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opencv/modules/dnn/test/test_caffe_importer.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename)
{
return findDataFile(std::string("dnn/") + filename);
}
class Test_Caffe_nets : public DNNTestLayer
{
public:
void testFaster(const std::string& proto, const std::string& model, const Mat& ref,
double scoreDiff = 0.0, double iouDiff = 0.0)
{
checkBackend();
Net net = readNetFromCaffe(findDataFile("dnn/" + proto),
findDataFile("dnn/" + model, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/dog416.png"));
resize(img, img, Size(800, 600));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
net.setInput(blob, "data");
net.setInput(imInfo, "im_info");
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
scoreDiff = scoreDiff ? scoreDiff : default_l1;
iouDiff = iouDiff ? iouDiff : default_lInf;
normAssertDetections(ref, out, ("model name: " + model).c_str(), 0.8, scoreDiff, iouDiff);
}
};
TEST(Test_Caffe, memory_read)
{
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
std::vector<char> dataProto;
readFileContent(proto, dataProto);
std::vector<char> dataModel;
readFileContent(model, dataModel);
Net net = readNetFromCaffe(dataProto.data(), dataProto.size());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ASSERT_FALSE(net.empty());
Net net2 = readNetFromCaffe(dataProto.data(), dataProto.size(),
dataModel.data(), dataModel.size());
ASSERT_FALSE(net2.empty());
}
TEST(Test_Caffe, read_gtsrb)
{
Net net = readNetFromCaffe(_tf("gtsrb.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST(Test_Caffe, read_googlenet)
{
Net net = readNetFromCaffe(_tf("bvlc_googlenet.prototxt"));
ASSERT_FALSE(net.empty());
}
TEST_P(Test_Caffe_nets, Axpy)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String proto = _tf("axpy.prototxt");
Net net = readNetFromCaffe(proto);
checkBackend();
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
int size[] = {1, 2, 3, 4};
int scale_size[] = {1, 2, 1, 1};
Mat scale(4, &scale_size[0], CV_32F);
Mat shift(4, &size[0], CV_32F);
Mat inp(4, &size[0], CV_32F);
randu(scale, -1.0f, 1.0f);
randu(shift, -1.0f, 1.0f);
randu(inp, -1.0f, 1.0f);
net.setInput(scale, "scale");
net.setInput(shift, "shift");
net.setInput(inp, "data");
Mat out = net.forward();
Mat ref(4, &size[0], inp.type());
for (int i = 0; i < inp.size[1]; i++) {
for (int h = 0; h < inp.size[2]; h++) {
for (int w = 0; w < inp.size[3]; w++) {
int idx[] = {0, i, h, w};
int scale_idx[] = {0, i, 0, 0};
ref.at<float>(idx) = inp.at<float>(idx) * scale.at<float>(scale_idx) +
shift.at<float>(idx);
}
}
}
float l1 = 1e-5, lInf = 1e-4;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 2e-4;
lInf = 1e-3;
}
else if(target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0002;
lInf = 0.0007;
}
normAssert(ref, out, "", l1, lInf);
}
typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
Target targetId = get<1>(GetParam());
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
applyTestTag(targetId == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
ASSERT_TRUE(ocl::useOpenCL() || targetId == DNN_TARGET_CPU);
bool readFromMemory = get<0>(GetParam());
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
if (readFromMemory)
{
std::vector<char> dataProto;
readFileContent(proto, dataProto);
std::vector<char> dataModel;
readFileContent(model, dataModel);
net = readNetFromCaffe(dataProto.data(), dataProto.size(),
dataModel.data(), dataModel.size());
}
else
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
// Test input layer size
std::vector<MatShape> inLayerShapes;
std::vector<MatShape> outLayerShapes;
net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes);
ASSERT_FALSE(inLayerShapes.empty());
ASSERT_EQ(inLayerShapes[0].size(), 4);
ASSERT_EQ(inLayerShapes[0][0], 1);
ASSERT_EQ(inLayerShapes[0][1], 3);
ASSERT_EQ(inLayerShapes[0][2], 227);
ASSERT_EQ(inLayerShapes[0][3], 227);
const float l1 = 1e-5;
const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-3 : 1e-4;
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
normAssert(ref, out, "", l1, lInf);
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_AlexNet, Combine(testing::Bool(),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV))));
TEST(Reproducibility_FCN, Accuracy)
{
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG, CV_TEST_TAG_MEMORY_2GB);
Net net;
{
const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt");
const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
std::vector<int> layerIds;
std::vector<size_t> weights, blobs;
net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
Mat out = net.forward("score");
Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
int shape[] = {1, 21, 500, 500};
Mat ref(4, shape, CV_32FC1, refData.data);
normAssert(ref, out);
}
TEST(Reproducibility_SSD, Accuracy)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_LONG);
Net net;
{
const string proto = findDataFile("dnn/ssd_vgg16.prototxt");
const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(_tf("street.png"));
ASSERT_TRUE(!sample.empty());
if (sample.channels() == 4)
cvtColor(sample, sample, COLOR_BGRA2BGR);
Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
net.setInput(in_blob, "data");
Mat out = net.forward("detection_out");
Mat ref = blobFromNPY(_tf("ssd_out.npy"));
normAssertDetections(ref, out, "", FLT_MIN);
}
typedef testing::TestWithParam<tuple<Backend, Target> > Reproducibility_MobileNet_SSD;
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat out = net.forward().clone();
ASSERT_EQ(out.size[2], 100);
float scores_diff = 1e-5, boxes_iou_diff = 1e-4;
if (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD)
{
scores_diff = 1.5e-2;
boxes_iou_diff = 6.3e-2;
}
else if (targetId == DNN_TARGET_CUDA_FP16)
{
scores_diff = 0.015;
boxes_iou_diff = 0.07;
}
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
normAssertDetections(ref, out, "", FLT_MIN, scores_diff, boxes_iou_diff);
// Check that detections aren't preserved.
