mirror of
https://github.com/opencv/opencv.git
synced 2026-07-25 13:23:02 +04:00
Teddy-Yangjiale
2750 lines
92 KiB
C++
2750 lines
92 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) 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 "test_precomp.hpp"
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#include <opencv2/core/ocl.hpp>
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#include <opencv2/core/fast_math.hpp>
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#include "npy_blob.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 <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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#ifdef HAVE_INF_ENGINE
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#include <thread>
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#endif
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namespace opencv_test { namespace {
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template<typename TString>
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static String _tf(TString filename)
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{
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String basetestdir = getOpenCVExtraDir();
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size_t len = basetestdir.size();
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if(len > 0 && basetestdir[len-1] != '/' && basetestdir[len-1] != '\\')
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return (basetestdir + "/dnn/layers") + filename;
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return (basetestdir + "dnn/layers/") + filename;
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}
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void testReshape(const MatShape& inputShape, const MatShape& targetShape,
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int axis = 0, int num_axes = -1,
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MatShape mask = MatShape())
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{
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LayerParams params;
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params.set("axis", axis);
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params.set("num_axes", num_axes);
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if (!mask.empty())
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{
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params.set("dim", DictValue::arrayInt<int*>(&mask[0], mask.size()));
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}
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Mat inp(inputShape.size(), &inputShape[0], CV_32F);
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std::vector<Mat> inpVec(1, inp);
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std::vector<Mat> outVec, intVec;
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Ptr<Layer> rl = LayerFactory::createLayerInstance("Reshape", params);
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runLayer(rl, inpVec, outVec);
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Mat& out = outVec[0];
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MatShape shape = out.shape();
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EXPECT_EQ(shape, targetShape);
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}
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TEST(Layer_Test_Reshape, Accuracy)
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{
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{
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int inp[] = {4, 3, 1, 2};
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int out[] = {4, 3, 2};
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testReshape(MatShape(inp, inp + 4), MatShape(out, out + 3), 2, 1);
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}
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{
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int inp[] = {1, 128, 4, 4};
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int out[] = {1, 2048};
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int mask[] = {-1, 2048};
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testReshape(MatShape(inp, inp + 4), MatShape(out, out + 2), 0, -1,
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MatShape(mask, mask + 2));
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}
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{
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int inp[] = {1, 2, 3};
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int out[] = {3, 1, 2};
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int mask[] = {3, 1, 2};
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testReshape(MatShape(inp, inp + 3), MatShape(out, out + 3), 0, -1,
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MatShape(mask, mask + 3));
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}
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}
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class Layer_LSTM_Test : public ::testing::Test
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{
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public:
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int numInp, numOut;
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Mat Wh, Wx, b, h, c;
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Ptr<LSTMLayer> layer;
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std::vector<Mat> inputs, outputs;
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Layer_LSTM_Test() {}
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void init(const MatShape &inpShape_, const MatShape &outShape_,
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bool produceCellOutput, bool useTimestampDim)
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{
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numInp = total(inpShape_);
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numOut = total(outShape_);
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Wh = Mat::ones(4 * numOut, numOut, CV_32F);
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Wx = Mat::ones(4 * numOut, numInp, CV_32F);
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b = Mat::ones(4 * numOut, 1, CV_32F);
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h = Mat::ones(4, numOut, CV_32F);
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c = Mat::ones(4, numOut, CV_32F);
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LayerParams lp;
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lp.blobs.resize(5);
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lp.blobs[0] = Wh;
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lp.blobs[1] = Wx;
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lp.blobs[2] = b;
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lp.blobs[3] = h;
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lp.blobs[4] = c;
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lp.set<bool>("produce_cell_output", produceCellOutput);
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lp.set<bool>("use_timestamp_dim", useTimestampDim);
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layer = LSTMLayer::create(lp);
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layer->setOutShape(outShape_);
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}
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};
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TEST_F(Layer_LSTM_Test, get_set_test)
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{
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const int TN = 4;
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MatShape inpShape = shape(5, 3, 2);
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MatShape outShape = shape(3, 1, 2);
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MatShape inpResShape = concat(shape(TN), inpShape);
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MatShape outResShape = concat(shape(TN), outShape);
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init(inpShape, outShape, true, false);
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layer->setOutShape(outShape);
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Mat C((int)outResShape.size(), &outResShape[0], CV_32F);
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randu(C, -1., 1.);
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Mat H = C.clone();
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randu(H, -1., 1.);
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Mat inp((int)inpResShape.size(), &inpResShape[0], CV_32F);
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randu(inp, -1., 1.);
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inputs.push_back(inp);
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runLayer(layer, inputs, outputs);
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EXPECT_EQ(2u, outputs.size());
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//print(outResShape, "outResShape");
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//print(shape(outputs[0]), "out0");
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//print(shape(outputs[0]), "out1");
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EXPECT_EQ(outResShape, shape(outputs[0]));
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EXPECT_EQ(outResShape, shape(outputs[1]));
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EXPECT_EQ(0, layer->inputNameToIndex("x"));
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EXPECT_EQ(0, layer->outputNameToIndex("h"));
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EXPECT_EQ(1, layer->outputNameToIndex("c"));
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}
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TEST(Layer_LSTM_Test_Accuracy_with_, CaffeRecurrent)
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{
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LayerParams lp;
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lp.blobs.resize(5);
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lp.blobs[0] = blobFromNPY(_tf("lstm.prototxt.w_2.npy")); // Wh
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lp.blobs[1] = blobFromNPY(_tf("lstm.prototxt.w_0.npy")); // Wx
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lp.blobs[2] = blobFromNPY(_tf("lstm.prototxt.w_1.npy")); // bias
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lp.blobs[3] = Mat::zeros(2, 17, CV_32F); // h_0
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lp.blobs[4] = Mat::zeros(2, 17, CV_32F); // c_0
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Ptr<LSTMLayer> layer = LSTMLayer::create(lp);
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Mat inp = blobFromNPY(_tf("recurrent.input.npy"));
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std::vector<Mat> inputs(1, inp), outputs;
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runLayer(layer, inputs, outputs);
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Mat h_t_reference = blobFromNPY(_tf("lstm.prototxt.h_1.npy"));
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normAssert(h_t_reference, outputs[0]);
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}
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TEST(Layer_LSTM_Test_Accuracy_with_, HiddenParams)
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{
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Mat Wx = blobFromNPY(_tf("lstm.hidden.W.npy"));
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Mat Wh = blobFromNPY(_tf("lstm.hidden.R.npy"));
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Mat b = blobFromNPY(_tf("lstm.hidden.B.npy"));
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Mat h0 = blobFromNPY(_tf("lstm.hidden.h0.npy"));
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Mat c0 = blobFromNPY(_tf("lstm.hidden.c0.npy"));
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const int numHidden = 3;
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const int numDirs = Wx.size[0];
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const int numFeatures = Wx.size[2];
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b = b.reshape(1, b.size[0]);
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Mat bx = b.colRange(0, b.cols / 2);
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Mat bh = b.colRange(b.cols / 2, b.cols);
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b = bx + bh;
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// IFGO->IGFO
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for (int k = 0; k < numDirs; ++k)
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{
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float* WxData = Wx.ptr<float>(k);
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float* WhData = Wh.ptr<float>(k);
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float* biasData = b.ptr<float>(k);
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for (int j = 0; j < numHidden; ++j)
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{
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for (int i = 0; i < numFeatures; ++i)
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{
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std::swap(WxData[(numHidden + j) * numFeatures + i],
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WxData[(numHidden * 2 + j) * numFeatures + i]);
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}
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for (int i = 0; i < numHidden; ++i)
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{
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std::swap(WhData[(numHidden + j) * numHidden + i],
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WhData[(numHidden * 2 + j) * numHidden + i]);
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}
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std::swap(biasData[numHidden + j], biasData[numHidden * 2 + j]);
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}
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}
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Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]);
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Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]);
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h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
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c0 = c0.reshape(1, c0.size[0] * c0.size[1]);
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LayerParams lstmParams;
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lstmParams.blobs.resize(5);
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lstmParams.blobs[0] = Wh;
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lstmParams.blobs[1] = Wx;
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lstmParams.blobs[2] = b;
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lstmParams.blobs[3] = h0;
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lstmParams.blobs[4] = c0;
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lstmParams.set("bidirectional", false);
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Ptr<LSTMLayer> layer = LSTMLayer::create(lstmParams);
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Mat inp = blobFromNPY(_tf("lstm.hidden.input.npy"));
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std::vector<Mat> inputs(1, inp), outputs;
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runLayer(layer, inputs, outputs);
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Mat h_t_reference = blobFromNPY(_tf("lstm.hidden.output.npy"));
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normAssert(h_t_reference, outputs[0]);
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}
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TEST(Layer_GRU_Test_Accuracy_with_, Pytorch)
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{
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Mat Wx = blobFromNPY(_tf("gru.W.npy"));
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Mat Wh = blobFromNPY(_tf("gru.R.npy"));
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Mat b = blobFromNPY(_tf("gru.B.npy"));
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Mat h0 = blobFromNPY(_tf("gru.h0.npy"));
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Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]);
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Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]);
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h0 = h0.reshape(1, h0.size[0] * h0.size[1]);
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b = b.reshape(1, b.size[0]);
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LayerParams gruParams;
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gruParams.blobs.resize(4);
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gruParams.blobs[0] = Wh;
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gruParams.blobs[1] = Wx;
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gruParams.blobs[2] = b;
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gruParams.blobs[3] = h0;
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gruParams.set("bidirectional", false);
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Ptr<GRULayer> layer = GRULayer::create(gruParams);
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Mat inp = blobFromNPY(_tf("gru.input.npy"));
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std::vector<Mat> inputs(1, inp), outputs;
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runLayer(layer, inputs, outputs);
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Mat h_t_reference = blobFromNPY(_tf("gru.output.npy"));
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normAssert(h_t_reference, outputs[0]);
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}
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TEST(Layer_RNN_Test_Accuracy_with_, CaffeRecurrent)
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{
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Ptr<RNNLayer> layer = RNNLayer::create(LayerParams());
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layer->setWeights(
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blobFromNPY(_tf("rnn.prototxt.w_0.npy")),
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blobFromNPY(_tf("rnn.prototxt.w_1.npy")),
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blobFromNPY(_tf("rnn.prototxt.w_2.npy")),
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blobFromNPY(_tf("rnn.prototxt.w_3.npy")),
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blobFromNPY(_tf("rnn.prototxt.w_4.npy")) );
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std::vector<Mat> output, input(1, blobFromNPY(_tf("recurrent.input.npy")));
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runLayer(layer, input, output);
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Mat h_ref = blobFromNPY(_tf("rnn.prototxt.h_1.npy"));
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normAssert(h_ref, output[0]);
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}
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TEST(Layer_LSTM_Test_Accuracy_, Reverse)
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{
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// This handcrafted setup calculates (approximately) the prefix sum of the
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// input, assuming the inputs are suitably small.
