opencv/modules/dnn/test/test_misc.cpp

1011 lines
32 KiB
C++
Raw Normal View History

// 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 "npy_blob.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);
}
}
2018-01-13 23:17:56 +08:00
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++)
{
2020-02-24 01:05:05 +08:00
EXPECT_EQ(0, cvtest::norm(inputImgs[i], outputImgs[i], NORM_INF))
<< "i=" << i
<< " inputImgs[i]=" << inputImgs[i].size
<< " outputImgs[i]=" << outputImgs[i].size;
}
}
TEST(blobFromImageWithParams_4ch, NHWC_scalar_scale)
{
Mat img(10, 10, CV_8UC4, cv::Scalar(0,1,2,3));
std::vector<double> factorVec = {0.1, 0.2, 0.3, 0.4};
Scalar scalefactor(factorVec[0], factorVec[1], factorVec[2], factorVec[3]);
Image2BlobParams param;
param.scalefactor = scalefactor;
param.datalayout = DNN_LAYOUT_NHWC;
Mat blob = dnn::blobFromImageWithParams(img, param); // [1, 10, 10, 4]
float* blobPtr = blob.ptr<float>(0);
std::vector<float> targetVec = {(float )factorVec[0] * 0, (float )factorVec[1] * 1, (float )factorVec[2] * 2, (float )factorVec[3] * 3}; // Target Value.
for (int hi = 0; hi < 10; hi++)
{
for (int wi = 0; wi < 10; wi++)
{
float* hwPtr = blobPtr + hi * 10 * 4 + wi * 4;
// Check equal
EXPECT_NEAR(hwPtr[0], targetVec[0], 1e-5);
EXPECT_NEAR(hwPtr[1], targetVec[1], 1e-5);
EXPECT_NEAR(hwPtr[2], targetVec[2], 1e-5);
EXPECT_NEAR(hwPtr[3], targetVec[3], 1e-5);
}
}
}
TEST(blobFromImageWithParams_CustomPadding, letter_box)
{
Mat img(40, 20, CV_8UC4, Scalar(0, 1, 2, 3));
// Custom padding value that you have added
Scalar customPaddingValue(5, 6, 7, 8); // Example padding value
Size targetSize(20, 20);
Mat targetImg = img.clone();
cv::copyMakeBorder(
targetImg, targetImg, 0, 0,
targetSize.width / 2,
targetSize.width / 2,
BORDER_CONSTANT,
customPaddingValue);
// Set up Image2BlobParams with your new functionality
Image2BlobParams param;
param.size = targetSize;
param.paddingmode = DNN_PMODE_LETTERBOX;
param.borderValue = customPaddingValue; // Use your new feature here
// Create blob with custom padding
Mat blob = dnn::blobFromImageWithParams(img, param);
// Create target blob for comparison
Mat targetBlob = dnn::blobFromImage(targetImg, 1.0, targetSize);
EXPECT_EQ(0, cvtest::norm(targetBlob, blob, NORM_INF));
}
TEST(blobFromImageWithParams_4ch, letter_box)
{
Mat img(40, 20, CV_8UC4, cv::Scalar(0,1,2,3));
// Construct target mat.
Mat targetCh[4];
// The letterbox will add zero at the left and right of output blob.
// After the letterbox, every row data would have same value showing as valVec.
std::vector<uint8_t> valVec = {0,0,0,0,0, 1,1,1,1,1,1,1,1,1,1, 0,0,0,0,0};
Mat rowM(1, 20, CV_8UC1, valVec.data());
for(int i = 0; i < 4; i++)
{
targetCh[i] = rowM * i;
}
Mat targetImg;
merge(targetCh, 4, targetImg);
Size targeSize(20, 20);
Image2BlobParams param;
param.size = targeSize;
param.paddingmode = DNN_PMODE_LETTERBOX;
Mat blob = dnn::blobFromImageWithParams(img, param);
Mat targetBlob = dnn::blobFromImage(targetImg, 1.0, targeSize); // only convert data from uint8 to float32.
