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1444 lines
46 KiB
C++
1444 lines
46 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 "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 runLayer(Ptr<Layer> layer, std::vector<Mat> &inpBlobs, std::vector<Mat> &outBlobs)
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{
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size_t ninputs = inpBlobs.size();
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std::vector<Mat> inp(ninputs), outp, intp;
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std::vector<MatShape> inputs, outputs, internals;
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for (size_t i = 0; i < ninputs; i++)
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{
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inp[i] = inpBlobs[i].clone();
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inputs.push_back(shape(inp[i]));
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}
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layer->getMemoryShapes(inputs, 0, outputs, internals);
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for (size_t i = 0; i < outputs.size(); i++)
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{
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outp.push_back(Mat(outputs[i], CV_32F));
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}
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for (size_t i = 0; i < internals.size(); i++)
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{
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intp.push_back(Mat(internals[i], CV_32F));
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}
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layer->finalize(inp, outp);
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layer->forward(inp, outp, intp);
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size_t noutputs = outp.size();
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outBlobs.resize(noutputs);
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for (size_t i = 0; i < noutputs; i++)
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outBlobs[i] = outp[i];
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}
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class Test_Caffe_layers : public DNNTestLayer
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{
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public:
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void testLayerUsingCaffeModels(const String& basename, bool useCaffeModel = false,
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bool useCommonInputBlob = true, double l1 = 0.0,
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double lInf = 0.0)
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{
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String prototxt = _tf(basename + ".prototxt");
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String caffemodel = _tf(basename + ".caffemodel");
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String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
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String outfile = _tf(basename + ".npy");
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Mat inp = blobFromNPY(inpfile);
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Mat ref = blobFromNPY(outfile);
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checkBackend(&inp, &ref);
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Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp, "input");
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Mat out = net.forward("output");
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normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
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}
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};
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TEST_P(Test_Caffe_layers, Softmax)
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{
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testLayerUsingCaffeModels("layer_softmax");
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}
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TEST_P(Test_Caffe_layers, LRN)
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{
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testLayerUsingCaffeModels("layer_lrn_spatial");
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testLayerUsingCaffeModels("layer_lrn_channels");
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}
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TEST_P(Test_Caffe_layers, Convolution)
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{
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testLayerUsingCaffeModels("layer_convolution", true);
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}
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TEST_P(Test_Caffe_layers, DeConvolution)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
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throw SkipTestException("Test is disabled for OpenVINO 2018R4");
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#endif
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testLayerUsingCaffeModels("layer_deconvolution", true, false);
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}
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TEST_P(Test_Caffe_layers, InnerProduct)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
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(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
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throw SkipTestException("");
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testLayerUsingCaffeModels("layer_inner_product", true);
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}
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TEST_P(Test_Caffe_layers, Pooling_max)
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{
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testLayerUsingCaffeModels("layer_pooling_max");
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}
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TEST_P(Test_Caffe_layers, Pooling_ave)
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{
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testLayerUsingCaffeModels("layer_pooling_ave");
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}
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TEST_P(Test_Caffe_layers, MVN)
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{
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testLayerUsingCaffeModels("layer_mvn");
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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.size.p, out.size.p + out.dims);
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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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TEST_P(Test_Caffe_layers, BatchNorm)
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{
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testLayerUsingCaffeModels("layer_batch_norm", true);
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testLayerUsingCaffeModels("layer_batch_norm_local_stats", true, false);
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}
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TEST_P(Test_Caffe_layers, ReLU)
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{
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testLayerUsingCaffeModels("layer_relu");
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}
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TEST_P(Test_Caffe_layers, Dropout)
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{
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testLayerUsingCaffeModels("layer_dropout");
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}
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TEST_P(Test_Caffe_layers, Concat)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE > 2018050000
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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#endif
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testLayerUsingCaffeModels("layer_concat");
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testLayerUsingCaffeModels("layer_concat_optim", true, false);
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testLayerUsingCaffeModels("layer_concat_shared_input", true, false);
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}
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TEST_P(Test_Caffe_layers, Fused_Concat)
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{
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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{
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if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16 ||
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(INF_ENGINE_RELEASE < 2018040000 && target == DNN_TARGET_CPU))
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throw SkipTestException("");
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}
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#endif
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checkBackend();
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// Test case
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// input
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// |
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// v
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// some_layer
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// | |
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// v v
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// concat
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Net net;
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int interLayer;
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{
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LayerParams lp;
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lp.type = "AbsVal";
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lp.name = "someLayer";
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interLayer = net.addLayerToPrev(lp.name, lp.type, lp);
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}
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{
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LayerParams lp;
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lp.set("axis", 1);
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lp.type = "Concat";
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lp.name = "testConcat";
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int id = net.addLayer(lp.name, lp.type, lp);
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net.connect(interLayer, 0, id, 0);
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net.connect(interLayer, 0, id, 1);
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}
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int shape[] = {1, 2, 3, 4};
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Mat input(4, shape, CV_32F);
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randu(input, 0.0f, 1.0f); // [0, 1] to make AbsVal an identity transformation.