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cv::Mat input(2, 1, CV_32FC1);
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input.at<float>(0, 0) = 1e-5f;
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input.at<float>(1, 0) = 2e-5f;
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cv::Mat Wx(4, 1, CV_32FC1);
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Wx.at<float>(0, 0) = 0.f; // Input gate
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Wx.at<float>(1, 0) = 0.f; // Forget gate
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Wx.at<float>(2, 0) = 0.f; // Output gate
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Wx.at<float>(3, 0) = 1.f; // Update signal
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cv::Mat Wh(4, 1, CV_32FC1);
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Wh.at<float>(0, 0) = 0.f; // Input gate
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Wh.at<float>(1, 0) = 0.f; // Forget gate
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Wh.at<float>(2, 0) = 0.f; // Output gate
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Wh.at<float>(3, 0) = 0.f; // Update signal
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cv::Mat bias(4, 1, CV_32FC1);
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bias.at<float>(0, 0) = 1e10f; // Input gate - always allows input to c
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bias.at<float>(1, 0) = 1e10f; // Forget gate - never forget anything on c
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bias.at<float>(2, 0) = 1e10f; // Output gate - always output everything
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bias.at<float>(3, 0) = 0.f; // Update signal
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cv::Mat hInternal = cv::Mat::zeros(1, 1, CV_32FC1);
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cv::Mat cInternal = cv::Mat::zeros(1, 1, CV_32FC1);
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LayerParams lp;
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lp.set("reverse", true);
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lp.set("use_timestamp_dim", true);
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lp.blobs.clear();
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lp.blobs.push_back(Wh);
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lp.blobs.push_back(Wx);
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lp.blobs.push_back(bias);
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lp.blobs.push_back(hInternal);
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lp.blobs.push_back(cInternal);
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cv::Ptr<cv::dnn::LSTMLayer> layer = LSTMLayer::create(lp);
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std::vector<cv::Mat> outputs;
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std::vector<cv::Mat> inputs;
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inputs.push_back(input);
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runLayer(layer, inputs, outputs);
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ASSERT_EQ(1, outputs.size());
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cv::Mat out = outputs[0];
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ASSERT_EQ(3, out.dims);
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ASSERT_EQ(shape(2, 1, 1), shape(out));
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float* data = reinterpret_cast<float*>(out.data);
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EXPECT_NEAR(std::tanh(1e-5f) + std::tanh(2e-5f), data[0], 1e-10);
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EXPECT_NEAR(std::tanh(2e-5f), data[1], 1e-10);
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}
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class Layer_RNN_Test : public ::testing::Test
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{
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public:
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int nX, nH, nO, nT, nS;
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Mat Whh, Wxh, bh, Who, bo;
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Ptr<RNNLayer> layer;
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std::vector<Mat> inputs, outputs;
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Layer_RNN_Test()
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{
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nT = 3;
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nS = 5;
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nX = 31;
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nH = 64;
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nO = 100;
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Whh = Mat::ones(nH, nH, CV_32F);
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Wxh = Mat::ones(nH, nX, CV_32F);
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bh = Mat::ones(nH, 1, CV_32F);
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Who = Mat::ones(nO, nH, CV_32F);
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bo = Mat::ones(nO, 1, CV_32F);
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layer = RNNLayer::create(LayerParams());
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layer->setProduceHiddenOutput(true);
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layer->setWeights(Wxh, bh, Whh, Who, bo);
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}
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};
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TEST_F(Layer_RNN_Test, get_set_test)
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{
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int sz[] = { nT, nS, 1, nX };
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Mat inp(4, sz, CV_32F);
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randu(inp, -1., 1.);
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inputs.push_back(inp);
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runLayer(layer, inputs, outputs);
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EXPECT_EQ(outputs.size(), 2u);
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EXPECT_EQ(shape(outputs[0]), shape(nT, nS, nO));
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EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
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}
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TEST(Layer_MHARoPe_Test_Accuracy_with_, Pytorch)
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{
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Mat QKV = blobFromNPY(_tf("mha_rope.QKV.npy"));
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Mat QKV_bias = blobFromNPY(_tf("mha_rope.QKV_bias.npy"));
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std::vector<int> qkv_hidden_sizes = { 256, 256, 256 };
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LayerParams mhaParams;
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mhaParams.blobs.resize(2);
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mhaParams.blobs[0] = QKV;
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mhaParams.blobs[1] = QKV_bias;
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mhaParams.set("num_heads", 4);
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mhaParams.set(
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"qkv_hidden_sizes",
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DictValue::arrayInt(&qkv_hidden_sizes[0], qkv_hidden_sizes.size())
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);
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mhaParams.set("do_rotary", true);
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Ptr<AttentionLayer> layer = AttentionLayer::create(mhaParams);
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Mat inp = blobFromNPY(_tf("mha_rope.input.npy"));
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std::vector<Mat> inputs(1, inp), outputs;
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runLayer(layer, inputs, outputs);
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Mat h_t_reference = blobFromNPY(_tf("mha_rope.output.npy"));
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normAssert(h_t_reference, outputs[0]);
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}
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typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
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TEST_P(Scale_untrainable, Accuracy)
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{
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Vec4i inpShapeVec = get<0>(GetParam());
|
|
int axis = get<1>(GetParam())[0];
|
|
int weightsDims = get<1>(GetParam())[1];
|
|
bool testFusion = get<2>(GetParam());
|
|
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
|
|
|
|
// Create a network with two inputs. Scale layer multiplies a first input to
|
|
// a second one. See http://caffe.berkeleyvision.org/tutorial/layers/scale.html
|
|
Net net;
|
|
// Check that this version of Scale layer won't be fused with Convolution layer.
|
|
if (testFusion)
|
|
{
|
|
LayerParams lp;
|
|
lp.set("kernel_size", 1);
|
|
lp.set("num_output", 3);
|
|
lp.set("group", 3);
|
|
lp.set("bias_term", false);
|
|
lp.type = "Convolution";
|
|
lp.name = "testConv";
|
|
|
|
std::vector<int> weightsShape(4);
|
|
weightsShape[0] = 3; // #outChannels
|
|
weightsShape[1] = 1; // #inpChannels / group
|
|
weightsShape[2] = 1; // height
|
|
weightsShape[3] = 1; // width
|
|
Mat weights(weightsShape, CV_32F);
|
|
weights.setTo(1);
|
|
lp.blobs.push_back(weights);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
LayerParams lp;
|
|
lp.type = "Scale";
|
|
lp.name = "testLayer";
|
|
lp.set("axis", axis);
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 1, id, 1);
|
|
|
|
Mat input(4, inpShape, CV_32F);
|
|
Mat weights(weightsDims, &inpShape[axis], CV_32F);
|
|
randu(input, -1, 1);
|
|
randu(weights, -1, 1);
|
|
|
|
std::vector<String> inpNames(2);
|
|
inpNames[0] = "scale_input";
|
|
inpNames[1] = "scale_weights";
|
|
net.setInputsNames(inpNames);
|
|
net.setInput(input, inpNames[0]);
|
|
net.setInput(weights, inpNames[1]);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
Mat out = net.forward();
|
|
|
|
Mat ref(input.size, CV_32F);
|
|
float* inpData = (float*)input.data;
|
|
float* refData = (float*)ref.data;
|
|
float* weightsData = (float*)weights.data;
|
|
int spatialSize = 1;
|
|
for (int i = axis + weightsDims; i < 4; ++i)
|
|
spatialSize *= inpShape[i];
|
|
for (int i = 0; i < ref.total(); ++i)
|
|
{
|
|
float w = weightsData[(i / spatialSize) % weights.total()];
|
|
refData[i] = inpData[i] * w;
|
|
}
|
|
normAssert(out, ref);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test, Scale_untrainable, Combine(
|
|
/*input size*/ Values(Vec4i(2, 3, 4, 5)),
|
|
/*axis, #dims*/ Values(Vec2i(0, 1), Vec2i(0, 2), Vec2i(0, 3), Vec2i(0, 4),
|
|
Vec2i(1, 1), Vec2i(1, 2), Vec2i(1, 3),
|
|
Vec2i(2, 1), Vec2i(2, 2),
|
|
Vec2i(3, 1)),
|
|
/*conv fusion*/ testing::Bool()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<Vec4i, Vec4i, int, int, int> > Crop;
|
|
TEST_P(Crop, Accuracy)
|
|
{
|
|
Vec4i inpShapeVec = get<0>(GetParam());
|
|
Vec4i sizShapeVec = get<1>(GetParam());
|
|
int axis = get<2>(GetParam());
|
|
int numOffsets = get<3>(GetParam());
|
|
int offsetVal = get<4>(GetParam());
|
|
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
|
|
const int sizShape[] = {sizShapeVec[0], sizShapeVec[1], sizShapeVec[2], sizShapeVec[3]};
|
|
|
|
// Create a network with two inputs. Crop layer crops a first input to
|
|
// the size of a second one.
|
|
// See http://caffe.berkeleyvision.org/tutorial/layers/crop.html
|
|
Net net;
|
|
|
|
LayerParams lp;
|
|
lp.name = "testCrop";
|
|
lp.type = "Crop";
|
|
lp.set("axis", axis);
|
|
if (numOffsets > 0)
|
|
{
|
|
std::vector<int> offsets(numOffsets, offsetVal);
|
|
lp.set("offset", DictValue::arrayInt<int*>(&offsets[0], offsets.size()));
|
|
}
|
|
else
|
|
offsetVal = 0;
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 1, id, 1);
|
|
|
|
Mat inpImage(4, inpShape, CV_32F);
|
|
Mat sizImage(4, sizShape, CV_32F);
|
|
randu(inpImage, -1, 1);
|
|
randu(sizImage, -1, 1);
|
|
|
|
std::vector<String> inpNames(2);
|
|
inpNames[0] = "cropImage";
|
|
inpNames[1] = "sizImage";
|
|
net.setInputsNames(inpNames);
|
|
net.setInput(inpImage, inpNames[0]);
|
|
net.setInput(sizImage, inpNames[1]);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
// There are a few conditions that represent invalid input to the crop
|
|
// layer, so in those cases we want to verify an exception is thrown.
|
|
|
|
bool shouldThrowException = false;
|
|
if (numOffsets > 1 && numOffsets != 4 - axis)
|
|
shouldThrowException = true;
|
|
else
|
|
for (int i = axis; i < 4; i++)
|
|
if (sizShape[i] + offsetVal > inpShape[i])
|
|
shouldThrowException = true;
|
|
|
|
Mat out;
|
|
if (shouldThrowException)
|
|
{
|
|
ASSERT_ANY_THROW(out = net.forward());
|
|
return;
|
|
}
|
|
else
|
|
out = net.forward();
|
|
|
|
// Finally, compare the cropped output blob from the DNN layer (out)
|
|
// to a reference blob (ref) that we compute here.
|
|
|
|
std::vector<Range> crop_range;
|
|
crop_range.resize(4, Range::all());
|
|
for (int i = axis; i < 4; i++)
|
|
crop_range[i] = Range(offsetVal, sizShape[i] + offsetVal);
|
|
|
|
Mat ref(sizImage.size, CV_32F);
|
|
inpImage(&crop_range[0]).copyTo(ref);
|
|
normAssert(out, ref);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test, Crop, Combine(
|
|
/*input blob shape*/ Values(Vec4i(1, 3, 20, 30)),
|
|
/*cropsize blob shape*/ Values(Vec4i(1, 3, 10, 12)),
|
|
/*start axis*/ Values(0, 1, 2),
|
|
/*number of offsets*/ Values(0, 1, 2, 4),
|
|
/*offset value*/ Values(3, 4)
|
|
));
|
|
|
|
class Test_Caffe_layers : public DNNTestLayer {};
|
|
|
|
// Check that by default average pooling layer should not count zero padded values
|
|
// into the normalization area.
|
|
TEST_P(Test_Caffe_layers, Average_pooling_kernel_area)
|
|
{
|
|
LayerParams lp;
|
|
lp.name = "testAvePool";
|
|
lp.type = "Pooling";
|
|
lp.set("kernel_size", 2);
|
|
lp.set("stride", 2);
|
|
lp.set("pool", "AVE");
|
|
|
|
Net net;
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
// 1 2 | 3
|
|
// 4 5 | 6
|
|
// ----+--
|
|
// 7 8 | 9
|
|
Mat inp = (Mat_<float>(3, 3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
|
|
Mat ref = (Mat_<float>(2, 2) << (1 + 2 + 4 + 5) / 4.f, (3 + 6) / 2.f, (7 + 8) / 2.f, 9);
|
|
Mat tmp = blobFromImage(inp);
|
|
net.setInput(blobFromImage(inp));
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
Mat out = net.forward();
|
|
normAssert(out, blobFromImage(ref));
|
|
}
|
|
|
|
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
|
|
TEST_P(Test_Caffe_layers, PriorBox_squares)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
|
|
LayerParams lp;
|
|
lp.name = "testPriorBox";
|
|
lp.type = "PriorBox";
|
|
lp.set("min_size", 2);
|
|
lp.set("flip", true);
|
|
lp.set("clip", true);
|
|
float variance[] = {0.1f, 0.1f, 0.2f, 0.2f};
|
|
float aspectRatios[] = {1.0f}; // That should be ignored.
|
|
lp.set("variance", DictValue::arrayReal<float*>(&variance[0], 4));
|
|
lp.set("aspect_ratio", DictValue::arrayReal<float*>(&aspectRatios[0], 1));
|
|
|
|
Net net;
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 0, id, 1); // The second input is an input image. Shapes are used for boxes normalization.
