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opencv/modules/dnn/test/test_misc.cpp

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#include <opencv2/core/ocl.hpp>
#include <opencv2/core/opencl/ocl_defs.hpp>
#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
namespace opencv_test { namespace {
TEST(blobFromImage_4ch, Regression)
{
Mat ch[4];
for(int i = 0; i < 4; i++)
ch[i] = Mat::ones(10, 10, CV_8U)*i;
Mat img;
merge(ch, 4, img);
Mat blob = dnn::blobFromImage(img, 1., Size(), Scalar(), false, false);
for(int i = 0; i < 4; i++)
{
ch[i] = Mat(img.rows, img.cols, CV_32F, blob.ptr(0, i));
ASSERT_DOUBLE_EQ(cvtest::norm(ch[i], cv::NORM_INF), i);
}
}
TEST(blobFromImage, allocated)
{
int size[] = {1, 3, 4, 5};
Mat img(size[2], size[3], CV_32FC(size[1]));
Mat blob(4, size, CV_32F);
void* blobData = blob.data;
dnn::blobFromImage(img, blob, 1.0 / 255, Size(), Scalar(), false, false);
ASSERT_EQ(blobData, blob.data);
}
TEST(imagesFromBlob, Regression)
{
int nbOfImages = 8;
std::vector<cv::Mat> inputImgs(nbOfImages);
for (int i = 0; i < nbOfImages; i++)
{
inputImgs[i] = cv::Mat::ones(100, 100, CV_32FC3);
cv::randu(inputImgs[i], cv::Scalar::all(0), cv::Scalar::all(1));
}
cv::Mat blob = cv::dnn::blobFromImages(inputImgs, 1., cv::Size(), cv::Scalar(), false, false);
std::vector<cv::Mat> outputImgs;
cv::dnn::imagesFromBlob(blob, outputImgs);
for (int i = 0; i < nbOfImages; i++)
{
ASSERT_EQ(cv::countNonZero(inputImgs[i] != outputImgs[i]), 0);
}
}
TEST(readNet, Regression)
{
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
findDataFile("dnn/opencv_face_detector.prototxt"));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg"),
findDataFile("dnn/tiny-yolo-voc.weights", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt"),
findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
EXPECT_FALSE(net.empty());
}
typedef testing::TestWithParam<tuple<Backend, Target> > dump;
TEST_P(dump, Regression)
{
const int backend = get<0>(GetParam());
const int target = get<1>(GetParam());
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
ASSERT_EQ(net.getLayerInputs(net.getLayerId("fire2/concat")).size(), 2);
int size[] = {1, 3, 227, 227};
Mat input = cv::Mat::ones(4, size, CV_32F);
net.setInput(input);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
EXPECT_FALSE(net.dump().empty());
net.forward();
EXPECT_FALSE(net.dump().empty());
}
INSTANTIATE_TEST_CASE_P(/**/, dump, dnnBackendsAndTargets());
class FirstCustomLayer CV_FINAL : public Layer
{
public:
FirstCustomLayer(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new FirstCustomLayer(params));
}
void forward(InputArrayOfArrays, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
outputs[0].setTo(1);
}
};
class SecondCustomLayer CV_FINAL : public Layer
{
public:
SecondCustomLayer(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new SecondCustomLayer(params));
}
void forward(InputArrayOfArrays, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
outputs[0].setTo(2);
}
};
TEST(LayerFactory, custom_layers)
{
LayerParams lp;
lp.name = "name";
lp.type = "CustomType";
Mat inp(1, 1, CV_32FC1);
for (int i = 0; i < 3; ++i)
{
if (i == 0) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, FirstCustomLayer); }
else if (i == 1) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, SecondCustomLayer); }
else if (i == 2) { LayerFactory::unregisterLayer("CustomType"); }
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward();
if (i == 0) { EXPECT_EQ(output.at<float>(0), 1); }
else if (i == 1) { EXPECT_EQ(output.at<float>(0), 2); }
else if (i == 2) { EXPECT_EQ(output.at<float>(0), 1); }
