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Parametric OpenCL deep learning tests

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This commit is contained in:
Dmitry Kurtaev
2018-03-05 18:21:19 +03:00
parent 667f5b655a
commit e1c3237532
4 changed files with 316 additions and 526 deletions
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modules/dnn/test/test_caffe_importer.cpp
+64 -119
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@@ -47,6 +47,21 @@
namespace opencv_test { namespace {
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
static testing::internal::ParamGenerator<DNNTarget> availableBackends()
{
static std::vector<DNNTarget> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(DNN_TARGET_OPENCL);
#endif
}
return testing::ValuesIn(targets);
}
template<typename TString>
static std::string _tf(TString filename)
{
@@ -83,10 +98,10 @@ TEST(Test_Caffe, read_googlenet)
ASSERT_FALSE(net.empty());
}
typedef testing::TestWithParam<bool> Reproducibility_AlexNet;
typedef testing::TestWithParam<tuple<bool, DNNTarget> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
bool readFromMemory = GetParam();
bool readFromMemory = get<0>(GetParam());
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
@@ -106,42 +121,7 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
ASSERT_FALSE(net.empty());
}
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);
}
INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_AlexNet, testing::Bool());
typedef testing::TestWithParam<bool> Reproducibility_OCL_AlexNet;
OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
{
bool readFromMemory = GetParam();
Net net;
{
const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
if (readFromMemory)
{
string dataProto;
ASSERT_TRUE(readFileInMemory(proto, dataProto));
string dataModel;
ASSERT_TRUE(readFileInMemory(model, dataModel));
net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
dataModel.c_str(), dataModel.size());
}
else
net = readNetFromCaffe(proto, model);
ASSERT_FALSE(net.empty());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
net.setPreferableTarget(get<1>(GetParam()));
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
@@ -152,7 +132,7 @@ OCL_TEST_P(Reproducibility_OCL_AlexNet, Accuracy)
normAssert(ref, out);
}
OCL_INSTANTIATE_TEST_CASE_P(Test_Caffe, Reproducibility_OCL_AlexNet, testing::Bool());
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_AlexNet, Combine(testing::Bool(), availableBackends()));
#if !defined(_WIN32) || defined(_WIN64)
TEST(Reproducibility_FCN, Accuracy)
@@ -207,12 +187,15 @@ TEST(Reproducibility_SSD, Accuracy)
normAssert(ref, out);
}
TEST(Reproducibility_MobileNet_SSD, Accuracy)
typedef testing::TestWithParam<DNNTarget> 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);
net.setPreferableTarget(GetParam());
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);
@@ -226,70 +209,39 @@ TEST(Reproducibility_MobileNet_SSD, Accuracy)
inp.setTo(0.0f);
net.setInput(inp);
out = net.forward();
out = out.reshape(1, out.total() / 7);
const int numDetections = out.size[2];
const int numDetections = out.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = out.ptr<float>(0, 0, i)[2];
float confidence = out.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
}
OCL_TEST(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);
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
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);
// Check batching mode.
ref = ref.reshape(1, numDetections);
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 out = net.forward();
Mat outBatch = net.forward();
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
normAssert(ref, out);
// Check that detections aren't preserved.
inp.setTo(0.0f);
net.setInput(inp);
out = net.forward();
const int numDetections = out.size[2];
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
float confidence = out.ptr<float>(0, 0, i)[2];
ASSERT_EQ(confidence, 0);
}
// 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.
outBatch = outBatch.reshape(1, outBatch.total() / 7);
EXPECT_EQ(outBatch.rows, 2 * numDetections);
normAssert(outBatch.rowRange(0, numDetections), ref);
normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7));
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, availableBackends());
TEST(Reproducibility_ResNet50, Accuracy)
typedef testing::TestWithParam<DNNTarget> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
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);
}
OCL_TEST(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
findDataFile("dnn/ResNet-50-model.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
int targetId = GetParam();
net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
ASSERT_TRUE(!input.empty());
@@ -300,52 +252,46 @@ OCL_TEST(Reproducibility_ResNet50, Accuracy)
Mat ref = blobFromNPY(_tf("resnet50_prob.npy"));
normAssert(ref, out);
UMat out_umat;
net.forward(out_umat);
normAssert(ref, out_umat, "out_umat");
if (targetId == DNN_TARGET_OPENCL)
{
UMat out_umat;
net.forward(out_umat);
normAssert(ref, out_umat, "out_umat");
std::vector<UMat> out_umats;
net.forward(out_umats);
normAssert(ref, out_umats[0], "out_umat_vector");
std::vector<UMat> out_umats;
net.forward(out_umats);
normAssert(ref, out_umats[0], "out_umat_vector");
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50, availableBackends());
TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
typedef testing::TestWithParam<DNNTarget> Reproducibility_SqueezeNet_v1_1;
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
ASSERT_TRUE(!input.empty());
net.setInput(input);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("squeezenet_v1.1_prob.npy"));
normAssert(ref, out);
}
OCL_TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
int targetId = GetParam();
net.setPreferableTarget(targetId);
Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
ASSERT_TRUE(!input.empty());
// Firstly set a wrong input blob and run the model to receive a wrong output.
net.setInput(input * 2.0f);
Mat out = net.forward();
// Then set a correct input blob to check CPU->GPU synchronization is working well.
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, availableBackends());
TEST(Reproducibility_AlexNet_fp16, Accuracy)
{
@@ -456,7 +402,6 @@ TEST(Test_Caffe, multiple_inputs)
normAssert(out, first_image + second_image);
}
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy)
{