|
|
Mat inp(1, 2, CV_32F);
|
|
randu(inp, -1, 1);
|
|
net.setInput(blobFromImage(inp));
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
Mat out = net.forward();
|
|
|
|
Mat ref = (Mat_<float>(4, 4) << 0.0, 0.0, 0.75, 1.0,
|
|
0.25, 0.0, 1.0, 1.0,
|
|
0.1f, 0.1f, 0.2f, 0.2f,
|
|
0.1f, 0.1f, 0.2f, 0.2f);
|
|
double l1 = 1e-5;
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
|
|
l1 = 2e-5;
|
|
normAssert(out.reshape(1, 4), ref, "", l1);
|
|
}
|
|
|
|
typedef TestWithParam<tuple<int, int> > Layer_Test_DWconv_Prelu;
|
|
TEST_P(Layer_Test_DWconv_Prelu, Accuracy)
|
|
{
|
|
// Test case
|
|
// input img size 3x16x16 value all 1
|
|
// |
|
|
// v
|
|
// dw_conv weight[0]=-1 weight[1]=-2 weight[2]=-3 bias={1,2,3}
|
|
// |
|
|
// v
|
|
// prelu weight={1,2,3}
|
|
// |
|
|
// v
|
|
// output out size 3x14x14 if right: out[0]=-8 out[0]=-32 out[0]=-72
|
|
// but current opencv output: out[0]=-24 out[0]=-48 out[0]=-72
|
|
|
|
const int num_input = get<0>(GetParam()); //inpChannels
|
|
const int group = 3; //outChannels=group when group>1
|
|
const int num_output = get<1>(GetParam());
|
|
const int kernel_depth = num_input/group;
|
|
CV_Assert_N(num_output >= group, num_output % group == 0, num_input % group == 0);
|
|
|
|
Net net;
|
|
//layer 1: dwconv
|
|
LayerParams lp;
|
|
lp.name = "dwconv";
|
|
lp.type = "Convolution";
|
|
lp.set("kernel_size", 3);
|
|
lp.set("num_output", num_output);
|
|
lp.set("pad", 0);
|
|
lp.set("group", group);
|
|
lp.set("stride", 1);
|
|
lp.set("engine", "CAFFE");
|
|
lp.set("bias_term", "true");
|
|
|
|
std::vector<int> weightsShape(4);
|
|
weightsShape[0] = num_output; // #outChannels
|
|
weightsShape[1] = kernel_depth; // #inpChannels / group
|
|
weightsShape[2] = 3; // height
|
|
weightsShape[3] = 3; // width
|
|
Mat weights(weightsShape, CV_32F, Scalar(1));
|
|
|
|
//assign weights
|
|
for (int i = 0; i < weightsShape[0]; ++i)
|
|
{
|
|
for (int j = 0; j < weightsShape[1]; ++j)
|
|
{
|
|
for (int k = 0; k < weightsShape[2]; ++k)
|
|
{
|
|
for (int l = 0; l < weightsShape[3]; ++l)
|
|
{
|
|
weights.ptr<float>(i, j, k)[l]=-1*(i+1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
lp.blobs.push_back(weights);
|
|
|
|
//assign bias
|
|
Mat bias(1, num_output, CV_32F, Scalar(1));
|
|
for (int i = 0; i < 1; ++i)
|
|
{
|
|
for (int j = 0; j < num_output; ++j)
|
|
{
|
|
bias.ptr<float>(i)[j]=j+1;
|
|
}
|
|
}
|
|
lp.blobs.push_back(bias);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
//layer 2: prelu
|
|
LayerParams lpr;
|
|
lpr.name = "dw_relu";
|
|
lpr.type = "PReLU";
|
|
Mat weightsp(1, num_output, CV_32F, Scalar(1));
|
|
|
|
//assign weights
|
|
for (int i = 0; i < 1; ++i)
|
|
{
|
|
for (int j = 0; j < num_output; ++j)
|
|
{
|
|
weightsp.ptr<float>(i)[j]=j+1;
|
|
}
|
|
}
|
|
|
|
lpr.blobs.push_back(weightsp);
|
|
net.addLayerToPrev(lpr.name, lpr.type, lpr);
|
|
|
|
int shape[] = {1, num_input, 16, 16};
|
|
Mat in_blob(4, &shape[0], CV_32FC1, Scalar(1));
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
net.enableWinograd(false);
|
|
net.setInput(in_blob);
|
|
Mat out = net.forward();
|
|
|
|
//assign target
|
|
std::vector<int> outShape(4);
|
|
outShape[0] = 1;
|
|
outShape[1] = num_output; // outChannels
|
|
outShape[2] = 14; // height
|
|
outShape[3] = 14; // width
|
|
Mat target(outShape, CV_32F, Scalar(1));
|
|
for (int i = 0; i < outShape[0]; ++i)
|
|
{
|
|
for (int j = 0; j < outShape[1]; ++j)
|
|
{
|
|
for (int k = 0; k < outShape[2]; ++k)
|
|
{
|
|
for (int l = 0; l < outShape[3]; ++l)
|
|
{
|
|
target.ptr<float>(i, j, k)[l]=(-9*kernel_depth*(j+1)+j+1)*(j+1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
normAssert(out, target);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Values(3, 6)));
|
|
|
|
#ifdef HAVE_INF_ENGINE
|
|
// Using Intel's Model Optimizer generate .xml and .bin files:
|
|
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
|
|
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
|
|
typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Convolution_DLDT;
|
|
TEST_P(Layer_Test_Convolution_DLDT, multithreading)
|
|
{
|
|
const Backend backendId = get<0>(GetParam());
|
|
const Target targetId = get<1>(GetParam());
|
|
|
|
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
|
|
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
throw SkipTestException("No support for async forward");
|
|
|
|
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
|
|
|
|
std::string xmlPath = _tf("layer_convolution.xml");
|
|
std::string binPath = _tf("layer_convolution.bin");
|
|
Net firstNet = readNet(xmlPath, binPath);
|
|
Net secondNet = readNet(xmlPath, binPath);
|
|
Mat inp = blobFromNPY(_tf("blob.npy"));
|
|
|
|
firstNet.setInput(inp);
|
|
secondNet.setInput(inp);
|
|
firstNet.setPreferableBackend(backendId);
|
|
firstNet.setPreferableTarget(targetId);
|
|
secondNet.setPreferableBackend(backendId);
|
|
secondNet.setPreferableTarget(targetId);
|
|
|
|
Mat out1, out2;
|
|
std::thread t1([&]{out1 = firstNet.forward();});
|
|
std::thread t2([&]{out2 = secondNet.forward();});
|
|
|
|
t1.join();
|
|
t2.join();
|
|
|
|
Mat ref = blobFromNPY(_tf("layer_convolution.npy"));
|
|
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.5e-3 : 1e-5;
|
|
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.8e-2 : 1e-4;
|
|
normAssert(out1, ref, "first thread", l1, lInf);
|
|
normAssert(out2, ref, "second thread", l1, lInf);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Convolution_DLDT,
|
|
dnnBackendsAndTargetsIE()
|
|
);
|
|
|
|
// 1. Create a .prototxt file with the following network:
|
|
// layer {
|
|
// type: "Input" name: "data" top: "data"
|
|
// input_param { shape { dim: 1 dim: 2 dim: 3 } }
|
|
// }
|
|
// layer {
|
|
// type: "Input" name: "second_input" top: "second_input"
|
|
// input_param { shape { dim: 1 dim: 2 dim: 3 } }
|
|
// }
|
|
// layer {
|
|
// type: "Eltwise" name: "output" top: "output"
|
|
// bottom: "data" bottom: "second_input"
|
|
// eltwise_param { operation: SUM }
|
|
// }
|
|
//
|
|
// 2. Create a .caffemodel file using Caffe:
|
|
//
|
|
// import caffe
|
|
// net = caffe.Net('/path/to/prototxt', caffe.TEST)
|
|
// net.save('/path/to/caffemodel')
|
|
//
|
|
// 3. Convert using ModelOptimizer.
|
|
typedef testing::TestWithParam<tuple<int, int, Target, std::vector<int> > > Test_DLDT_two_inputs_3dim;
|
|
TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
|
|
{
|
|
int firstInpType = get<0>(GetParam());
|
|
int secondInpType = get<1>(GetParam());
|
|
Target targetId = get<2>(GetParam());
|
|
|
|
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
|
|
std::vector<int> inpSize = get<3>(GetParam());
|
|
Mat firstInp(3, inpSize.data(), firstInpType);
|
|
Mat secondInp(3, inpSize.data(), secondInpType);
|
|
randu(firstInp, 0, 255);
|
|
randu(secondInp, 0, 255);
|
|
|
|
net.setInput(firstInp, "data");
|
|
net.setInput(secondInp, "second_input");
|
|
net.setPreferableTarget(targetId);
|
|
|
|
double l1 = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
|
|
(firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.06 : 0.0;
|
|
double lInf = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
|
|
(firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.23 : 0.0;
|
|
|
|
Mat out = net.forward();
|
|
|
|
Mat ref;
|
|
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
|
|
normAssert(out, ref, "", l1, lInf);
|
|
}
|
|
|
|
std::vector< std::vector<int> > list_sizes{ {1, 2, 3}, {3, 2, 1}, {5, 5, 5}, {13, 7, 11} };
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs_3dim, Combine(
|
|
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
|
|
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)),
|
|
testing::ValuesIn(list_sizes)
|
|
));
|
|
|
|
class UnsupportedLayer : public Layer
|
|
{
|
|
public:
|
|
UnsupportedLayer(const LayerParams ¶ms) : Layer(params) {}
|
|
|
|
static Ptr<Layer> create(const LayerParams& params)
|
|
{
|
|
return Ptr<Layer>(new UnsupportedLayer(params));
|
|
}
|
|
|
|
virtual bool supportBackend(int backendId) CV_OVERRIDE
|
|
{
|
|
return backendId == DNN_BACKEND_OPENCV;
|
|
}
|
|
|
|
virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE {}
|
|
};
|
|
|
|
typedef DNNTestLayer Test_DLDT_layers;
|
|
|
|
static void test_dldt_fused_output(Backend backend, Target target)
|
|
{
|
|
static const int kNumChannels = 3;
|
|
Net net;
|
|
{
|
|
LayerParams lp;
|
|
lp.set("kernel_size", 1);
|
|
lp.set("num_output", 3);
|
|
lp.set("bias_term", false);
|
|
lp.type = "Convolution";
|
|
lp.name = "testConv";
|
|
lp.blobs.push_back(Mat({kNumChannels, 1, 1, 1}, CV_32F, Scalar(1)));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
{
|
|
LayerParams lp;
|
|
lp.set("bias_term", false);
|
|
lp.type = "Scale";
|
|
lp.name = "testScale";
|
|
lp.blobs.push_back(Mat({kNumChannels}, CV_32F, Scalar(1)));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
{
|
|
LayerParams lp;
|
|
net.addLayerToPrev("unsupported_layer", "Unsupported", lp);
|
|
}
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.setInput(Mat({1, 1, 2, 3}, CV_32FC1, Scalar(1)));
|
|
net.forward();
|
|
}
|
|
|
|
TEST_P(Test_DLDT_layers, fused_output)
|
|
{
|
|
CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer);
|
|
try
|
|
{
|
|
test_dldt_fused_output(backend, target);
|
|
}
|
|
catch (const std::exception& e)
|
|
{
|
|
ADD_FAILURE() << "Exception: " << e.what();
|
|
}
|
|
catch(...)
|
|
{
|
|
ADD_FAILURE() << "Unknown exception";
|
|
}
|
|
LayerFactory::unregisterLayer("Unsupported");
|
|
}
|
|
|
|
TEST_P(Test_DLDT_layers, multiple_networks)
|
|
{
|
|
Net nets[2];
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
nets[i].setInputsNames(std::vector<String>(1, format("input_%d", i)));
|
|
|
|
LayerParams lp;
|
|
lp.set("kernel_size", 1);
|
|
lp.set("num_output", 1);
|
|
lp.set("bias_term", false);
|
|
lp.type = "Convolution";
|
|
lp.name = format("testConv_%d", i);
|
|
lp.blobs.push_back(Mat({1, 1, 1, 1}, CV_32F, Scalar(1 + i)));
|
|
nets[i].addLayerToPrev(lp.name, lp.type, lp);
|
|
nets[i].setPreferableBackend(backend);
|
|
nets[i].setPreferableTarget(target);
|
|
nets[i].setInput(Mat({1, 1, 2, 3}, CV_32FC1, Scalar(1)));
|
|
}
|
|
Mat out_1 = nets[0].forward();
|
|
Mat out_2 = nets[1].forward();
|
|
// After the second model is initialized we try to receive an output from the first network again.
|
|
out_1 = nets[0].forward();
|
|
normAssert(2 * out_1, out_2);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_layers, dnnBackendsAndTargets());
|
|
|
|
#endif // HAVE_INF_ENGINE
|
|
|
|
// Test a custom layer.