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net.setInput(input);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat out = net.forward();
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normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input, "", default_l1, default_lInf);
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normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input, "", default_l1, default_lInf);
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}
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TEST_P(Test_Caffe_layers, Eltwise)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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testLayerUsingCaffeModels("layer_eltwise");
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}
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TEST_P(Test_Caffe_layers, PReLU)
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{
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testLayerUsingCaffeModels("layer_prelu", true);
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}
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// TODO: fix an unstable test case
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TEST_P(Test_Caffe_layers, layer_prelu_fc)
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{
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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throw SkipTestException("");
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testLayerUsingCaffeModels("layer_prelu_fc", true, false);
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}
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//template<typename XMat>
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//static void test_Layer_Concat()
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//{
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// Matx21f a(1.f, 1.f), b(2.f, 2.f), c(3.f, 3.f);
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// std::vector<Blob> res(1), src = { Blob(XMat(a)), Blob(XMat(b)), Blob(XMat(c)) };
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// Blob ref(XMat(Matx23f(1.f, 2.f, 3.f, 1.f, 2.f, 3.f)));
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//
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// runLayer(ConcatLayer::create(1), src, res);
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// normAssert(ref, res[0]);
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//}
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//TEST(Layer_Concat, Accuracy)
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//{
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// test_Layer_Concat<Mat>());
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//}
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//OCL_TEST(Layer_Concat, Accuracy)
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//{
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// OCL_ON(test_Layer_Concat<Mat>());
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// );
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//}
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TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat input(6, 12, CV_32F);
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RNG rng(0);
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rng.fill(input, RNG::UNIFORM, -1, 1);
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net.setInput(input, "input");
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Mat output = net.forward("output");
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normAssert(input, output, "", default_l1, default_lInf);
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}
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TEST_P(Test_Caffe_layers, Conv_Elu)
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{
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Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
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ASSERT_FALSE(net.empty());
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Mat inp = blobFromNPY(_tf("layer_elu_in.npy"));
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Mat ref = blobFromNPY(_tf("layer_elu_out.npy"));
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net.setInput(inp, "input");
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat out = net.forward();
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normAssert(ref, out, "", default_l1, default_lInf);
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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;
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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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LayerParams lp;