inp.setTo(0.0f);
net.setInput(inp);
Mat zerosOut = net.forward();
zerosOut = zerosOut.reshape(1, zerosOut.total() / 7);
const int numDetections = zerosOut.rows;
// TODO: fix it
if (targetId != DNN_TARGET_MYRIAD ||
getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = zerosOut.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
}
// There is something wrong with Reshape layer in Myriad plugin.
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
)
{
if (targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_OPENCL_FP16)
return;
}
// Check batching mode.
inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat outBatch = net.forward();
// Output blob has a shape 1x1x2Nx7 where N is a number of detection for
// a single sample in batch. The first numbers of detection vectors are batch id.
// For Inference Engine backend there is -1 delimiter which points the end of detections.
const int numRealDetections = ref.size[2];
EXPECT_EQ(outBatch.size[2], 2 * numDetections);
out = out.reshape(1, numDetections).rowRange(0, numRealDetections);
outBatch = outBatch.reshape(1, 2 * numDetections);
for (int i = 0; i < 2; ++i)
{
Mat pred = outBatch.rowRange(i * numRealDetections, (i + 1) * numRealDetections);
EXPECT_EQ(countNonZero(pred.col(0) != i), 0);
normAssert(pred.colRange(1, 7), out.colRange(1, 7));
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, dnnBackendsAndTargets());
typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)
{
Target targetId = GetParam();
applyTestTag(targetId == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
ASSERT_TRUE(ocl::useOpenCL() || targetId == DNN_TARGET_CPU);
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt"),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 3e-5 : 1e-5;
float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 1e-4;
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
ASSERT_TRUE(!input.empty());
net.setInput(input);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
normAssert(ref, out, "", l1, lInf);
if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
{
UMat out_umat;
net.forward(out_umat);
normAssert(ref, out_umat, "out_umat", l1, lInf);
std::vector<UMat> out_umats;
net.forward(out_umats);
normAssert(ref, out_umats[0], "out_umat_vector", l1, lInf);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)));
typedef testing::TestWithParam<Target> Reproducibility_SqueezeNet_v1_1;
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
int targetId = GetParam();
if(targetId == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false, true);
ASSERT_TRUE(!input.empty());
Mat out;
if (targetId == DNN_TARGET_OPENCL)
{
// Firstly set a wrong input blob and run the model to receive a wrong output.
// Then set a correct input blob to check CPU->GPU synchronization is working well.