|
|
class CustomInterpLayer CV_FINAL : public Layer
|
|
{
|
|
public:
|
|
CustomInterpLayer(const LayerParams ¶ms) : Layer(params)
|
|
{
|
|
zoomFactor = params.get<int>("zoom_factor", 0);
|
|
outWidth = params.get<int>("width", 0);
|
|
outHeight = params.get<int>("height", 0);
|
|
}
|
|
|
|
static Ptr<Layer> create(LayerParams& params)
|
|
{
|
|
return Ptr<Layer>(new CustomInterpLayer(params));
|
|
}
|
|
|
|
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<MatShape> &outputs,
|
|
std::vector<MatShape> &internals) const CV_OVERRIDE
|
|
{
|
|
const int batchSize = inputs[0][0];
|
|
const int numChannels = inputs[0][1];
|
|
const int inpHeight = inputs[0][2];
|
|
const int inpWidth = inputs[0][3];
|
|
|
|
MatShape outShape(4);
|
|
outShape[0] = batchSize;
|
|
outShape[1] = numChannels;
|
|
outShape[2] = outHeight != 0 ? outHeight : (inpHeight + (inpHeight - 1) * (zoomFactor - 1));
|
|
outShape[3] = outWidth != 0 ? outWidth : (inpWidth + (inpWidth - 1) * (zoomFactor - 1));
|
|
outputs.assign(1, outShape);
|
|
return false;
|
|
}
|
|
|
|
virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
|
|
{
|
|
std::vector<Mat> outputs;
|
|
outputs_arr.getMatVector(outputs);
|
|
|
|
if (!outWidth && !outHeight)
|
|
{
|
|
outHeight = outputs[0].size[2];
|
|
outWidth = outputs[0].size[3];
|
|
}
|
|
}
|
|
|
|
// Implementation of this custom layer is based on https://github.com/cdmh/deeplab-public/blob/master/src/caffe/layers/interp_layer.cpp
|
|
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
|
|
{
|
|
CV_TRACE_FUNCTION();
|
|
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
|
|
|
if (inputs_arr.depth() == CV_16F)
|
|
{
|
|
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> inputs, outputs;
|
|
inputs_arr.getMatVector(inputs);
|
|
outputs_arr.getMatVector(outputs);
|
|
|
|
Mat& inp = inputs[0];
|
|
Mat& out = outputs[0];
|
|
const float* inpData = (float*)inp.data;
|
|
float* outData = (float*)out.data;
|
|
|
|
const int batchSize = inp.size[0];
|
|
const int numChannels = inp.size[1];
|
|
const int inpHeight = inp.size[2];
|
|
const int inpWidth = inp.size[3];
|
|
|
|
const float rheight = (outHeight > 1) ? static_cast<float>(inpHeight - 1) / (outHeight - 1) : 0.f;
|
|
const float rwidth = (outWidth > 1) ? static_cast<float>(inpWidth - 1) / (outWidth - 1) : 0.f;
|
|
for (int h2 = 0; h2 < outHeight; ++h2)
|
|
{
|
|
const float h1r = rheight * h2;
|
|
const int h1 = h1r;
|
|
const int h1p = (h1 < inpHeight - 1) ? 1 : 0;
|
|
const float h1lambda = h1r - h1;
|
|
const float h0lambda = 1.f - h1lambda;
|
|
for (int w2 = 0; w2 < outWidth; ++w2)
|
|
{
|
|
const float w1r = rwidth * w2;
|
|
const int w1 = w1r;
|
|
const int w1p = (w1 < inpWidth - 1) ? 1 : 0;
|
|
const float w1lambda = w1r - w1;
|
|
const float w0lambda = 1.f - w1lambda;
|
|
const float* pos1 = inpData + h1 * inpWidth + w1;
|
|
float* pos2 = outData + h2 * outWidth + w2;
|
|
for (int c = 0; c < batchSize * numChannels; ++c)
|
|
{
|
|
pos2[0] =
|
|
h0lambda * (w0lambda * pos1[0] + w1lambda * pos1[w1p]) +
|
|
h1lambda * (w0lambda * pos1[h1p * inpWidth] + w1lambda * pos1[h1p * inpWidth + w1p]);
|
|
pos1 += inpWidth * inpHeight;
|
|
pos2 += outWidth * outHeight;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
int outWidth, outHeight, zoomFactor;
|
|
};
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_Caffe_layers, dnnBackendsAndTargets());
|
|
|
|
TEST(Layer_Test_PoolingIndices, Accuracy)
|
|
{
|
|
Net net;
|
|
|
|
LayerParams lp;
|
|
lp.set("pool", "max");
|
|
lp.set("kernel_w", 2);
|
|
lp.set("kernel_h", 2);
|
|
lp.set("stride_w", 2);
|
|
lp.set("stride_h", 2);
|
|
lp.set("pad_w", 0);
|
|
lp.set("pad_h", 0);
|
|
lp.name = "testLayer.name"; // This test also checks that OpenCV lets use names with dots.
|
|
lp.type = "Pooling";
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
Mat inp(10, 10, CV_8U);
|
|
randu(inp, 0, 255);
|
|
|
|
Mat maxValues(5, 5, CV_32F, Scalar(-1)), indices(5, 5, CV_64S, Scalar(-1));
|
|
for (int y = 0; y < 10; ++y)
|
|
{
|
|
int dstY = y / 2;
|
|
for (int x = 0; x < 10; ++x)
|
|
{
|
|
int dstX = x / 2;
|
|
uint8_t val = inp.at<uint8_t>(y, x);
|
|
if ((float)inp.at<uint8_t>(y, x) > maxValues.at<float>(dstY, dstX))
|
|
{
|
|
maxValues.at<float>(dstY, dstX) = val;
|
|
indices.at<int64_t>(dstY, dstX) = y * 10 + x;
|
|
}
|
|
}
|
|
}
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
net.setInput(blobFromImage(inp));
|
|
|
|
std::vector<Mat> outputs;
|
|
net.forward(outputs, lp.name);
|
|
normAssert(maxValues, outputs[0].reshape(1, 5));
|
|
normAssert(indices, outputs[1].reshape(1, 5));
|
|
}
|
|
|
|
typedef testing::TestWithParam<tuple<Vec4i, int, tuple<Backend, Target> > > Layer_Test_ShuffleChannel;
|
|
TEST_P(Layer_Test_ShuffleChannel, Accuracy)
|
|
{
|
|
Vec4i inpShapeVec = get<0>(GetParam());
|
|
int group = get<1>(GetParam());
|
|
ASSERT_EQ(inpShapeVec[1] % group, 0);
|
|
const int groupSize = inpShapeVec[1] / group;
|
|
int backendId = get<0>(get<2>(GetParam()));
|
|
int targetId = get<1>(get<2>(GetParam()));
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.set("group", group);
|
|
lp.type = "ShuffleChannel";
|
|
lp.name = "testLayer";
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
|
|
Mat inp(4, inpShape, CV_32F);
|
|
randu(inp, 0, 255);
|
|
|
|
net.setInput(inp);
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
|
|
double l1 = 1e-5, lInf = 1e-4;
|
|
if (targetId == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
l1 = 5e-2;
|
|
lInf = 7e-2;
|
|
}
|
|
else if (targetId == DNN_TARGET_CUDA_FP16)
|
|
{
|
|
l1 = 0.06;
|
|
lInf = 0.07;
|
|
}
|
|
for (int n = 0; n < inpShapeVec[0]; ++n)
|
|
{
|
|
for (int c = 0; c < inpShapeVec[1]; ++c)
|
|
{
|
|
Mat outChannel = getPlane(out, n, c);
|
|
Mat inpChannel = getPlane(inp, n, groupSize * (c % group) + c / group);
|
|
normAssert(outChannel, inpChannel, "", l1, lInf);
|
|
}
|
|
}
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
|
|
/*input shape*/ Values(Vec4i(1, 6, 5, 7), Vec4i(3, 12, 1, 4)),
|
|
/*group*/ Values(1, 2, 3, 6), dnnBackendsAndTargets(/*with IE*/ false)
|
|
));
|
|
|
|
TEST(Layer_Test_ReduceMean, accuracy_input_0)
|
|
{
|
|
vector<int> szData = { 2, 1, 2, 1 ,2 };
|
|
std::vector<float> initData = { 0, 1, 2, 3, 4, 5, 6, 7 };
|
|
Mat inpInitA(szData, CV_32FC1, Mat(initData).data);
|
|
std::vector<float> resAxes0 = { 2, 3, 4, 5 };
|
|
std::vector<float> resAxes1 = { 0, 1, 2, 3, 4, 5, 6, 7 };
|
|
std::vector<float> resAxes2 = { 1, 2, 5, 6 };
|
|
std::vector<float> resAxes3 = { 0, 1, 2, 3, 4, 5, 6, 7 };
|
|
std::vector<float> resAxes4 = { 0.5, 2.5, 4.5, 6.5 };
|
|
std::vector < vector<float>> resReduceMean = { resAxes0, resAxes1, resAxes2, resAxes3, resAxes4 };
|
|
|
|
|
|
for (int i = 0; i < resReduceMean.size(); i++)
|
|
{
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.set("keepdims", 0);
|
|
lp.type = "Reduce";
|
|
lp.set("reduce", "MEAN");
|
|
lp.name = "testReduceMean";
|
|
lp.set("axes", i);
|
|
lp.blobs.push_back(inpInitA);
|
|
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.setInput(inpInitA);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
Mat output = net.forward();
|
|
MatShape gt_shape;
|
|
for (int j = 0; j < szData.size(); j++)
|
|
{
|
|
if (i == j) continue;
|
|
gt_shape.push_back(szData[j]);
|
|
}
|
|
|
|
EXPECT_EQ(gt_shape, shape(output));
|
|
normAssert(output, Mat(gt_shape, CV_32F, resReduceMean[i].data()));
|
|
}
|
|
}
|
|
|
|
|
|
// Check if relu is not fused to convolution if we requested it's output
|
|
TEST(Layer_Test_Convolution, relu_fusion)
|
|
{
|
|
Net net;
|
|
{
|
|
LayerParams lp;
|
|
lp.set("kernel_size", 1);
|
|
lp.set("num_output", 1);
|
|
lp.set("bias_term", false);
|
|
lp.type = "Convolution";
|
|
lp.name = "testConv";
|
|
|
|
int weightsShape[] = {1, 1, 1, 1};
|
|
Mat weights(4, &weightsShape[0], CV_32F, Scalar(1));
|
|
lp.blobs.push_back(weights);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "ReLU";
|
|
lp.name = "testReLU";
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
int sz[] = {1, 1, 2, 3};
|
|
Mat input(4, &sz[0], CV_32F);
|
|
randu(input, -1.0, -0.1);
|
|
net.setInput(input);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
Mat output = net.forward("testConv");
|
|
normAssert(input, output);
|
|
}
|
|
|
|
typedef testing::TestWithParam<tuple<bool, tuple<Backend, Target> > > Layer_Test_Eltwise_unequal;
|
|
TEST_P(Layer_Test_Eltwise_unequal, accuracy_input_0_truncate)
|
|
{
|
|
bool weighted = get<0>(GetParam());
|
|
int backendId = get<0>(get<1>(GetParam()));
|
|
int targetId = get<1>(get<1>(GetParam()));
|
|
|
|
if (backendId == DNN_BACKEND_CUDA && weighted)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Eltwise";
|
|
lp.name = "testLayer";
|
|
lp.set<std::string>("output_channels_mode", "input_0_truncate");
|
|
|
|
const int inpShapes[][4] = {{1, 4, 2, 2}, {1, 5, 2, 2}, {1, 3, 2, 2}};
|
|
const int out_channels = inpShapes[0][1];
|
|
std::vector<String> inpNames(3);
|
|
std::vector<Mat> inputs(3);
|
|
|
|
std::vector<float> weights(3, 1);
|
|
if (weighted)
|
|
{
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
weights[i] = -0.125f + i * 0.25f;
|
|
lp.set("coeff", DictValue::arrayReal<float*>(&weights[0], weights.size()));
|
|
}
|
|
|
|
int eltwiseId = net.addLayer(lp.name, lp.type, lp);
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
{
|
|
inputs[i].create(4, inpShapes[i], CV_32F);
|
|
size_t total = inputs[i].total();
|
|
for (size_t j = 0; j < total; j++)
|
|
inputs[i].ptr<float>()[j] = j + i * 100;
|
|
inpNames[i] = format("input_%d", i);
|
|
net.connect(0, i, eltwiseId, i);
|
|
}
|
|
Mat ref(4, inpShapes[0], CV_32F, Scalar(0));
|
|
|
|
net.setInputsNames(inpNames);
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
{
|
|
//std::cout << ref.reshape(1,1) << endl;
|
|
net.setInput(inputs[i], inpNames[i]);
|
|
for (size_t batchId = 0; batchId < ref.size[0]; batchId++)
|
|
{
|
|
int input_channels = inputs[i].size[1];
|
|
Range ranges[4] = { Range(batchId, batchId + 1), Range(0, std::min(out_channels, input_channels)), Range::all(), Range::all() };
|
|
Mat ref_slice = ref(ranges);
|
|
Mat input_slice = inputs[i](ranges);
|
|
ref_slice += weights[i] * input_slice;
|
|
}
|
|
}
|
|
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
normAssert(out, ref);
|
|
if (testing::Test::HasFailure())
|
|
{
|
|
std::cout << out.reshape(1,1) << endl;
|
|
std::cout << ref.reshape(1,1) << endl;
|
|
}
|
|
}
|
|
|
|
TEST_P(Layer_Test_Eltwise_unequal, accuracy_input_0)
|
|
{