}
LayerFactory::unregisterLayer("CustomType");
}
typedef testing::TestWithParam<tuple<float, Vec3f, int, tuple<Backend, Target> > > setInput;
TEST_P(setInput, normalization)
{
const float kScale = get<0>(GetParam());
const Scalar kMean = get<1>(GetParam());
const int dtype = get<2>(GetParam());
const int backend = get<0>(get<3>(GetParam()));
const int target = get<1>(get<3>(GetParam()));
const bool kSwapRB = true;
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 && dtype != CV_32F)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_VKCOM && dtype != CV_32F)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
Mat inp(5, 5, CV_8UC3);
randu(inp, 0, 255);
Mat ref = blobFromImage(inp, kScale, Size(), kMean, kSwapRB, /*crop*/false);
LayerParams lp;
Net net;
net.addLayerToPrev("testLayer", "Identity", lp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat blob = blobFromImage(inp, 1.0, Size(), Scalar(), kSwapRB, /*crop*/false, dtype);
ASSERT_EQ(blob.type(), dtype);
net.setInput(blob, "", kScale, kMean);
Mat out = net.forward();
ASSERT_EQ(out.type(), CV_32F);
normAssert(ref, out, "", 4e-4, 1e-3);
}
INSTANTIATE_TEST_CASE_P(/**/, setInput, Combine(
Values(1.0f, 1.0 / 127.5),
Values(Vec3f(), Vec3f(50, 50, 50), Vec3f(10, 50, 140)),
Values(CV_32F, CV_8U),
dnnBackendsAndTargets()
));
class CustomLayerWithDeprecatedForward CV_FINAL : public Layer
{
public:
CustomLayerWithDeprecatedForward(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new CustomLayerWithDeprecatedForward(params));
}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_Assert_N(inputs[0]->depth() == CV_32F, outputs[0].depth() == CV_32F);
cv::add(*inputs[0], 0.5f, outputs[0]);
}
};
class CustomLayerWithDeprecatedForwardAndFallback CV_FINAL : public Layer
{
public:
CustomLayerWithDeprecatedForwardAndFallback(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new CustomLayerWithDeprecatedForwardAndFallback(params));
}
void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16,
forward_ocl(inputs, outputs, internals));
Layer::forward_fallback(inputs, outputs, internals);
}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_Assert_N(inputs[0]->depth() == CV_32F, outputs[0].depth() == CV_32F);
cv::add(*inputs[0], 0.5f, outputs[0]);
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
if (inputs_arr.depth() != CV_32F)
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_arr.getUMatVector(inputs);
outputs_arr.getUMatVector(outputs);
cv::add(inputs[0], 0.5f, outputs[0]);
return true;
}
#endif
};
typedef testing::TestWithParam<tuple<Backend, Target> > DeprecatedForward;
TEST_P(DeprecatedForward, CustomLayer)
{
const int backend = get<0>(GetParam());
const int target = get<1>(GetParam());
Mat inp(5, 5, CV_32FC1);
randu(inp, -1.0f, 1.0f);
inp = blobFromImage(inp);
CV_DNN_REGISTER_LAYER_CLASS(CustomType, CustomLayerWithDeprecatedForward);
try
{
LayerParams lp;
Net net;
net.addLayerToPrev("testLayer", "CustomType", lp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, inp + 0.5f, "", 2e-4, 7e-4);
}
catch (...)
{
LayerFactory::unregisterLayer("CustomType");
throw;
}
LayerFactory::unregisterLayer("CustomType");
}
TEST_P(DeprecatedForward, CustomLayerWithFallback)
{
const int backend = get<0>(GetParam());
const int target = get<1>(GetParam());
Mat inp(5, 5, CV_32FC1);
randu(inp, -1.0f, 1.0f);
inp = blobFromImage(inp);
CV_DNN_REGISTER_LAYER_CLASS(CustomType, CustomLayerWithDeprecatedForwardAndFallback);
try
{
LayerParams lp;
Net net;
net.addLayerToPrev("testLayer", "CustomType", lp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, inp + 0.5f, "", 2e-4, 7e-4);
}
catch (...)
