mirror of https://github.com/opencv/opencv.git
Merge pull request #18783 from sl-sergei:fix_conv1d
Add support for Conv1D on OpenCV backend * Add support for Conv1D on OpenCV backend * disable tests on other targets/backends * Fix formatting * Restore comment * Remove unnecessary flag and fix test logic * Fix perf test * fix braces * Fix indentation, assert check and remove unnecessary condition * Remove unnecessary changes * Add test cases for variable weights and bias * dnn(conv): fallback on OpenCV+CPU instead of failures * coding stylepull/18800/head^2
Sergei Slashchinin
4 years ago
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GitHub
parent
d23435baac
commit
61144f935e
7 changed files with 404 additions and 70 deletions
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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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#include "perf_precomp.hpp" |
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#include <opencv2/dnn/shape_utils.hpp> |
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namespace opencv_test { |
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struct Conv1DParam_t { |
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int kernel; |
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struct BlobShape { int dims[3]; } shapeIn; |
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int outCN; |
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int groups; |
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int stride; |
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int dilation; |
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int pad[2]; |
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const char* padMode; |
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bool hasBias; |
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double declared_flops; |
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}; |
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// Details: #12142
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static const Conv1DParam_t testConvolution1DConfigs[] = { |
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{3, {{1, 6, 10}}, 6, 1, 1, 1, {0, 0}, "VALID", true, 1776.}, |
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{3, {{1, 2, 19}}, 2, 2, 2, 1, {1, 1}, "", true, 260.}, |
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{3, {{1, 2, 25}}, 2, 2, 1, 1, {2, 2}, "SAME", false, 650.}, |
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}; |
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struct Conv1DParamID |
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{ |
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enum { |
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CONV_0 = 0, |
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CONV_LAST = sizeof(testConvolution1DConfigs) / sizeof(testConvolution1DConfigs[0]) |
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}; |
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int val_; |
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Conv1DParamID(int val = 0) : val_(val) {} |
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operator int() const { return val_; } |
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static ::testing::internal::ParamGenerator<Conv1DParamID> all() |
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{ |
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enum { NUM = (int)CONV_LAST }; |
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Conv1DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv1DParamID(i); } // reduce generated code size
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return ::testing::ValuesIn(v_, v_ + NUM); |
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} |
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}; |
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static inline void PrintTo(const Conv1DParamID& v, std::ostream* os) |
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{ |
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CV_Assert((int)v >= 0); CV_Assert((int)v < Conv1DParamID::CONV_LAST); |
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const Conv1DParam_t& p = testConvolution1DConfigs[(int)v]; |
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*os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9) |
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<< ", K=[" << p.kernel << "]" |
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<< ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << "}" |
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<< ", OCN=" << p.outCN; |
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if (p.groups > 1) |
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*os << ", G=" << p.groups; |
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if (p.stride != 1) |
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*os << ", S=" << p.stride; |
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if (p.dilation != 1) |
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*os << ", D=" << p.dilation; |
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if (p.pad[0] != 0 && p.pad[1] != 0 ) |
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*os << ", P=(" << p.pad[0] << ", " << p.pad[1] << ")"; |
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if (!((std::string)p.padMode).empty()) |
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*os << ", PM=" << ((std::string)p.padMode); |
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if (p.hasBias) |
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*os << ", BIAS"; |
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} |
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typedef tuple<Conv1DParamID, tuple<Backend, Target> > Conv1DTestParam_t; |
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typedef TestBaseWithParam<Conv1DTestParam_t> Conv1D; |
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PERF_TEST_P_(Conv1D, conv1d) |
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{ |
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int test_id = (int)get<0>(GetParam()); |
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ASSERT_GE(test_id, 0); ASSERT_LT(test_id, Conv1DParamID::CONV_LAST); |
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const Conv1DParam_t& params = testConvolution1DConfigs[test_id]; |
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double declared_flops = params.declared_flops; |
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DictValue kernel = DictValue::arrayInt(¶ms.kernel, 1); |
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DictValue stride = DictValue::arrayInt(¶ms.stride, 1); |
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DictValue pad = DictValue::arrayInt(¶ms.pad[0], 2); |
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DictValue dilation = DictValue::arrayInt(¶ms.dilation, 1); |
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MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 3); |
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int outChannels = params.outCN; |
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int groups = params.groups; |
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std::string padMode(params.padMode); |
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bool hasBias = params.hasBias; |
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Backend backendId = get<0>(get<1>(GetParam())); |
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Target targetId = get<1>(get<1>(GetParam())); |
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if (targetId != DNN_TARGET_CPU) |
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throw SkipTestException("Only CPU is supported"); |
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int inChannels = inputShape[1]; |
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int sz[] = {outChannels, inChannels / groups, params.kernel}; |
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Mat weights(3, &sz[0], CV_32F); |
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randu(weights, -1.0f, 1.0f); |
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LayerParams lp; |
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lp.set("kernel_size", kernel); |
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lp.set("pad", pad); |
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if (!padMode.empty()) |
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lp.set("pad_mode", padMode); |
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lp.set("stride", stride); |
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lp.set("dilation", dilation); |
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lp.set("num_output", outChannels); |
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lp.set("group", groups); |
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lp.set("bias_term", hasBias); |
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lp.type = "Convolution"; |
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lp.name = "testLayer"; |
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lp.blobs.push_back(weights); |
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if (hasBias) |
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{ |
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Mat bias(1, outChannels, CV_32F); |
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randu(bias, -1.0f, 1.0f); |
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lp.blobs.push_back(bias); |
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} |
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int inpSz[] = {1, inChannels, inputShape[2]}; |
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Mat input(3, &inpSz[0], CV_32F); |
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randu(input, -1.0f, 1.0f); |
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Net net; |
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net.addLayerToPrev(lp.name, lp.type, lp); |
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net.setInput(input); |
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net.setPreferableBackend(backendId); |
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net.setPreferableTarget(targetId); |
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// warmup
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Mat output = net.forward(); |
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MatShape netInputShape = shape(input); |
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size_t weightsMemory = 0, blobsMemory = 0; |
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net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory); |
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int64 flops = net.getFLOPS(netInputShape); |
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CV_Assert(flops > 0); |
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std::cout |
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<< "IN=" << divUp(input.total() * input.elemSize(), 1u<<10) << " Kb " << netInputShape |
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<< " OUT=" << divUp(output.total() * output.elemSize(), 1u<<10) << " Kb " << shape(output) |
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<< " Weights(parameters): " << divUp(weightsMemory, 1u<<10) << " Kb" |
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<< " MFLOPS=" << flops * 1e-6 << std::endl; |
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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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EXPECT_NEAR(flops, declared_flops, declared_flops * 1e-6); |
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SANITY_CHECK_NOTHING(); |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, Conv1D, Combine( |
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Conv1DParamID::all(), |
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dnnBackendsAndTargets(false, false) // defined in ../test/test_common.hpp
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)); |
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} // namespace
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