|
|
bool weighted = get<0>(GetParam());
|
|
int backendId = get<0>(get<1>(GetParam()));
|
|
int targetId = get<1>(get<1>(GetParam()));
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Eltwise";
|
|
lp.name = "testLayer";
|
|
lp.set<std::string>("output_channels_mode", "input_0");
|
|
|
|
if (backendId == DNN_BACKEND_CUDA && weighted)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
|
|
|
|
const int inpShapes[][4] = {{1, 4, 2, 2}, {1, 2, 2, 2}, {1, 3, 2, 2}};
|
|
const int out_channels = inpShapes[0][1];
|
|
std::vector<String> inpNames(3);
|
|
std::vector<Mat> inputs(3);
|
|
|
|
std::vector<float> weights(3, 1);
|
|
if (weighted)
|
|
{
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
weights[i] = -0.125f + i * 0.25f;
|
|
lp.set("coeff", DictValue::arrayReal<float*>(&weights[0], weights.size()));
|
|
}
|
|
|
|
int eltwiseId = net.addLayer(lp.name, lp.type, lp);
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
{
|
|
inputs[i].create(4, inpShapes[i], CV_32F);
|
|
size_t total = inputs[i].total();
|
|
for (size_t j = 0; j < total; j++)
|
|
inputs[i].ptr<float>()[j] = j + i * 100;
|
|
inpNames[i] = format("input_%d", i);
|
|
net.connect(0, i, eltwiseId, i);
|
|
}
|
|
Mat ref(4, inpShapes[0], CV_32F, Scalar(0));
|
|
|
|
net.setInputsNames(inpNames);
|
|
for (int i = 0; i < inputs.size(); ++i)
|
|
{
|
|
//std::cout << ref.reshape(1,1) << endl;
|
|
net.setInput(inputs[i], inpNames[i]);
|
|
for (size_t batchId = 0; batchId < ref.size[0]; batchId++)
|
|
{
|
|
int input_channels = inputs[i].size[1];
|
|
Range ranges[4] = { Range(batchId, batchId + 1), Range(0, std::min(out_channels, input_channels)), Range::all(), Range::all() };
|
|
Mat ref_slice = ref(ranges);
|
|
Mat input_slice = inputs[i](ranges);
|
|
ref_slice += weights[i] * input_slice;
|
|
}
|
|
}
|
|
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
normAssert(out, ref);
|
|
if (testing::Test::HasFailure())
|
|
{
|
|
std::cout << out.reshape(1,1) << endl;
|
|
std::cout << ref.reshape(1,1) << endl;
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_unequal, Combine(
|
|
testing::Bool(),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
|
|
struct Layer_Test_Eltwise_bcast : testing::TestWithParam<tuple<string, int, tuple<Backend, Target>>>
|
|
{
|
|
public:
|
|
void test_bcast()
|
|
{
|
|
string op = get<0>(GetParam());
|
|
int dim = get<1>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<2>(GetParam());
|
|
int backend = get<0>(backend_target);
|
|
int target = get<1>(backend_target);
|
|
|
|
if (backend == DNN_BACKEND_CUDA && dim > 4)
|
|
applyTestTag(CV_TEST_TAG_LONG);
|
|
|
|
vector<vector<int>> dim_shape_list;
|
|
get_all_arr(dim_shape_list, dim);
|
|
replace(dim_shape_list, 1, 3);
|
|
// same shape
|
|
for (int i = 0; i < dim_shape_list.size(); i++)
|
|
for (int j = 0; j < dim_shape_list.size(); j++)
|
|
run(dim_shape_list[i], dim_shape_list[j], op, backend, target);
|
|
|
|
vector<vector<int>> sub_shape_list;
|
|
vector<vector<int>> tmp;
|
|
for(int i = 1; i < dim; i++){
|
|
get_all_arr(tmp, i);
|
|
replace(tmp, 1, 3);
|
|
sub_shape_list.insert(sub_shape_list.end(), tmp.begin(), tmp.end());
|
|
}
|
|
|
|
// diff shape
|
|
for (const auto &shp1: dim_shape_list)
|
|
for (const auto &shp2: sub_shape_list)
|
|
run(shp1, shp2, op, backend, target);
|
|
|
|
// diff shape
|
|
for (const auto &shp1: sub_shape_list)
|
|
for (const auto &shp2: dim_shape_list)
|
|
run(shp1, shp2, op, backend, target);
|
|
}
|
|
|
|
private:
|
|
// give n to generate all n-D arrays with 0 or 1
|
|
static void get_all_arr(vector<vector<int>> &arr, int n)
|
|
{
|
|
int total = 1 << n;
|
|
arr.assign(total, vector<int>(n, -1));
|
|
for (int i = 0; i < total; i++)
|
|
for (int j = 0; j < n; j++)
|
|
arr[i][j] = (i >> (n - j - 1)) & 1;
|
|
}
|
|
|
|
// zero will replace all 0, one will replace all 1
|
|
static void replace(vector<vector<int>> &arr, int zero, int one)
|
|
{
|
|
for (int i = 0; i < arr.size(); i++)
|
|
for (int j = 0; j < arr[0].size(); j++)
|
|
arr[i][j] = arr[i][j] ? one : zero;
|
|
}
|
|
|
|
static void run(const vector<int> &a_shape, const vector<int> &b_shape, const String &op, const int backend, const int target)
|
|
{
|
|
Mat a = Mat::zeros((int) a_shape.size(), a_shape.data(), CV_32FC1);
|
|
Mat b = Mat::ones((int) b_shape.size(), b_shape.data(), CV_32FC1);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "NaryEltwise";
|
|
lp.name = "testLayer";
|
|
lp.set("operation", op);
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 1, id, 1);
|
|
|
|
vector<String> inpNames(2);
|
|
inpNames[0] = "a";
|
|
inpNames[1] = "b";
|
|
net.setInputsNames(inpNames);
|
|
net.setInput(a, inpNames[0]);
|
|
net.setInput(b, inpNames[1]);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
auto ptr_re = (float *) re.data;
|
|
for (int i = 0; i < re.total(); i++)
|
|
if (op == "sum"){
|
|
ASSERT_EQ(1, ptr_re[i]); // sum result should be 1
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST_P(Layer_Test_Eltwise_bcast, brute_force)
|
|
{
|
|
test_bcast();
|
|
}
|
|
|
|
// This test is to verify whether the broadcast operations of unidirectional and bidirectional,
|
|
// as well as tensors with same and different shapes, can be forwarded correctly.
|
|
// This can ensure that the elementwise layer does not have any errors when forwarding.
|
|
//
|
|
// To test which cases the backend will fallback to the cpu, replace the fallback command like
|
|
// `return Ptr<BackendNode>();` in `initCUDA()` with `throw std::runtime_error("fallback");`
|
|
//
|
|
// To test more operators, add more ops after "sum".
|
|
// Default only "sum" is tested, because for the most cases they have the same implementation.
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Eltwise_bcast, Combine(
|
|
Values("sum"),
|
|
Values(1, 2, 3, 4, 5),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Resize;
|
|
TEST_P(Layer_Test_Resize, change_input)
|
|
{
|
|
int backendId = get<0>(GetParam());
|
|
int targetId = get<1>(GetParam());
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Resize";
|
|
lp.name = "testLayer";
|
|
lp.set("zoom_factor", 2);
|
|
lp.set("interpolation", "nearest");
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
Mat inp(4 + i, 5 + i, CV_8UC3), ref;
|
|
randu(inp, 0, 255);
|
|
resize(inp, ref, Size(0, 0), 2, 2, INTER_NEAREST);
|
|
ref = blobFromImage(ref);
|
|
|
|
net.setInput(blobFromImage(inp));
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
normAssert(out, ref);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Resize, dnnBackendsAndTargets());
|
|
|
|
struct Layer_Test_Slice : public testing::TestWithParam<tuple<Backend, Target> >
|
|
{
|
|
template<int DIMS>
|
|
void test_slice(const int* inputShape, const int* begin, const int* end)
|
|
{
|
|
int backendId = get<0>(GetParam());
|
|
int targetId = get<1>(GetParam());
|
|
|
|
Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0));
|
|
for (int i = 0; i < (int)input.total(); ++i)
|
|
input.ptr<float>()[i] = (float)i;
|
|
|
|
std::vector<Range> range(DIMS);
|
|
for (int i = 0; i < DIMS; ++i)
|
|
range[i] = Range(begin[i], end[i]);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Slice";
|
|
lp.name = "testLayer";
|
|
lp.set("begin", DictValue::arrayInt<int*>((int*)&begin[0], DIMS));
|
|
lp.set("end", DictValue::arrayInt<int*>((int*)&end[0], DIMS));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
{
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
|
|
EXPECT_GT(cv::norm(out, NORM_INF), 0);
|
|
normAssert(out, input(range));
|
|
#if 0
|
|
cout << input(range).clone().reshape(1, 1) << endl;
|
|
cout << out.reshape(1, 1) << endl;
|
|
#endif
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST_P(Layer_Test_Slice, slice_channels_17762)
|
|
{
|
|
const int inputShape[4] = {1, 16, 6, 8};
|
|
const int begin[] = {0, 4, 0, 0};
|
|
const int end[] = {1, 8, 6, 8};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_channels_with_batch_17762)
|
|
{
|
|
const int inputShape[4] = {4, 4, 3, 4};
|
|
const int begin[] = {0, 1, 0, 0};
|
|
const int end[] = {4, 3, 3, 4};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_channels_and_batch_17762)
|
|
{
|
|
int backend = get<0>(GetParam());
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
|
|
|
const int inputShape[4] = {4, 4, 3, 4};
|
|
const int begin[] = {2, 1, 0, 0};
|
|
const int end[] = {4, 3, 3, 4};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_rows)
|
|
{
|
|
const int inputShape[4] = {1, 2, 6, 4};
|
|
const int begin[] = {0, 0, 4, 0};
|
|
const int end[] = {1, 2, 6, 4};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_cols)
|
|
{
|
|
const int inputShape[4] = {1, 2, 3, 8};
|
|
const int begin[] = {0, 0, 0, 4};
|
|
const int end[] = {1, 2, 3, 8};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
|
|
TEST_P(Layer_Test_Slice, slice_complex_1_unaligned)
|
|
{
|
|
const int inputShape[4] = {1, 4, 2, 3};
|
|
const int begin[] = {0, 2, 1, 0};
|
|
const int end[] = {1, 3, 2, 2};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_complex_2_x4)
|
|
{
|
|
const int inputShape[4] = {1, 3, 2, 4};
|
|
const int begin[] = {0, 2, 1, 0};
|
|
const int end[] = {1, 3, 2, 2};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, slice_complex_3)
|
|
{
|
|
const int inputShape[4] = {1, 6, 4, 8};
|
|
const int begin[] = {0, 2, 1, 4};
|
|
const int end[] = {1, 4, 3, 8};
|
|
test_slice<4>(inputShape, begin, end);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Slice, variable_input_shape)
|
|
{
|
|
int backendId = get<0>(GetParam());
|
|
int targetId = get<1>(GetParam());
|
|
|
|
int begin[] = {0, 0, 0, 0};
|
|
int end[] = {INT_MAX, INT_MAX, INT_MAX, INT_MAX};
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Slice";
|
|
lp.name = "testLayer";
|
|
lp.set("begin", DictValue::arrayInt<int*>(&begin[0], 4));
|
|
lp.set("end", DictValue::arrayInt<int*>(&end[0], 4));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
Mat inp(4 + i, 5 + i, CV_8UC1);
|
|
randu(inp, 0, 255);
|
|
inp = blobFromImage(inp);
|
|
|
|
net.setInput(inp);
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
|
|
normAssert(out, inp);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Slice, dnnBackendsAndTargets());
|
|
|
|
typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_BatchNorm;
|
|
TEST_P(Layer_Test_BatchNorm, fusion)
|
|
{
|
|
// This tests reinitializes network by forwarding different batch size input.
|
|
// We check BatchNorm layer weights restoring after fusion.
