.depth() == blob_.depth());
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CV_Assert(image.channels() == image0.channels());
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CV_Assert(image.size() == image0.size());
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