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lp.blobs.resize(3);
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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.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(3);
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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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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_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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class Layer_RNN_Test : public ::testing::Test
|
|
{
|
|
public:
|
|
int nX, nH, nO, nT, nS;
|
|
Mat Whh, Wxh, bh, Who, bo;
|
|
Ptr<RNNLayer> layer;
|
|
|
|
std::vector<Mat> inputs, outputs;
|
|
|
|
Layer_RNN_Test()
|
|
{
|
|
nT = 3;
|
|
nS = 5;
|
|
nX = 31;
|
|
nH = 64;
|
|
nO = 100;
|
|
|
|
Whh = Mat::ones(nH, nH, CV_32F);
|
|
Wxh = Mat::ones(nH, nX, CV_32F);
|
|
bh = Mat::ones(nH, 1, CV_32F);
|
|
Who = Mat::ones(nO, nH, CV_32F);
|
|
bo = Mat::ones(nO, 1, CV_32F);
|
|
|
|
layer = RNNLayer::create(LayerParams());
|
|
layer->setProduceHiddenOutput(true);
|
|
layer->setWeights(Wxh, bh, Whh, Who, bo);
|
|
}
|
|
};
|
|
|
|
TEST_F(Layer_RNN_Test, get_set_test)
|
|
{
|
|
int sz[] = { nT, nS, 1, nX };
|
|
Mat inp(4, sz, CV_32F);
|
|
randu(inp, -1., 1.);
|
|
inputs.push_back(inp);
|
|
runLayer(layer, inputs, outputs);
|
|
|
|
EXPECT_EQ(outputs.size(), 2u);
|
|
EXPECT_EQ(shape(outputs[0]), shape(nT, nS, nO));
|
|
EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
|
|
}
|
|
|
|
TEST(Layer_Test_ROIPooling, Accuracy)
|
|
{
|
|
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));
|
|
|
|
Mat inp = blobFromNPY(_tf("net_roi_pooling.input.npy"));
|
|
Mat rois = blobFromNPY(_tf("net_roi_pooling.rois.npy"));
|
|
Mat ref = blobFromNPY(_tf("net_roi_pooling.npy"));
|
|
|
|
net.setInput(inp, "input");
|
|
net.setInput(rois, "rois");
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
|
|
Mat out = net.forward();
|
|
|
|
normAssert(out, ref);
|
|
}
|
|
|
|
TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
|
|
{
|
|
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
throw SkipTestException("");
|
|
Net net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
|
|
|
|
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
|
|
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.npy"));
|
|
Mat imInfo = (Mat_<float>(1, 3) << 600, 800, 1.6f);
|
|
|
|
net.setInput(scores, "rpn_cls_prob_reshape");
|
|
net.setInput(deltas, "rpn_bbox_pred");
|
|
net.setInput(imInfo, "im_info");
|
|
|
|
std::vector<Mat> outs;
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.forward(outs, "output");
|
|
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
Mat ref = blobFromNPY(_tf(i == 0 ? "net_faster_rcnn_proposal.out_rois.npy" :
|
|
"net_faster_rcnn_proposal.out_scores.npy"));
|
|
const int numDets = ref.size[0];
|
|
EXPECT_LE(numDets, outs[i].size[0]);
|
|
normAssert(outs[i].rowRange(0, numDets), ref);
|
|
|
|
if (numDets < outs[i].size[0])
|
|
{
|
|
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
|
|
}
|
|
}
|
|
}
|
|
|
|
typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
|
|
TEST_P(Scale_untrainable, Accuracy)
|
|
{
|
|
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.dims, 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.dims, 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)
|
|
));
|
|
|
|
// 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)
|
|
throw SkipTestException("");
|
|
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 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2e-5 : 1e-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.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<Target> Layer_Test_Convolution_DLDT;
|
|
TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
|
|
{
|
|
Target targetId = GetParam();
|
|
|
|
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
|
|
Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
|
|
|
|
Mat inp = blobFromNPY(_tf("blob.npy"));
|
|
|
|
netDefault.setInput(inp);
|
|
netDefault.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
Mat outDefault = netDefault.forward();
|
|
|
|
net.setInput(inp);
|
|
net.setPreferableTarget(targetId);
|
|
|
|
Mat out = net.forward();
|
|
|
|
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.4e-3 : 1e-5;
|
|
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.8e-2 : 1e-4;
|
|
normAssert(outDefault, out, "", l1, lInf);
|
|
|
|
std::vector<int> outLayers = net.getUnconnectedOutLayers();
|
|
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output");
|
|
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Convolution");
|
|
}
|
|
|
|
TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
|
|
{
|
|
Target targetId = GetParam();
|
|
Mat inp = blobFromNPY(_tf("blob.npy"));
|
|
|
|
Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
|
|
randu(inputs[0], 0, 255);
|
|
inputs[0].convertTo(inputs[1], CV_32F);
|
|
|
|
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
|
|
Mat outs[2];
|
|
for (int i = 0; i < 2; ++i)
|
|
{
|
|
Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
|
|
net.setPreferableTarget(targetId);
|
|
net.setInput(inputs[i]);
|
|
outs[i] = net.forward();
|
|
ASSERT_EQ(outs[i].type(), CV_32F);
|
|
}
|
|
if (targetId != DNN_TARGET_MYRIAD)
|
|
normAssert(outs[0], outs[1]);
|
|
}
|
|
|
|
TEST_P(Layer_Test_Convolution_DLDT, multithreading)
|
|
{
|
|
Target targetId = GetParam();
|
|
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
std::string xmlPath = _tf("layer_convolution" + suffix + ".xml");
|
|
std::string binPath = _tf("layer_convolution" + suffix + ".bin");
|
|
Net firstNet = readNet(xmlPath, binPath);
|
|
Net secondNet = readNet(xmlPath, binPath);
|
|
Mat inp = blobFromNPY(_tf("blob.npy"));
|
|
|
|
firstNet.setInput(inp);
|
|
secondNet.setInput(inp);
|
|
firstNet.setPreferableTarget(targetId);
|
|
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,
|
|
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)));
|
|
|
|
// 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());
|
|
|
|
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018040000
|
|
if (secondInpType == CV_8U)
|
|
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
|
|
#endif
|
|
|
|
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
|
|
Net net = readNet(_tf("net_two_inputs" + suffix + ".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)),
|
|
testing::ValuesIn(list_sizes)
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, int, Target> > Test_DLDT_two_inputs;
|
|
TEST_P(Test_DLDT_two_inputs, as_backend)
|
|
{
|
|
static const float kScale = 0.5f;
|
|
static const float kScaleInv = 1.0f / kScale;
|
|
|
|
Target targetId = get<2>(GetParam());
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Eltwise";
|
|
lp.name = "testLayer";
|
|
lp.set("operation", "sum");
|
|
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
|
|
net.connect(0, 1, eltwiseId, 1); // connect to a second input
|
|
|
|
int inpSize[] = {1, 2, 3, 4};
|
|
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
|
|
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
|
|
randu(firstInp, 0, 255);
|
|
randu(secondInp, 0, 255);
|
|
|
|
net.setInputsNames({"data", "second_input"});
|
|
net.setInput(firstInp, "data", kScale);
|
|
net.setInput(secondInp, "second_input", kScaleInv);
|
|
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
|
|
net.setPreferableTarget(targetId);
|
|
Mat out = net.forward();
|
|
|
|
Mat ref;
|
|
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
|
|
// Output values are in range [0, 637.5].
|
|
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
|
|
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
|
|
normAssert(out, ref, "", l1, lInf);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
|
|
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
|
|
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
|
|
));
|
|
|
|
class UnsupportedLayer : public Layer
|
|
{
|
|
public:
|
|
UnsupportedLayer(const LayerParams ¶ms) {}
|
|
|
|
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 {}
|
|
};
|
|
|
|
TEST(Test_DLDT, fused_output)
|
|
{
|
|
static const int kNumChannels = 3;
|
|
CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer);
|
|
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(DNN_BACKEND_INFERENCE_ENGINE);
|
|
net.setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
|
|
ASSERT_NO_THROW(net.forward());
|
|
LayerFactory::unregisterLayer("Unsupported");
|
|
}
|
|
|
|
TEST(Test_DLDT, 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(DNN_BACKEND_INFERENCE_ENGINE);
|
|
nets[i].setInput(Mat({1, 1, 1, 1}, 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);
|
|
}
|
|
#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<std::vector<int> > &inputs,
|
|
const int requiredOutputs,
|
|
std::vector<std::vector<int> > &outputs,
|
|
std::vector<std::vector<int> > &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];
|
|
|
|
std::vector<int> 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_16S)
|
|
{
|
|
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;
|
|
};
|
|
|
|
#ifndef OPENCV_DNN_EXTERNAL_PROTOBUF
|
|
TEST_P(Test_Caffe_layers, Interp)
|
|
#else
|
|
TEST_P(Test_Caffe_layers, DISABLED_Interp) // requires patched protobuf (available in OpenCV source tree only)
|
|
#endif
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
throw SkipTestException("");
|
|
// Test a custom layer.