|
|
int backendId = get<0>(GetParam());
|
|
int targetId = get<1>(GetParam());
|
|
const int ch = 4;
|
|
|
|
Mat mean(1, ch, CV_32F), var(1, ch, CV_32F), weights(1, ch, CV_32F);
|
|
randu(mean, 0, 1);
|
|
randu(var, 0, 1);
|
|
randu(weights, 0, 1);
|
|
|
|
Net net;
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "BatchNorm";
|
|
lp.name = "bn";
|
|
lp.set("has_weight", false);
|
|
lp.set("has_bias", false);
|
|
lp.blobs.push_back(mean);
|
|
lp.blobs.push_back(var);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "Scale";
|
|
lp.name = "scale";
|
|
lp.set("has_bias", false);
|
|
lp.blobs.push_back(weights);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
}
|
|
|
|
Mat inp(4, 5, CV_32FC(ch));
|
|
randu(inp, 0, 1);
|
|
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
|
|
net.setInput(blobFromImage(inp));
|
|
Mat ref = net.forward();
|
|
|
|
net.setInput(blobFromImages(std::vector<Mat>(2, inp)));
|
|
Mat out = net.forward();
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
std::vector<Range> ranges(4, Range::all());
|
|
ranges[0].start = i;
|
|
ranges[0].end = i + 1;
|
|
normAssert(out(ranges), ref);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_BatchNorm, dnnBackendsAndTargets());
|
|
|
|
class TestLayerFusion : public DNNTestLayer {
|
|
public:
|
|
static void makeDefaultTestConvolutionLayer(LayerParams& convParams, int in_channels, int num_filters, bool bias_term)
|
|
{
|
|
const int kernel_h = 3, kernel_w = 3;
|
|
const int pad_h = kernel_h / 2, pad_w = kernel_w / 2;
|
|
|
|
convParams.set("kernel_h", kernel_h);
|
|
convParams.set("kernel_w", kernel_w);
|
|
convParams.set("pad_h", pad_h);
|
|
convParams.set("pad_w", pad_w);
|
|
convParams.set("num_output", num_filters);
|
|
convParams.set("bias_term", bias_term);
|
|
convParams.type = "Convolution";
|
|
convParams.name = "convolution";
|
|
|
|
float conv_init_magnitude = 1.0f / in_channels / kernel_h / kernel_w;
|
|
int weightsShape[] = {num_filters, in_channels, kernel_h, kernel_w};
|
|
Mat weights(4, &weightsShape[0], CV_32F);
|
|
randu(weights, -conv_init_magnitude, conv_init_magnitude);
|
|
convParams.blobs.push_back(weights);
|
|
if (bias_term)
|
|
{
|
|
Mat bias(1, num_filters, CV_32F);
|
|
randu(bias, -1.0f, 1.0f);
|
|
convParams.blobs.push_back(bias);
|
|
}
|
|
}
|
|
|
|
static void makeDefaultTestActivationLayer(LayerParams& activationParams, const std::string& type, int in_channels)
|
|
{
|
|
activationParams.type = type;
|
|
activationParams.name = "activation";
|
|
if (activationParams.type == "ReLU")
|
|
activationParams.set("negative_slope", 0.1f);
|
|
else if (activationParams.type == "Power")
|
|
{
|
|
activationParams.set("power", 2.0f);
|
|
activationParams.set("scale", 0.5f);
|
|
activationParams.set("shift", 0.3f);
|
|
}
|
|
else if (activationParams.type == "ReLU6")
|
|
{
|
|
activationParams.set("min_value", -1.0f);
|
|
activationParams.set("max_value", 1.0f);
|
|
}
|
|
else if (activationParams.type == "ChannelsPReLU")
|
|
{
|
|
Mat scales(1, in_channels, CV_32F);
|
|
randu(scales, -1.0f, 1.0f);
|
|
activationParams.blobs.push_back(scales);
|
|
}
|
|
else if (activationParams.type == "Exp")
|
|
{
|
|
activationParams.set("base", -1.0f);
|
|
activationParams.set("scale", 0.3f);
|
|
activationParams.set("shift", 0.6f);
|
|
}
|
|
else if (activationParams.type == "ELU")
|
|
{
|
|
activationParams.set("alpha", 1.0f);
|
|
}
|
|
else if (activationParams.type == "HardSigmoid")
|
|
{
|
|
activationParams.set("alpha", 0.2f);
|
|
activationParams.set("beta", 0.5f);
|
|
}
|
|
}
|
|
|
|
static void makeDefaultTestEltwiseLayer(LayerParams& eltwiseParams, const std::string& op, bool withCoefficients)
|
|
{
|
|
eltwiseParams.type = "Eltwise";
|
|
eltwiseParams.name = "eltwise";
|
|
eltwiseParams.set("operation", op);
|
|
if (withCoefficients)
|
|
{
|
|
float coeff[] = {0.3f, 0.5f};
|
|
eltwiseParams.set("coeff", DictValue::arrayReal<float*>(coeff, 2));
|
|
}
|
|
}
|
|
|
|
static void test(Mat& input, Net& net, Backend backendId, Target targetId, std::vector<int> expectedFusedLayers = std::vector<int>(), double l1 = 0.0, double lInf = 0.0)
|
|
{
|
|
DNNTestLayer::checkBackend(backendId, targetId);
|
|
|
|
net.enableFusion(false);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
net.setPreferableTarget(DNN_TARGET_CPU);
|
|
net.setInput(input);
|
|
Mat outputReference = net.forward().clone();
|
|
std::vector<double> refTimings;
|
|
net.getPerfProfile(refTimings);
|
|
for (int i = 0; i < refTimings.size(); i++)
|
|
{
|
|
CV_Assert(refTimings[i] != 0.0);
|
|
}
|
|
|
|
net.enableFusion(true);
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
net.setInput(input);
|
|
Mat outputTest = net.forward().clone();
|
|
std::vector<double> testTimings;
|
|
net.getPerfProfile(testTimings);
|
|
for (int i = 0; i < testTimings.size(); i++)
|
|
{
|
|
if(std::find(expectedFusedLayers.begin(), expectedFusedLayers.end(), i + 1) != expectedFusedLayers.end())
|
|
{
|
|
EXPECT_EQ(testTimings[i], 0.0);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_NE(testTimings[i], 0.0);
|
|
}
|
|
}
|
|
|
|
// double ref_max_value, ref_min_value;
|
|
// minMaxLoc(outputReference.reshape(1, 1), &ref_min_value, &ref_max_value);
|
|
// std::cout << "reference range: " << ref_min_value << ' ' << ref_max_value << std::endl;
|
|
|
|
double default_l1, default_lInf;
|
|
DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf);
|
|
if (l1 == 0.0)
|
|
l1 = default_l1;
|
|
if (lInf == 0.0)
|
|
lInf = default_lInf;
|
|
normAssert(outputReference, outputTest, "", l1, lInf);
|
|
}
|
|
|
|
static testing::internal::ParamGenerator<std::string> eltwiseOpList()
|
|
{
|
|
// TODO: automate list generation
|
|
return Values("sum", "max", "min", "prod", "div");
|
|
}
|
|
|
|
static testing::internal::ParamGenerator<std::string> activationLayersList()
|
|
{
|
|
// TODO: automate list generation
|
|
return Values("ReLU", "ReLU6", "ChannelsPReLU", "TanH", "Swish", "Mish", "Sigmoid", "ELU",
|
|
"AbsVal", "BNLL", "Power", "Exp", "HardSwish", "HardSigmoid", "Gelu", "GeluApproximation");
|
|
}
|
|
|
|
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsForFusionTests()
|
|
{
|
|
return dnnBackendsAndTargets(/* withInferenceEngine = */ false,
|
|
/* obsolete_withHalide = */ false,
|
|
/* withCpuOCV = */ true,
|
|
/* withVkCom = */ false,
|
|
/* withCUDA = */ true,
|
|
/* withNgraph = */false); // OCV OpenCL + OCV CPU + CUDA
|
|
}
|
|
};
|
|
|
|
typedef TestWithParam<tuple<bool, std::string, tuple<Backend, Target> > > ConvolutionActivationFusion;
|
|
TEST_P(ConvolutionActivationFusion, Accuracy)
|
|
{
|
|
// input
|
|
// |
|
|
// -----------------------
|
|
// | convolution |
|
|
// -----------------------
|
|
// |
|
|
// -----------------------
|
|
// | activation |
|
|
// -----------------------
|
|
// |
|
|
// output
|
|
|
|
const int batch_size = 2, in_channels = 16;
|
|
const int in_height = 16, in_width = 16;
|
|
int inputShape[] = {batch_size, in_channels, in_height, in_width};
|
|
Mat input(4, &inputShape[0], CV_32F);
|
|
randu(input, 1.0f, 2.0f);
|
|
|
|
bool bias_term = get<0>(GetParam());
|
|
LayerParams convParams;
|
|
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
|
|
|
|
std::string actType = get<1>(GetParam());
|
|
LayerParams activationParams;
|
|
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
|
|
|
|
Backend backendId = get<0>(get<2>(GetParam()));
|
|
Target targetId = get<1>(get<2>(GetParam()));
|
|
|
|
Net net;
|
|
int convId = net.addLayer(convParams.name, convParams.type, convParams);
|
|
int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);
|
|
net.connect(0, 0, convId, 0);
|
|
|
|
std::vector<int> expectedFusedLayers;
|
|
if (backendId == DNN_BACKEND_OPENCV)
|
|
{
|
|
if (targetId == DNN_TARGET_CPU || targetId == DNN_TARGET_CPU_FP16)
|
|
expectedFusedLayers.push_back(activId); // all activations are fused
|
|
else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
|
|
expectedFusedLayers.push_back(activId);
|
|
}
|
|
}
|
|
else if (backendId == DNN_BACKEND_CUDA)
|
|
{
|
|
if (actType == "ReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Swish" ||
|
|
actType == "Mish" || actType == "Sigmoid" || actType == "Power")
|
|
expectedFusedLayers.push_back(activId);
|
|
}
|
|
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationFusion, Combine(
|
|
/* bias */ testing::Bool(),
|
|
/* activation */ TestLayerFusion::activationLayersList(),
|
|
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
|
|
));
|
|
|
|
typedef TestWithParam<tuple<bool, std::string, bool, tuple<Backend, Target> > > ConvolutionEltwiseFusion;
|
|
TEST_P(ConvolutionEltwiseFusion, Accuracy)
|
|
{
|
|
// input
|
|
// |
|
|
// -------------------------------
|
|
// | |
|
|
// | ---------------
|
|
// | | convolution |
|
|
// | ---------------
|
|
// | |
|
|
// | ---------------- |
|
|
// --------| eltwise op |-------
|
|
// ----------------
|
|
// |
|
|
// output
|
|
|
|
const int batch_size = 2, in_channels = 16;
|
|
const int in_height = 16, in_width = 16;
|
|
int inputShape[] = {batch_size, in_channels, in_height, in_width};
|
|
Mat input(4, &inputShape[0], CV_32F);
|
|
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
|
|
|
|
bool bias_term = get<0>(GetParam());
|
|
LayerParams convParams;
|
|
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
|
|
|
|
std::string eltwiseOp = get<1>(GetParam());
|
|
bool weightedEltwise = get<2>(GetParam());
|
|
if (eltwiseOp != "sum" && weightedEltwise)
|
|
throw SkipTestException("weighted eltwise not supported");
|
|
LayerParams eltwiseParams;
|
|
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, weightedEltwise);
|
|
|
|
Net net;
|
|
int convId = net.addLayer(convParams.name, convParams.type, convParams);
|
|
int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
|
|
net.connect(0, 0, convId, 0);
|
|
net.connect(convId, 0, eltwiseId, 0);
|
|
net.connect(0, 0, eltwiseId, 1);
|
|
|
|
Backend backendId = get<0>(get<3>(GetParam()));
|
|
Target targetId = get<1>(get<3>(GetParam()));
|
|
|
|
std::vector<int> expectedFusedLayers;
|
|
if (backendId == DNN_BACKEND_CUDA && eltwiseOp == "sum" && !weightedEltwise)
|
|
expectedFusedLayers.push_back(eltwiseId);
|
|
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseFusion, Combine(
|
|
/* bias */ testing::Bool(),
|
|
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
|
|
/* eltwise weighted */ testing::Bool(),
|
|
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
|
|
));
|
|
|
|
typedef TestWithParam<tuple<bool, std::string, bool, std::string, tuple<Backend, Target> > > ConvolutionEltwiseActivationFusion;
|
|
TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
|
|
{
|
|
// input
|
|
// |
|
|
// -------------------------------
|
|
// | |
|
|
// | ---------------
|
|
// | | convolution |
|
|
// | ---------------
|
|
// | |
|
|
// | ---------------- |
|
|
// --------| eltwise op |-------
|
|
// ----------------
|
|
// |
|
|
// ----------------
|
|
// | activation |
|
|
// ----------------
|
|
// |
|
|
// output
|
|
|
|
const int batch_size = 2, in_channels = 16;
|
|
const int in_height = 16, in_width = 16;
|
|
int inputShape[] = {batch_size, in_channels, in_height, in_width};
|
|
Mat input(4, &inputShape[0], CV_32F);
|
|
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
|
|
|
|
bool bias_term = get<0>(GetParam());
|
|
LayerParams convParams;
|
|
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
|
|
|
|
std::string eltwiseOp = get<1>(GetParam());
|
|
bool weightedEltwise = get<2>(GetParam());
|
|
if (eltwiseOp != "sum" && weightedEltwise)
|
|
throw SkipTestException("weighted eltwise not supported");
|
|
LayerParams eltwiseParams;
|
|
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, weightedEltwise);
|
|
|
|
std::string actType = get<3>(GetParam());
|
|
LayerParams activationParams;
|
|
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
|
|
|
|
Backend backendId = get<0>(get<4>(GetParam()));
|
|
Target targetId = get<1>(get<4>(GetParam()));
|
|
|
|
Net net;
|
|
int convId = net.addLayer(convParams.name, convParams.type, convParams);
