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https://github.com/opencv/opencv.git
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Hanbin Bae
371b802103
optimize(dnn): parallelize Resize layer implementation #28442 ### optimize(dnn): parallelize Resize layer implementation This PR addresses the `TODO` in `modules/dnn/src/layers/resize_layer.cpp` regarding the slow implementation of the `Resize` layer when using `opencv_linear` or `nearest` interpolation with specific configurations. ### Changes - Replaced the serial nested loop (batch x channels) with `cv::parallel_for_`. - This allows OpenCV to utilize multi-threading for the resizing operation, which was previously single-threaded for these specific paths. ### Performance Results Benchmark run on 20-thread CPU (AVX2): | Test Case | Resolution Change | Before (ms) | After (ms) | Speedup | | :--- | :--- | :--- | :--- | :--- | | **Upsample Linear** | 64x64 -> 128x128 | 1.47 ms | **0.18 ms** | **~8.1x** | | **Downsample Nearest** | 128x128 -> 64x64 | 1.55 ms | **0.45 ms** | **~3.4x** | **Test Configuration:** - **Upsample**: `[4, 64, 64, 64]` -> `[4, 64, 128, 128]` (Factor: 2.0, Linear) - **Downsample**: `[4, 128, 128, 128]` -> `[4, 128, 64, 64]` (Factor: 0.5, Nearest) ### 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 - [x] 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 - Added `modules/dnn/perf/perf_resize.cpp` to verify performance gains. - [ ] The feature is well documented and sample code can be built with the project CMake
66 lines
1.7 KiB
C++
66 lines
1.7 KiB
C++
// 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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#include "perf_precomp.hpp"
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namespace opencv_test {
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struct Layer_Resize : public TestBaseWithParam<tuple<Backend, Target>>
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{
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void test_layer(const std::vector<int>& inpShape, int outH, int outW, const String& interp)
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{
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int backendId = get<0>(GetParam());
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int targetId = get<1>(GetParam());
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Mat input(inpShape, CV_32FC1);
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randu(input, 0.f, 1.f);
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Net net;
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LayerParams lp;
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lp.type = "Resize";
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lp.name = "testLayer";
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lp.set("interpolation", interp);
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lp.set("width", outW);
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lp.set("height", outH);
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 0, id, 0);
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// warmup
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{
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net.setInputsNames({"data"});
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net.setInput(input, "data");
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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}
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TEST_CYCLE()
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{
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Mat res = net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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};
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PERF_TEST_P_(Layer_Resize, Resize_Upsample_Linear)
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{
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// N=4, C=64, H=64, W=64 -> 128x128 (x2 upsample)
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// Common in segmentation/detection heads
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test_layer({4, 64, 64, 64}, 128, 128, "opencv_linear");
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}
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PERF_TEST_P_(Layer_Resize, Resize_Downsample_Nearest)
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{
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// N=4, C=128, H=128, W=128 -> 64x64 (x0.5 downsample)
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test_layer({4, 128, 128, 128}, 64, 64, "nearest");
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}
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Resize, dnnBackendsAndTargets());
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} // namespace opencv_test
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