mirror of https://github.com/opencv/opencv.git
Merge pull request #14842 from l-bat:ocv_conv3d
* Support Conv3D on OCV backend * Add header * Add perf tests * Support pool3d * Enable Resnet34_kinetics on OCV backend * Add test * Fix conv * Optimize Conv2Dpull/15025/head
Lubov Batanina
5 years ago
committed by
Alexander Alekhin
parent
3c086fb2fe
commit
8bcd7e122a
5 changed files with 515 additions and 151 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 Conv3DParam_t { |
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int kernel[3]; |
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struct BlobShape { int dims[5]; } shapeIn; |
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int outCN; |
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int groups; |
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int stride[3]; |
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int dilation[3]; |
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int pad[6]; |
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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 Conv3DParam_t testConvolution3DConfigs[] = { |
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{{3, 3, 3}, {{1, 6, 10, 38, 50}}, 6, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 26956800.}, |
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{{3, 3, 3}, {{1, 2, 19, 19, 19}}, 2, 2, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 218000.}, |
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{{3, 3, 3}, {{1, 2, 25, 19, 19}}, 2, 2, {1, 2, 2}, {1, 1, 1}, {2, 2, 2, 2, 2, 2}, "SAME", false, 545000.}, |
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{{3, 3, 3}, {{1, 11, 9, 150, 200}}, 11, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "VALID", true, 1342562760.}, |
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{{3, 3, 3}, {{1, 10, 98, 10, 10}}, 10, 1, {1, 1, 1}, {1, 1, 1}, {1, 0, 1, 1, 0,1}, "SAME", false, 53018000.}, |
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{{5, 5, 5}, {{1, 6, 19, 19, 19}}, 6, 2, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 30395250.}, |
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{{5, 5, 5}, {{1, 4, 50, 19, 19}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 5893888.}, |
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{{5, 5, 5}, {{1, 3, 75, 75, 100}}, 3, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", true, 1267312500.}, |
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{{5, 5, 5}, {{1, 2, 21, 75, 100}}, 2, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 116103744.}, |
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{{5, 5, 5}, {{1, 4, 40, 75, 75}}, 4, 1, {2, 2, 2}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 93405312.}, |
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{{7, 7, 7}, {{1, 6, 15, 19, 19}}, 6, 1, {2, 1, 1}, {1, 1, 1}, {3, 3, 3, 3, 3, 3}, "SAME", true, 71339376.}, |
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{{7, 7, 7}, {{1, 2, 38, 38, 38}}, 2, 1, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", false, 44990464.}, |
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{{1, 1, 1}, {{1, 4, 9, 10, 10}}, 4, 1, {1, 1, 2}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "VALID", false, 16200.}, |
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{{3, 1, 4}, {{1, 14, 5, 10, 10}}, 14, 1, {1, 1, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "SAME", false, 2359000.}, |
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{{1, 1, 1}, {{1, 8, 1, 10, 10}}, 8, 8, {1, 1, 1}, {1, 1, 1}, {1, 1, 1, 1, 1, 1}, "", true, 58752.}, |
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{{3, 4, 2}, {{1, 4, 8, 10, 10}}, 4, 4, {1, 2, 1}, {1, 1, 1}, {0, 0, 0, 0, 0, 0}, "", true, 166752.} |
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}; |
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struct Conv3DParamID |
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{ |
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enum { |
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CONV_0 = 0, |
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CONV_100 = 16, |
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CONV_LAST = sizeof(testConvolution3DConfigs) / sizeof(testConvolution3DConfigs[0]) |
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}; |
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int val_; \
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Conv3DParamID(int val = 0) : val_(val) {} |
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operator int() const { return val_; } |
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static ::testing::internal::ParamGenerator<Conv3DParamID> all() |
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{ |
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#if 0 |
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enum { NUM = (int)CONV_LAST }; |
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#else |
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enum { NUM = (int)CONV_100 }; |
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#endif |
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Conv3DParamID v_[NUM]; for (int i = 0; i < NUM; ++i) { v_[i] = Conv3DParamID(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 Conv3DParamID& v, std::ostream* os) |
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{ |
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CV_Assert((int)v >= 0); CV_Assert((int)v < Conv3DParamID::CONV_LAST); |
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const Conv3DParam_t& p = testConvolution3DConfigs[(int)v]; |
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*os << "GFLOPS=" << cv::format("%.3f", p.declared_flops * 1e-9) |
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<< ", K=[" << p.kernel[0] << " x " << p.kernel[1] << " x " << p.kernel[2] << "]" |
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<< ", IN={" << p.shapeIn.dims[0] << ", " << p.shapeIn.dims[1] << ", " << p.shapeIn.dims[2] << ", " << p.shapeIn.dims[3] << ", " << p.shapeIn.dims[4] << "}" |
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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[0] * p.stride[1] * p.stride[2] != 1) |
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*os << ", S=[" << p.stride[0] << " x " << p.stride[1] << " x " << p.stride[2] << "]"; |
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if (p.dilation[0] * p.dilation[1] * p.dilation[2] != 1) |
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*os << ", D=[" << p.dilation[0] << " x " << p.dilation[1] << " x " << p.dilation[2] << "]"; |
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if (p.pad[0] != 0 && p.pad[1] != 0 && p.pad[2] != 0 && |
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p.pad[3] != 0 && p.pad[4] != 0 && p.pad[5] != 0) |
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*os << ", P=(" << p.pad[0] << ", " << p.pad[3] << ") x (" |
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<< p.pad[1] << ", " << p.pad[4] << ") x (" |
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<< p.pad[2] << ", " << p.pad[5] << ")"; |
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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<Conv3DParamID, tuple<Backend, Target> > Conv3DTestParam_t; |
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typedef TestBaseWithParam<Conv3DTestParam_t> Conv3D; |
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PERF_TEST_P_(Conv3D, conv3d) |
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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, Conv3DParamID::CONV_LAST); |
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const Conv3DParam_t& params = testConvolution3DConfigs[test_id]; |
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double declared_flops = params.declared_flops; |
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DictValue kernel = DictValue::arrayInt(¶ms.kernel[0], 3); |
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DictValue stride = DictValue::arrayInt(¶ms.stride[0], 3); |
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DictValue pad = DictValue::arrayInt(¶ms.pad[0], 6); |
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DictValue dilation = DictValue::arrayInt(¶ms.dilation[0], 3); |
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MatShape inputShape = MatShape(params.shapeIn.dims, params.shapeIn.dims + 5); |
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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[0], params.kernel[1], params.kernel[2]}; |
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Mat weights(5, &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], inputShape[3], inputShape[4]}; |
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Mat input(5, &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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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(/**/, Conv3D, Combine( |
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Conv3DParamID::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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