|
|
int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
|
|
int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
|
|
net.connect(0, 0, convId, 0);
|
|
net.connect(convId, 0, eltwiseId, 0);
|
|
net.connect(0, 0, eltwiseId, 1);
|
|
net.connect(eltwiseId, 0, activId, 0);
|
|
|
|
std::vector<int> expectedFusedLayers;
|
|
if (backendId == DNN_BACKEND_OPENCV)
|
|
{
|
|
if (targetId == DNN_TARGET_CPU || targetId == DNN_TARGET_CPU_FP16)
|
|
expectedFusedLayers.push_back(activId); // activation is fused with eltwise layer
|
|
else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
if (eltwiseOp == "sum" && !weightedEltwise &&
|
|
(actType == "ReLU" || actType == "ChannelsPReLU" /*|| actType == "Power"*/)
|
|
)
|
|
{
|
|
expectedFusedLayers.push_back(eltwiseId);
|
|
expectedFusedLayers.push_back(activId);
|
|
}
|
|
}
|
|
}
|
|
else if(backendId == DNN_BACKEND_CUDA)
|
|
{
|
|
if (eltwiseOp == "sum" && !weightedEltwise)
|
|
{
|
|
expectedFusedLayers.push_back(eltwiseId);
|
|
if (actType == "ReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Swish" ||
|
|
actType == "Mish" || actType == "Sigmoid" || actType == "Power")
|
|
expectedFusedLayers.push_back(activId);
|
|
}
|
|
}
|
|
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseActivationFusion, Combine(
|
|
/* bias */ testing::Bool(),
|
|
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
|
|
/* eltwise weighted */ testing::Bool(),
|
|
/* activation */ TestLayerFusion::activationLayersList(),
|
|
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
|
|
));
|
|
|
|
typedef TestWithParam<tuple<bool, std::string, std::string, bool, tuple<Backend, Target> > > ConvolutionActivationEltwiseFusion;
|
|
TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
|
|
{
|
|
// input
|
|
// |
|
|
// -------------------------------
|
|
// | |
|
|
// | ----------------
|
|
// | | convolution |
|
|
// | ----------------
|
|
// | |
|
|
// | ----------------
|
|
// | | activation |
|
|
// | ----------------
|
|
// | |
|
|
// | ---------------- |
|
|
// --------| eltwise sum |-------
|
|
// ----------------
|
|
// |
|
|
|
|
const int batch_size = 2, in_channels = 16;
|
|
const int in_height = 16, in_width = 16;
|
|
int inputShape[] = {batch_size, in_channels, in_height, in_width};
|
|
Mat input(4, &inputShape[0], CV_32F);
|
|
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
|
|
|
|
bool bias_term = get<0>(GetParam());
|
|
LayerParams convParams;
|
|
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
|
|
|
|
std::string actType = get<1>(GetParam());
|
|
LayerParams activationParams;
|
|
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
|
|
|
|
std::string eltwiseOp = get<2>(GetParam());
|
|
bool weightedEltwise = get<3>(GetParam());
|
|
if (eltwiseOp != "sum" && weightedEltwise)
|
|
throw SkipTestException("weighted eltwise not supported");
|
|
LayerParams eltwiseParams;
|
|
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, weightedEltwise);
|
|
|
|
Backend backendId = get<0>(get<4>(GetParam()));
|
|
Target targetId = get<1>(get<4>(GetParam()));
|
|
|
|
Net net;
|
|
int convId = net.addLayer(convParams.name, convParams.type, convParams);
|
|
int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
|
|
int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
|
|
net.connect(0, 0, convId, 0);
|
|
net.connect(convId, 0, activId, 0);
|
|
net.connect(activId, 0, eltwiseId, 0);
|
|
net.connect(0, 0, eltwiseId, 1);
|
|
|
|
std::vector<int> expectedFusedLayers;
|
|
if (backendId == DNN_BACKEND_OPENCV)
|
|
{
|
|
if (targetId == DNN_TARGET_CPU || targetId == DNN_TARGET_CPU_FP16)
|
|
expectedFusedLayers.push_back(activId); // activation fused with convolution
|
|
else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
|
|
{
|
|
if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
|
|
expectedFusedLayers.push_back(activId); // activation fused with convolution
|
|
}
|
|
}
|
|
else if(backendId == DNN_BACKEND_CUDA)
|
|
{
|
|
if (actType == "ReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Swish" ||
|
|
actType == "Mish" || actType == "Sigmoid" || actType == "Power")
|
|
{
|
|
expectedFusedLayers.push_back(activId);
|
|
if (eltwiseOp == "sum" && !weightedEltwise)
|
|
expectedFusedLayers.push_back(eltwiseId);
|
|
}
|
|
}
|
|
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationEltwiseFusion, Combine(
|
|
/* bias */ testing::Bool(),
|
|
/* activation */ TestLayerFusion::activationLayersList(),
|
|
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
|
|
/* eltwise weighted */ testing::Bool(),
|
|
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
|
|
));
|
|
|
|
TEST(Layer_LSTM, repeatedInference)
|
|
{
|
|
std::string onnx_file_path = findDataFile("dnn/onnx/models/onnxscript_lstm.onnx", true);
|
|
|
|
// Test parameters
|
|
const int batch_size = 1;
|
|
const int seq_length = 5;
|
|
const int input_size = 6;
|
|
const int hidden_size = 4;
|
|
const int num_directions = 1;
|
|
|
|
// Create random input tensors
|
|
int x_shape [] = {seq_length, batch_size, input_size};
|
|
int h_0_shape [] = {num_directions, batch_size, hidden_size};
|
|
int c_0_shape [] = {num_directions, batch_size, hidden_size};
|
|
|
|
Mat X(3, x_shape, CV_32F);
|
|
Mat h_0(3, h_0_shape, CV_32F);
|
|
Mat c_0(3, c_0_shape, CV_32F);
|
|
|
|
randu(X, 0, 1);
|
|
randu(h_0, 0, 1);
|
|
randu(c_0, 0, 1);
|
|
|
|
// Load and run ONNX model
|
|
dnn::Net net = dnn::readNet(onnx_file_path);
|
|
std::vector<std::string> outputNames = {"Y", "Y_h", "Y_c"};
|
|
|
|
std::vector<std::vector<Mat>> all_outputs;
|
|
// Run the model twice
|
|
for (int i = 0; i < 2; i++) {
|
|
|
|
net.setInput(X, "X");
|
|
net.setInput(h_0, "initial_h");
|
|
net.setInput(c_0, "initial_c");
|
|
|
|
std::vector<Mat> outputs;
|
|
net.forward(outputs, outputNames);
|
|
// std::cout << "........pass " << (i + 1) << " done........" << std::endl;
|
|
all_outputs.push_back(outputs);
|
|
}
|
|
// convert to assertions
|
|
double diff0 = cv::norm(all_outputs[0][0], all_outputs[1][0], NORM_L1);
|
|
double diff1 = cv::norm(all_outputs[0][1], all_outputs[1][1], NORM_L1);
|
|
double diff2 = cv::norm(all_outputs[0][2], all_outputs[1][2], NORM_L1);
|
|
EXPECT_EQ(diff0, 0.);
|
|
EXPECT_EQ(diff1, 0.);
|
|
EXPECT_EQ(diff2, 0.);
|
|
}
|
|
|
|
TEST(Layer_If, resize)
|
|
{
|
|
// Skip this test when the classic DNN engine is explicitly requested. The
|
|
// "if" layer is supported only by the new engine.
|
|
auto engine_forced = static_cast<cv::dnn::EngineType>(
|
|
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
|
|
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
|
|
{
|
|
// Mark the test as skipped and exit early.
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
|
|
return;
|
|
}
|
|
|
|
const std::string imgname = findDataFile("cv/shared/lena.png", true);
|
|
const std::string modelname = findDataFile("dnn/onnx/models/if_layer.onnx", true);
|
|
|
|
dnn::Net net = dnn::readNetFromONNX(modelname, ENGINE_NEW);
|
|
Mat src = imread(imgname), blob;
|
|
dnn::blobFromImage(src, blob, 1.0, cv::Size(), cv::Scalar(), false, false);
|
|
|
|
for (int f = 0; f <= 1; f++) {
|
|
Mat cond(1, 1, CV_BoolC1, cv::Scalar(f));
|
|
|
|
net.setInput(cond, "cond");
|
|
net.setInput(blob, "image");
|
|
|
|
std::vector<Mat> outs;
|
|
net.forward(outs);
|
|
|
|
std::vector<Mat> images;
|
|
dnn::imagesFromBlob(outs[0], images);
|
|
EXPECT_EQ(images.size(), 1u);
|
|
EXPECT_EQ(images[0].rows*(4 >> f), src.rows);
|
|
EXPECT_EQ(images[0].cols*(4 >> f), src.cols);
|
|
}
|
|
}
|
|
|
|
TEST(Layer_If, subgraph_name_scoping)
|
|
{
|
|
auto engine_forced = static_cast<cv::dnn::EngineType>(
|
|
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
|
|
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
|
|
{
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
|
|
return;
|
|
}
|
|
|
|
const std::string modelname = findDataFile("dnn/onnx/models/subgraph_name_scoping.onnx", true);
|
|
dnn::Net net = dnn::readNetFromONNX(modelname, ENGINE_NEW);
|
|
|
|
int xshape[1] = {2};
|
|
Mat x(1, xshape, CV_32F);
|
|
x.at<float>(0) = 1.f;
|
|
x.at<float>(1) = 2.f;
|
|
|
|
for (int f = 0; f <= 1; f++) {
|
|
Mat cond(1, 1, CV_BoolC1, cv::Scalar(f));
|
|
|
|
net.setInput(cond, "cond");
|
|
net.setInput(x.clone(), "x");
|
|
|
|
std::vector<Mat> outs;
|
|
net.forward(outs, std::vector<String>{"sum_outer", "branch_val"});
|
|
ASSERT_EQ(outs.size(), 2u);
|
|
|
|
// sum_outer = x + outer "shared" ([10, 20]).
|
|
const float* sumP = outs[0].ptr<float>();
|
|
EXPECT_FLOAT_EQ(sumP[0], 11.f);
|
|
EXPECT_FLOAT_EQ(sumP[1], 22.f);
|
|
|
|
// branch_val is the body's locally-scoped "shared": [1, 2] or [100, 200].
|
|
const float* brP = outs[1].ptr<float>();
|
|
if (f) {
|
|
EXPECT_FLOAT_EQ(brP[0], 1.f);
|
|
EXPECT_FLOAT_EQ(brP[1], 2.f);
|
|
} else {
|
|
EXPECT_FLOAT_EQ(brP[0], 100.f);
|
|
EXPECT_FLOAT_EQ(brP[1], 200.f);
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(Layer_Size, onnx_1d)
|
|
{
|
|
auto engine_forced = static_cast<cv::dnn::EngineType>(
|
|
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
|
|
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
|
|
{
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
|
|
return;
|
|
}
|
|
|
|
const std::string modelname = findDataFile("dnn/onnx/models/test_size_1d_model.onnx", true);
|
|
cv::dnn::Net net = cv::dnn::readNetFromONNX(modelname, ENGINE_NEW);
|
|
|
|
int sz1d[1] = {7};
|
|
cv::Mat x(1, sz1d, CV_32F);
|
|
cv::randu(x, 0, 1);
|
|
net.setInput(x);
|
|
|
|
std::vector<cv::Mat> outs;
|
|
net.forward(outs);
|
|
|
|
ASSERT_EQ(outs.size(), 1u);
|
|
EXPECT_EQ(outs[0].total(), (size_t)1);
|
|
EXPECT_EQ(outs[0].type(), CV_64S);
|
|
EXPECT_EQ(outs[0].at<int64_t>(0), static_cast<int64_t>(sz1d[0]));
|
|
}
|
|
|
|
TEST(Layer_Size, onnx_0d_scalar)
|
|
{
|
|
auto engine_forced = static_cast<cv::dnn::EngineType>(
|
|
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
|
|
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
|
|
{
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
|
|
return;
|
|
}
|
|
|
|
const std::string modelname = findDataFile("dnn/onnx/models/test_size_0d_model.onnx", true);
|
|
cv::dnn::Net net = cv::dnn::readNetFromONNX(modelname, ENGINE_NEW);
|
|
|
|
cv::Mat x(1, 1, CV_32F);
|
|
x.at<float>(0, 0) = 3.14f;
|
|
net.setInput(x);
|
|
|
|
std::vector<cv::Mat> outs;
|
|
net.forward(outs);
|
|
|
|
ASSERT_EQ(outs.size(), 1u);
|
|
EXPECT_EQ(outs[0].total(), (size_t)1);
|
|
EXPECT_EQ(outs[0].type(), CV_64S);
|
|
EXPECT_EQ(outs[0].at<int64_t>(0), 1);
|
|
}
|
|
|
|
TEST(ConvolutionWinograd, Accuracy)
|
|
{
|
|
Mat weights({2, 1, 3, 3}, CV_32F);
|
|
randn(weights, 0, 1);
|
|
|
|
// Check convolution can switch between implementations on changed shape.
|
|
auto getNet = [&]() {
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.name = "conv";
|
|
lp.type = "Convolution";
|
|
lp.set("kernel_size", 3);
|
|
lp.set("num_output", 2);
|
|
lp.set("pad", 0);
|
|
lp.set("stride", 1);
|
|
lp.set("bias_term", false);
|
|
|
|
lp.blobs.push_back(weights);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
return net;
|
|
};
|
|
|
|
Mat inpSmall({1, 1, 5, 5}, CV_32F);
|
|
Mat inpLarge({1, 1, 64, 64}, CV_32F);
|
|
randn(inpSmall, 0, 1);
|
|
randn(inpLarge, 0, 1);
|
|
|
|
Net net1 = getNet();
|
|
Net net2 = getNet();
|
|
net1.setInput(inpSmall);
|
|
net2.setInput(inpLarge);
|
|
Mat refSmall = net1.forward();
|
|
Mat refLarge = net2.forward();
|
|
|
|
net1.setInput(inpLarge);
|
|
net2.setInput(inpSmall);
|
|
Mat outLarge = net1.forward();
|
|
Mat outSmall = net2.forward();
|
|
|
|
normAssert(outSmall, refSmall, "Small input after large", 0.0, 0.0);
|
|
normAssert(outLarge, refLarge, "Large input after small", 0.0, 0.0);
|
|
}
|
|
|
|
class TESTKVCache : public testing::TestWithParam<std::string>
|
|
{
|
|
public:
|
|
void testKVCache(const std::string& layout)
|
|
{
|
|
auto engine_forced = static_cast<cv::dnn::EngineType>(
|
|
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
|
|
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
|
|
{
|
|
// Mark the test as skipped and exit early.