{
LayerFactory::unregisterLayer("CustomType");
throw;
}
LayerFactory::unregisterLayer("CustomType");
}
INSTANTIATE_TEST_CASE_P(/**/, DeprecatedForward, dnnBackendsAndTargets());
TEST(Net, forwardAndRetrieve)
{
std::string prototxt =
"input: \"data\"\n"
"layer {\n"
" name: \"testLayer\"\n"
" type: \"Slice\"\n"
" bottom: \"data\"\n"
" top: \"firstCopy\"\n"
" top: \"secondCopy\"\n"
" slice_param {\n"
" axis: 0\n"
" slice_point: 2\n"
" }\n"
"}";
Net net = readNetFromCaffe(&prototxt[0], prototxt.size());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat inp(4, 5, CV_32F);
randu(inp, -1, 1);
net.setInput(inp);
std::vector<String> outNames;
outNames.push_back("testLayer");
std::vector<std::vector<Mat> > outBlobs;
net.forward(outBlobs, outNames);
EXPECT_EQ(outBlobs.size(), 1);
EXPECT_EQ(outBlobs[0].size(), 2);
normAssert(outBlobs[0][0], inp.rowRange(0, 2), "first part");
normAssert(outBlobs[0][1], inp.rowRange(2, 4), "second part");
}
#ifdef HAVE_INF_ENGINE
static const std::chrono::milliseconds async_timeout(10000);
// This test runs network in synchronous mode for different inputs and then
// runs the same model asynchronously for the same inputs.
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Async;
TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
{
const int dtype = get<0>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
Net netAsync = readNet(model, proto);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {2, 6, 75, 113};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], dtype);
randu(inputs[i], 0, 255);
}
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
// Run asynchronously. To make test more robust, process inputs in the reversed order.
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
AsyncArray out = netAsync.forwardAsync();
ASSERT_TRUE(out.valid());
Mat result;
EXPECT_TRUE(out.get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
{
const int dtype = get<0>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
Net netAsync = readNet(model, proto);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {2, 6, 75, 113};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], dtype);
randu(inputs[i], 0, 255);
}
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
// Run asynchronously. To make test more robust, process inputs in the reversed order.
std::vector<AsyncArray> outs(numInputs);
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
outs[i] = netAsync.forwardAsync();
}
for (int i = numInputs - 1; i >= 0; --i)
{
ASSERT_TRUE(outs[i].valid());
Mat result;
EXPECT_TRUE(outs[i].get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
TEST_P(Async, create_layer_pipeline_set_and_forward_all)
{
const int dtype = get<0>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync;
Net netAsync;
{
int inChannels = 4;
int outChannels = 12;
int group = 3;
Size inSize(113, 75);
Size kernel(4, 5);
Size stride(2, 3);
Size pad(0, 1);
Size dilation(1, 1);
bool hasBias = true;
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
netSync.addLayerToPrev(lp.name, lp.type, lp);
netAsync.addLayerToPrev(lp.name, lp.type, lp);
}
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {1, 4, 75, 113};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], dtype);
randu(inputs[i], 0, 255);
}
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
// Run asynchronously. To make test more robust, process inputs in the reversed order.
std::vector<AsyncArray> outs(numInputs);
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
outs[i] = netAsync.forwardAsync();
}
for (int i = numInputs - 1; i >= 0; --i)
{
ASSERT_TRUE(outs[i].valid());
Mat result;
EXPECT_TRUE(outs[i].get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Async, Combine(
Values(CV_32F, CV_8U),
dnnBackendsAndTargetsIE()
));
typedef testing::TestWithParam<tuple<Backend, Target> > Test_Model_Optimizer;
TEST_P(Test_Model_Optimizer, forward_two_nets)
{
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);
Net net1 = readNet(model, proto);
net1.setPreferableTarget(targetId);
// Generate inputs.
int blobSize[] = {2, 6, 75, 113};
Mat input(4, &blobSize[0], CV_32F);
randu(input, 0, 255);
net0.setInput(input);
Mat ref0 = net0.forward().clone();
net1.setInput(input);
Mat ref1 = net1.forward();
net0.setInput(input);
Mat ref2 = net0.forward();
normAssert(ref0, ref2, 0, 0);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
dnnBackendsAndTargetsIE()
);
#endif // HAVE_INF_ENGINE
}} // namespace