|
|
CV_DNN_REGISTER_LAYER_CLASS(Interp, CustomInterpLayer);
|
|
try
|
|
{
|
|
testLayerUsingCaffeModels("layer_interp", false, false);
|
|
}
|
|
catch (...)
|
|
{
|
|
LayerFactory::unregisterLayer("Interp");
|
|
throw;
|
|
}
|
|
LayerFactory::unregisterLayer("Interp");
|
|
|
|
// Test an implemented layer.
|
|
testLayerUsingCaffeModels("layer_interp", false, false);
|
|
}
|
|
|
|
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_32F, 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<float>(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;
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TEST_P(Layer_Test_ShuffleChannel, Accuracy)
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{
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Vec4i inpShapeVec = get<0>(GetParam());
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int group = get<1>(GetParam());
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ASSERT_EQ(inpShapeVec[1] % group, 0);
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const int groupSize = inpShapeVec[1] / group;
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int backendId = get<0>(get<2>(GetParam()));
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int targetId = get<1>(get<2>(GetParam()));
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Net net;
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LayerParams lp;
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lp.set("group", group);
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lp.type = "ShuffleChannel";
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lp.name = "testLayer";
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net.addLayerToPrev(lp.name, lp.type, lp);
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const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
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Mat inp(4, inpShape, CV_32F);
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randu(inp, 0, 255);
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net.setInput(inp);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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double l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-2 : 1e-5;
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double lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 7e-2 : 1e-4;
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for (int n = 0; n < inpShapeVec[0]; ++n)
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{
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for (int c = 0; c < inpShapeVec[1]; ++c)
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{
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Mat outChannel = getPlane(out, n, c);
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Mat inpChannel = getPlane(inp, n, groupSize * (c % group) + c / group);
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normAssert(outChannel, inpChannel, "", l1, lInf);
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
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/*input shape*/ Values(Vec4i(1, 6, 5, 7), Vec4i(3, 12, 1, 4)),
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/*group*/ Values(1, 2, 3, 6), dnnBackendsAndTargets(/*with IE*/ false)
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));
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// Check if relu is not fused to convolution if we requested it's output
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TEST(Layer_Test_Convolution, relu_fusion)
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{
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Net net;
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{
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LayerParams lp;
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lp.set("kernel_size", 1);
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lp.set("num_output", 1);
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lp.set("bias_term", false);
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lp.type = "Convolution";
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lp.name = "testConv";
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int weightsShape[] = {1, 1, 1, 1};
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Mat weights(4, &weightsShape[0], CV_32F, Scalar(1));
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lp.blobs.push_back(weights);
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net.addLayerToPrev(lp.name, lp.type, lp);
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}
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{
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LayerParams lp;
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lp.type = "ReLU";
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lp.name = "testReLU";
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net.addLayerToPrev(lp.name, lp.type, lp);
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}
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int sz[] = {1, 1, 2, 3};
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Mat input(4, &sz[0], CV_32F);
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randu(input, -1.0, -0.1);
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net.setInput(input);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat output = net.forward("testConv");
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normAssert(input, output);
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}
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}} // namespace
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