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
|
|
return;
|
|
}
|
|
|
|
std::string model_path = "dnn/onnx/models/test_attention_kv_cache_" + layout + ".onnx";
|
|
|
|
Net netWithKVCache = readNetFromONNX(findDataFile(model_path, true), cv::dnn::ENGINE_NEW);
|
|
netWithKVCache.enableKVCache();
|
|
Net netWithoutKVCache = readNetFromONNX(findDataFile(model_path, true), cv::dnn::ENGINE_NEW);
|
|
|
|
int T = 523, Nq = 8, Nkv = 4, D = 256;
|
|
// Keep the prefill larger than one cache page, then exercise generation
|
|
// across the partially filled last page.
|
|
int T_pref = T - 7;
|
|
|
|
std::vector<int> q_sz, k_sz, v_sz;
|
|
if (layout == "3d") {
|
|
q_sz = {1, T, Nq * D};
|
|
k_sz = {1, T, Nkv * D};
|
|
v_sz = {1, T, Nkv * D};
|
|
} else {
|
|
q_sz = {1, Nq, T, D};
|
|
k_sz = {1, Nkv, T, D};
|
|
v_sz = {1, Nkv, T, D};
|
|
}
|
|
|
|
Mat Q_all(q_sz, CV_32F);
|
|
Mat K_all(k_sz, CV_32F);
|
|
Mat V_all(v_sz, CV_32F);
|
|
|
|
cv::randn(Q_all, 0.0, 1.0);
|
|
cv::randn(K_all, 0.0, 1.0);
|
|
cv::randn(V_all, 0.0, 1.0);
|
|
|
|
std::vector<int> mask_sz = {1, Nq, T, T};
|
|
Mat mask(mask_sz, CV_32S, cv::Scalar(0));
|
|
|
|
int* mask_ptr = (int*)mask.data;
|
|
for (int n = 0; n < Nq; n++) {
|
|
for (int i = 0; i < T; i++) {
|
|
for (int j = 0; j < T; j++) {
|
|
int idx = n * T * T +
|
|
i * T + j;
|
|
if (i < T_pref) {
|
|
if (j < T_pref) mask_ptr[idx] = 1;
|
|
} else {
|
|
if (j <= i) mask_ptr[idx] = 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
Mat Y;
|
|
if (layout == "3d") {
|
|
std::vector<int> sz = {1, T, Nq * D};
|
|
Y = Mat(sz, CV_32F);
|
|
} else {
|
|
std::vector<int> sz = {1, Nq, T, D};
|
|
Y = Mat(sz, CV_32F);
|
|
}
|
|
Y.setTo(0);
|
|
|
|
std::vector<Range> ranges_pref;
|
|
if (layout == "3d") {
|
|
ranges_pref = {Range::all(), Range(0, T_pref), Range::all()};
|
|
} else {
|
|
ranges_pref = {Range::all(), Range::all(), Range(0, T_pref), Range::all()};
|
|
}
|
|
|
|
Mat Q_pref = Q_all(ranges_pref);
|
|
Mat K_pref = K_all(ranges_pref);
|
|
Mat V_pref = V_all(ranges_pref);
|
|
|
|
// 1. Prefill
|
|
netWithKVCache.setInput(Q_pref, "Q");
|
|
netWithKVCache.setInput(K_pref, "K");
|
|
netWithKVCache.setInput(V_pref, "V");
|
|
Mat prefillResult = netWithKVCache.forward(); // prefill
|
|
prefillResult.copyTo(Y(ranges_pref));
|
|
// 2. Generate
|
|
for(int t = T_pref; t < T; t++)
|
|
{
|
|
std::vector<Range> ranges_gen;
|
|
if (layout == "3d") {
|
|
ranges_gen = {Range::all(), Range(t, t + 1), Range::all()};
|
|
} else {
|
|
ranges_gen = {Range::all(), Range::all(), Range(t, t + 1), Range::all()};
|
|
}
|
|
|
|
netWithKVCache.setInput(Q_all(ranges_gen), "Q");
|
|
netWithKVCache.setInput(K_all(ranges_gen), "K");
|
|
netWithKVCache.setInput(V_all(ranges_gen), "V");
|
|
|
|
Mat nextToken = netWithKVCache.forward();
|
|
nextToken.copyTo(Y(ranges_gen));
|
|
}
|
|
|
|
// 3. Standard path
|
|
netWithoutKVCache.setInput(Q_all, "Q");
|
|
netWithoutKVCache.setInput(K_all, "K");
|
|
netWithoutKVCache.setInput(V_all, "V");
|
|
netWithoutKVCache.setInput(mask, "Mask");
|
|
|
|
Mat Yref = netWithoutKVCache.forward();
|
|
|
|
std::string msg = "Attention generate " + layout + ": KV vs standard";
|
|
normAssert(Y, Yref, msg.c_str(), 1e-3, 1e-3);
|
|
}
|
|
};
|
|
|
|
TEST_P(TESTKVCache, layouts)
|
|
{
|
|
testKVCache(GetParam());
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(KV_Cache, TESTKVCache, testing::Values("3d", "4d"));
|
|
|
|
|
|
|
|
TEST(Layer_Test_GeluApprox, NoNaN_LargeInput)
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "GeluApproximation";
|
|
lp.name = "test_gelu_approx";
|
|
Ptr<Layer> layer = LayerFactory::createLayerInstance("GeluApproximation", lp);
|
|
ASSERT_TRUE(layer != nullptr);
|
|
|
|
float data[] = {-15.f, -10.f, -7.4f, -1.f, 0.f, 1.f, 5.f, 10.6f, 15.f, 20.f};
|
|
int dims[] = {1, 1, 10};
|
|
Mat inp(3, dims, CV_32F, data);
|
|
std::vector<Mat> inpVec = {inp};
|
|
std::vector<Mat> outVec;
|
|
|
|
runLayer(layer, inpVec, outVec);
|
|
ASSERT_EQ(outVec.size(), (size_t)1);
|
|
|
|
Mat& out = outVec[0];
|
|
for (int i = 0; i < 10; i++) {
|
|
float val = out.ptr<float>()[i];
|
|
EXPECT_FALSE(cvIsNaN(val)) << "NaN at index " << i << " (input=" << data[i] << ")";
|
|
EXPECT_FALSE(cvIsInf(val)) << "Inf at index " << i << " (input=" << data[i] << ")";
|
|
}
|
|
|
|
EXPECT_NEAR(out.ptr<float>()[9], 20.f, 0.01f);
|
|
EXPECT_NEAR(out.ptr<float>()[0], 0.f, 1e-6f);
|
|
EXPECT_NEAR(out.ptr<float>()[4], 0.f, 1e-6f);
|
|
}
|
|
|
|
TEST(Layer_Test_Softmax, NoNaN_AllNegInf)
|
|
{
|
|
LayerParams lp;
|
|
lp.type = "Softmax";
|
|
lp.name = "test_softmax";
|
|
lp.set("axis", 1);
|
|
Ptr<Layer> layer = LayerFactory::createLayerInstance("Softmax", lp);
|
|
ASSERT_TRUE(layer != nullptr);
|
|
|
|
int dims[] = {1, 8};
|
|
Mat inp(2, dims, CV_32F, Scalar(-std::numeric_limits<float>::infinity()));
|
|
std::vector<Mat> inpVec = {inp};
|
|
std::vector<Mat> outVec;
|
|
|
|
runLayer(layer, inpVec, outVec);
|
|
ASSERT_EQ(outVec.size(), (size_t)1);
|
|
|
|
Mat& out = outVec[0];
|
|
for (int i = 0; i < 8; i++) {
|
|
float val = out.ptr<float>()[i];
|
|
EXPECT_FALSE(cvIsNaN(val)) << "NaN at index " << i;
|
|
EXPECT_FALSE(cvIsInf(val)) << "Inf at index " << i;
|
|
EXPECT_EQ(val, 0.f) << "Expected 0 at index " << i;
|
|
}
|
|
}
|
|
|
|
TEST(Test_Gemm, FastGemmBlockedTails)
|
|
{
|
|
struct TestCase
|
|
{
|
|
int M, N, K;
|
|
bool transB;
|
|
};
|
|
const TestCase cases[] = {
|
|
{7, 15, 129, false}, // partial M/N and K tail
|
|
{8, 16, 128, false}, // one full RVV micro-tile
|
|
{9, 17, 65, false}, // full tile plus M/N/K tails
|
|
{31, 33, 129, true} // multiple tiles and transposed B
|
|
};
|
|
|
|
for (const TestCase& tc : cases)
|
|
{
|
|
Mat A(tc.M, tc.K, CV_32F);
|
|
Mat B(tc.transB ? tc.N : tc.K, tc.transB ? tc.K : tc.N, CV_32F);
|
|
randu(A, -1.f, 1.f);
|
|
randu(B, -1.f, 1.f);
|
|
|
|
LayerParams lp;
|
|
lp.type = "Gemm";
|
|
lp.name = "fast_gemm_blocked_tails";
|
|
lp.set("transA", false);
|
|
lp.set("transB", tc.transB);
|
|
lp.set("alpha", 0.75f);
|
|
lp.set("beta", 0.f);
|
|
lp.set("real_ndims_C", 0);
|
|
lp.set("constB", true);
|
|
lp.blobs.push_back(B);
|
|
|
|
Net net;
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
net.setPreferableTarget(DNN_TARGET_CPU);
|
|
net.setInput(A);
|
|
Mat actual = net.forward();
|
|
|
|
Mat expected;
|
|
gemm(A, B, 0.75, noArray(), 0., expected, tc.transB ? GEMM_2_T : 0);
|
|
normAssert(actual, expected, "fastGemm blocked/tail mismatch", 1e-4, 1e-4);
|
|
}
|
|
}
|
|
|
|
TEST(Test_Gemm, FastGemmDynamicTransposeAlphaBeta)
|
|
{
|
|
const int M = 11, N = 19, K = 67;
|
|
const float alpha = 0.75f, beta = -0.25f;
|
|
|
|
for (int flags = 0; flags < 4; flags++)
|
|
{
|
|
const bool transA = (flags & 1) != 0;
|
|
const bool transB = (flags & 2) != 0;
|
|
Mat A(transA ? K : M, transA ? M : K, CV_32F);
|
|
Mat B(transB ? N : K, transB ? K : N, CV_32F);
|
|
Mat C(M, N, CV_32F);
|
|
randu(A, -1.f, 1.f);
|
|
randu(B, -1.f, 1.f);
|
|
randu(C, -1.f, 1.f);
|
|
|
|
LayerParams lp;
|
|
lp.type = "Gemm";
|
|
lp.name = "fast_gemm_dynamic";
|
|
lp.set("transA", transA);
|
|
lp.set("transB", transB);
|
|
lp.set("alpha", alpha);
|
|
lp.set("beta", beta);
|
|
lp.set("have_bias", true);
|
|
lp.set("real_ndims_C", 2);
|
|
|
|
Ptr<Layer> layer = LayerFactory::createLayerInstance(lp.type, lp);
|
|
ASSERT_TRUE(layer);
|
|
std::vector<Mat> inputs = {A, B, C}, outputs;
|
|
runLayer(layer, inputs, outputs);
|
|
ASSERT_EQ(outputs.size(), (size_t)1);
|
|
|
|
Mat expected;
|
|
int gemmFlags = (transA ? GEMM_1_T : 0) | (transB ? GEMM_2_T : 0);
|
|
gemm(A, B, alpha, C, beta, expected, gemmFlags);
|
|
normAssert(outputs[0], expected, "fastGemm dynamic transpose/alpha/beta mismatch", 1e-4, 1e-4);
|
|
}
|
|
}
|
|
|
|
TEST(Test_MatMul, FastGemmBatchDynamicAndPackedBroadcast)
|
|
{
|
|
const int batch = 3, M = 11, N = 19, K = 67;
|
|
Mat A({batch, M, K}, CV_32F);
|
|
Mat dynamicB({batch, N, K}, CV_32F); // transposed B
|
|
Mat packedB(K, N, CV_32F); // shared constant B
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randu(A, -1.f, 1.f);
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randu(dynamicB, -1.f, 1.f);
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randu(packedB, -1.f, 1.f);
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auto reference = [&](const Mat& B, bool transB, bool broadcastB)
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{
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Mat expected({batch, M, N}, CV_32F);
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for (int b = 0; b < batch; b++)
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{
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Mat a2d(M, K, CV_32F, A.ptr<float>(b));
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Mat b2d(transB ? N : K, transB ? K : N, CV_32F,
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broadcastB ? const_cast<float*>(B.ptr<float>()) : const_cast<float*>(B.ptr<float>(b)));
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Mat out2d(M, N, CV_32F, expected.ptr<float>(b));
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gemm(a2d, b2d, 1., noArray(), 0., out2d, transB ? GEMM_2_T : 0);
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}
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return expected;
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};
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LayerParams dynamicParams;
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dynamicParams.type = "MatMul";
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dynamicParams.name = "fast_gemm_batch_dynamic";
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dynamicParams.set("transA", false);
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dynamicParams.set("transB", true);
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Ptr<Layer> dynamicLayer = LayerFactory::createLayerInstance(dynamicParams.type, dynamicParams);
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ASSERT_TRUE(dynamicLayer);
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std::vector<Mat> dynamicInputs = {A, dynamicB}, dynamicOutputs;
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runLayer(dynamicLayer, dynamicInputs, dynamicOutputs);
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ASSERT_EQ(dynamicOutputs.size(), (size_t)1);
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Mat dynamicExpected = reference(dynamicB, true, false);
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normAssert(dynamicOutputs[0], dynamicExpected, "fastGemm dynamic batch mismatch", 1e-4, 1e-4);
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LayerParams packedParams;
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packedParams.type = "MatMul";
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packedParams.name = "fast_gemm_batch_packed";
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packedParams.set("transA", false);
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packedParams.set("transB", false);
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packedParams.blobs.push_back(packedB);
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Ptr<Layer> packedLayer = LayerFactory::createLayerInstance(packedParams.type, packedParams);
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ASSERT_TRUE(packedLayer);
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std::vector<Mat> packedInputs = {A}, packedOutputs;
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runLayer(packedLayer, packedInputs, packedOutputs);
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ASSERT_EQ(packedOutputs.size(), (size_t)1);
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Mat packedExpected = reference(packedB, false, true);
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normAssert(packedOutputs[0], packedExpected, "fastGemm packed broadcast batch mismatch", 1e-4, 1e-4);
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}
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}} // namespace
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