net.setInput(input * 2.0f);
out = net.forward();
}
net.setInput(input);
out = net.forward();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_SqueezeNet_v1_1,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)));
TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(findDataFile("dnn/grace_hopper_227.png"));
net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar()));
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy"));
normAssert(ref, out, "", l1, lInf);
}
TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
{
const float l1 = 1e-5;
const float lInf = 3e-3;
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
Net net = readNetFromCaffe(proto, "bvlc_googlenet.caffemodel_fp16");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
std::vector<Mat> inpMats;
inpMats.push_back( imread(_tf("googlenet_0.png")) );
inpMats.push_back( imread(_tf("googlenet_1.png")) );
ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
Mat out = net.forward("prob");
Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
normAssert(out, ref, "", l1, lInf);
}
// https://github.com/richzhang/colorization
TEST_P(Test_Caffe_nets, Colorization)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
checkBackend();
Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
Mat ref = blobFromNPY(_tf("colorization_out.npy"));
Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
net.setInput(inp);
Mat out = net.forward();
// Reference output values are in range [-29.1, 69.5]
double l1 = 4e-4, lInf = 3e-3;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.25;
lInf = 5.3;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) ? 0.5 : 0.25;
lInf = (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) ? 11 : 5.3;
}
else if(target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.21;
lInf = 4.5;
}
normAssert(out, ref, "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_Caffe_nets, DenseNet_121)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
checkBackend();
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string weights = findDataFile("dnn/DenseNet_121.caffemodel", false);
Mat inp = imread(_tf("dog416.png"));
Model model(proto, weights);
model.setInputScale(1.0 / 255).setInputSwapRB(true).setInputCrop(true);
std::vector<Mat> outs;
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
model.setPreferableBackend(backend);
model.setPreferableTarget(target);
model.predict(inp, outs);
// Reference is an array of 1000 values from a range [-6.16, 7.9]
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
l1 = 0.04; lInf = 0.21;
#else
l1 = 0.017; lInf = 0.0795;
#endif
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.11; lInf = 0.5;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.04; lInf = 0.2;
}
normAssert(outs[0], ref, "", l1, lInf);
if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
expectNoFallbacksFromIE(model);
}
TEST(Test_Caffe, multiple_inputs)
{
const string proto = findDataFile("dnn/layers/net_input.prototxt");
Net net = readNetFromCaffe(proto);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat first_image(10, 11, CV_32FC3);
Mat second_image(10, 11, CV_32FC3);
randu(first_image, -1, 1);
randu(second_image, -1, 1);
first_image = blobFromImage(first_image);
second_image = blobFromImage(second_image);
Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());
net.setInput(first_image_blue_green, "old_style_input_blue_green");
net.setInput(first_image_red, "different_name_for_red");
net.setInput(second_image_blue_green, "input_layer_blue_green");
net.setInput(second_image_red, "old_style_input_red");
Mat out = net.forward();
normAssert(out, first_image + second_image);
}
TEST(Test_Caffe, shared_weights)
{
const string proto = findDataFile("dnn/layers/shared_weights.prototxt");
const string model = findDataFile("dnn/layers/shared_weights.caffemodel");
Net net = readNetFromCaffe(proto, model);
Mat input_1 = (Mat_<float>(2, 2) << 0., 2., 4., 6.);
Mat input_2 = (Mat_<float>(2, 2) << 1., 3., 5., 7.);
Mat blob_1 = blobFromImage(input_1);
Mat blob_2 = blobFromImage(input_2);
net.setInput(blob_1, "input_1");
net.setInput(blob_2, "input_2");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sum = net.forward();
EXPECT_EQ(sum.at<float>(0,0), 12.);
EXPECT_EQ(sum.at<float>(0,1), 16.);
}
typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy)
{
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt");
std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4);
}
// False positives bug for large faces: https://github.com/opencv/opencv/issues/15106
TEST_P(opencv_face_detector, issue_15106)
{
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt");
std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("cv/shared/lena.png"));
img = img.rowRange(img.rows / 4, 3 * img.rows / 4).colRange(img.cols / 4, 3 * img.cols / 4);
Mat blob = blobFromImage(img, 1.0, Size(300, 300), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
Mat ref = (Mat_<float>(1, 7) << 0, 1, 0.9149431, 0.30424616, 0.26964942, 0.88733053, 0.99815309);
normAssertDetections(ref, out, "", 0.2, 6e-5, 1e-4);
}
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
Combine(
Values("dnn/opencv_face_detector.caffemodel",
"dnn/opencv_face_detector_fp16.caffemodel"),
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)
)
);
TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB, // utilizes ~1Gb, but huge blobs may not be allocated on 32-bit systems due memory fragmentation
#else
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
#endif
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166);
testFaster("faster_rcnn_vgg16.prototxt", "VGG16_faster_rcnn_final.caffemodel", ref);
}
TEST_P(Test_Caffe_nets, FasterRCNN_zf)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
#endif
CV_TEST_TAG_DEBUG_LONG
);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
0, 12, 0.967198, 138.588, 206.843, 329.766, 553.176);
testFaster("faster_rcnn_zf.prototxt", "ZF_faster_rcnn_final.caffemodel", ref);
}
TEST_P(Test_Caffe_nets, RFCN)
{
applyTestTag(
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
float scoreDiff = default_l1, iouDiff = default_lInf;
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
{
scoreDiff = 4e-3;
iouDiff = 8e-2;
}
if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.0034;
iouDiff = 0.11;
}
static Mat ref = (Mat_<float>(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16);
testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref, scoreDiff, iouDiff);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());
}} // namespace