EXPECT_EQ(0, cvtest::norm(targetBlob, blob, NORM_INF));
}
2023-08-08 19:31:32 +08:00
TEST(blobFromImagesWithParams_4ch, multi_image)
{
Mat img(10, 10, CV_8UC4, cv::Scalar(0, 1, 2, 3));
Scalar scalefactor(0.1, 0.2, 0.3, 0.4);
Image2BlobParams param;
param.scalefactor = scalefactor;
param.datalayout = DNN_LAYOUT_NHWC;
Mat blobs = blobFromImagesWithParams(std::vector<Mat> { img, 2*img }, param);
vector<Range> ranges;
ranges.push_back(Range(0, 1));
ranges.push_back(Range(0, blobs.size[1]));
ranges.push_back(Range(0, blobs.size[2]));
ranges.push_back(Range(0, blobs.size[3]));
Mat blob0 = blobs(ranges);
ranges[0] = Range(1, 2);
Mat blob1 = blobs(ranges);
EXPECT_EQ(0, cvtest::norm(2*blob0, blob1, NORM_INF));
}
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());
}
TEST(readNet, do_not_call_setInput) // https://github.com/opencv/opencv/issues/16618
{
// 1. load network
const string proto = findDataFile("dnn/squeezenet_v1.1.prototxt");
const string model = findDataFile("dnn/squeezenet_v1.1.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
// 2. mistake: no inputs are specified through .setInput()
// 3. try inference
Mat res;
EXPECT_THROW(
{
res = net.forward(); // no inputs after loading => should fail
}, cv::Exception);
EXPECT_TRUE(res.empty()) << res.size;
}
2020-10-05 14:23:47 +08:00
TEST(Net, empty_forward_18392)
{
cv::dnn::Net net;
Mat image(Size(512, 512), CV_8UC3, Scalar::all(0));
Mat inputBlob = cv::dnn::blobFromImage(image, 1.0, Size(512, 512), Scalar(0,0,0), true, false);
net.setInput(inputBlob);
EXPECT_ANY_THROW(Mat output = net.forward());
}
#ifdef HAVE_INF_ENGINE
static
void test_readNet_IE_do_not_call_setInput(Backend backendId)
{
const Target targetId = DNN_TARGET_CPU;
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net = readNet(model, proto);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
// 2. mistake: no inputs are specified through .setInput()
// 3. try inference
Mat res;
EXPECT_THROW(
{
res = net.forward(); // no inputs after loading => should fail
}, cv::Exception);
EXPECT_TRUE(res.empty()) << res.size;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
TEST(readNet, do_not_call_setInput_IE_NN_BUILDER_2019)
{
test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
}
#endif
#ifdef HAVE_DNN_NGRAPH
TEST(readNet, do_not_call_setInput_IE_NGRAPH)
{
test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
}
#endif
#endif // HAVE_INF_ENGINE
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));
2019-11-07 02:05:35 +08:00
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();
2018-11-15 04:25:23 +08:00
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;
Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low CUDA backend for the DNN module * stub cuda4dnn design * minor fixes for tests and doxygen * add csl public api directory to module headers * add low-level CSL components * add high-level CSL components * integrate csl::Tensor into backbone code * switch to CPU iff unsupported; otherwise, fail on error * add fully connected layer * add softmax layer * add activation layers * support arbitary rank TensorDescriptor * pass input wrappers to `initCUDA()` * add 1d/2d/3d-convolution * add pooling layer * reorganize and refactor code * fixes for gcc, clang and doxygen; remove cxx14/17 code * add blank_layer * add LRN layer * add rounding modes for pooling layer * split tensor.hpp into tensor.hpp and tensor_ops.hpp * add concat layer * add scale layer * add batch normalization layer * split math.cu into activations.cu and math.hpp * add eltwise layer * add flatten layer * add tensor transform api * add asymmetric padding support for convolution layer * add reshape layer * fix rebase issues * add permute layer * add padding support for concat layer * refactor and reorganize code * add normalize layer * optimize bias addition in scale layer * add prior box layer * fix and optimize normalize layer * add asymmetric padding support for pooling layer * add event API * improve pooling performance for some padding scenarios * avoid over-allocation of compute resources to kernels * improve prior box performance * enable layer fusion * add const layer * add resize layer * add slice layer * add padding layer * add deconvolution layer * fix channelwise ReLU initialization * add vector traits * add vectorized versions of relu, clipped_relu, power * add vectorized concat kernels * improve concat_with_offsets performance * vectorize scale and bias kernels * add support for multi-billion element tensors * vectorize prior box kernels * fix address alignment check * improve bias addition performance of conv/deconv/fc layers * restructure code for supporting multiple targets * add DNN_TARGET_CUDA_FP64 * add DNN_TARGET_FP16 * improve vectorization * add region layer * improve tensor API, add dynamic ranks 1. use ManagedPtr instead of a Tensor in backend wrapper 2. add new methods to tensor classes - size_range: computes the combined size of for a given axis range - tensor span/view can be constructed from a raw pointer and shape 3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time) 4. remove device code from tensor classes (as they are unused) 5. enforce strict conditions on tensor class APIs to improve debugging ability * fix parametric relu activation * add squeeze/unsqueeze tensor API * add reorg layer * optimize permute and enable 2d permute * enable 1d and 2d slice * add split layer * add shuffle channel layer * allow tensors of different ranks in reshape primitive * patch SliceOp to allow Crop Layer * allow extra shape inputs in reshape layer * use `std::move_backward` instead of `std::move` for insert in resizable_static_array * improve workspace management * add spatial LRN * add nms (cpu) to region layer * add max pooling with argmax ( and a fix to limits.hpp) * add max unpooling layer * rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA * update supportBackend to be more rigorous * remove stray include from preventing non-cuda build * include op_cuda.hpp outside condition #if * refactoring, fixes and many optimizations * drop DNN_TARGET_CUDA_FP64 * fix gcc errors * increase max. tensor rank limit to six * add Interp layer * drop custom layers; use BackendNode * vectorize activation kernels * fixes for gcc * remove wrong assertion * fix broken assertion in unpooling primitive * fix build errors in non-CUDA build * completely remove workspace from public API * fix permute layer * enable accuracy and perf. tests for DNN_TARGET_CUDA * add asynchronous forward * vectorize eltwise ops * vectorize fill kernel * fixes for gcc * remove CSL headers from public API * remove csl header source group from cmake * update min. cudnn version in cmake * add numerically stable FP32 log1pexp * refactor code * add FP16 specialization to cudnn based tensor addition * vectorize scale1 and bias1 + minor refactoring * fix doxygen build * fix invalid alignment assertion * clear backend wrappers before allocateLayers * ignore memory lock failures * do not allocate internal blobs * integrate NVTX * add numerically stable half precision log1pexp * fix indentation, following coding style, improve docs * remove accidental modification of IE code * Revert "add asynchronous forward" This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70. * [cmake] throw error for unsupported CC versions * fix rebase issues * add more docs, refactor code, fix bugs * minor refactoring and fixes * resolve warnings/errors from clang * remove haveCUDA() checks from supportBackend() * remove NVTX integration * changes based on review comments * avoid exception when no CUDA device is present * add color code for CUDA in Net::dump
2019-10-21 19:28:00 +08:00
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);
Merge pull request #12703 from wzw-intel:vkcom * dnn: Add a Vulkan based backend This commit adds a new backend "DNN_BACKEND_VKCOM" and a new target "DNN_TARGET_VULKAN". VKCOM means vulkan based computation library. This backend uses Vulkan API and SPIR-V shaders to do the inference computation for layers. The layer types that implemented in DNN_BACKEND_VKCOM include: Conv, Concat, ReLU, LRN, PriorBox, Softmax, MaxPooling, AvePooling, Permute This is just a beginning work for Vulkan in OpenCV DNN, more layer types will be supported and performance tuning is on the way. Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> * dnn/vulkan: Add FindVulkan.cmake to detect Vulkan SDK In order to build dnn with Vulkan support, need installing Vulkan SDK and setting environment variable "VULKAN_SDK" and add "-DWITH_VULKAN=ON" to cmake command. You can download Vulkan SDK from: https://vulkan.lunarg.com/sdk/home#linux For how to install, see https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html https://vulkan.lunarg.com/doc/sdk/latest/windows/getting_started.html https://vulkan.lunarg.com/doc/sdk/latest/mac/getting_started.html respectively for linux, windows and mac. To run the vulkan backend, also need installing mesa driver. On Ubuntu, use this command 'sudo apt-get install mesa-vulkan-drivers' To test, use command '$BUILD_DIR/bin/opencv_test_dnn --gtest_filter=*VkCom*' Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> * dnn/Vulkan: dynamically load Vulkan runtime No compile-time dependency on Vulkan library. If Vulkan runtime is unavailable, fallback to CPU path. Use environment "OPENCL_VULKAN_RUNTIME" to specify path to your own vulkan runtime library. Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> * dnn/Vulkan: Add a python script to compile GLSL shaders to SPIR-V shaders The SPIR-V shaders are in format of text-based 32-bit hexadecimal numbers, and inserted into .cpp files as unsigned int32 array. * dnn/Vulkan: Put Vulkan headers into 3rdparty directory and some other fixes Vulkan header files are copied from https://github.com/KhronosGroup/Vulkan-Docs/tree/master/include/vulkan to 3rdparty/include Fix the Copyright declaration issue. Refine OpenCVDetectVulkan.cmake * dnn/Vulkan: Add vulkan backend tests into existing ones. Also fixed some test failures. - Don't use bool variable as uniform for shader - Fix dispathed group number beyond max issue - Bypass "group > 1" convolution. This should be support in future. * dnn/Vulkan: Fix multiple initialization in one thread.
2018-10-29 22:51:26 +08:00
if (backend == DNN_BACKEND_VKCOM && dtype != CV_32F)
2019-12-02 23:22:10 +08:00
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
2019-10-01 18:31:57 +08:00
static const std::chrono::milliseconds async_timeout(10000);
2019-05-01 19:51:12 +08:00
// 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()));
2020-07-30 23:04:22 +08:00
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
2020-07-30 23:04:22 +08:00
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, 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]);
2019-05-01 19:51:12 +08:00
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()));
2020-07-30 23:04:22 +08:00
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
2020-07-30 23:04:22 +08:00
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, 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.
2019-05-01 19:51:12 +08:00
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)
{
2019-05-01 19:51:12 +08:00
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");
// Exception: Default implementation fallbacks in asynchronous mode
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
2020-02-07 21:40:50 +08:00
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, 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());
2020-07-30 23:04:22 +08:00
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, 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);
}
2019-11-27 22:31:38 +08:00
TEST_P(Test_Model_Optimizer, readFromBuffer)
{
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
2020-07-30 23:04:22 +08:00
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
2019-11-27 22:31:38 +08:00
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
2020-07-30 23:04:22 +08:00
const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml");
2019-11-27 22:31:38 +08:00
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
2019-11-27 22:31:38 +08:00
Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
net1.setPreferableBackend(backendId);
net1.setPreferableTarget(targetId);
std::vector<char> modelConfig;
readFileContent(modelFile, modelConfig);
std::vector<char> weights;
readFileContent(weightsFile, weights);
Net net2 = readNetFromModelOptimizer(
(const uchar*)modelConfig.data(), modelConfig.size(),
(const uchar*)weights.data(), weights.size()
);
net2.setPreferableBackend(backendId);
net2.setPreferableTarget(targetId);
int blobSize[] = {2, 6, 75, 113};
Mat input(4, &blobSize[0], CV_32F);
randu(input, 0, 255);
Mat ref, actual;
{
net1.setInput(input);
ref = net1.forward();
}
{
net2.setInput(input);
actual = net2.forward();
}
normAssert(ref, actual, "", 0, 0);
}
2020-04-19 00:42:48 +08:00
TEST_P(Test_Model_Optimizer, flexible_inputs)
{
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
2020-07-30 23:04:22 +08:00
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
2020-04-19 00:42:48 +08:00
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
2020-04-19 00:42:48 +08:00
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);
Net net1 = readNet(model, proto);
net1.setPreferableTarget(targetId);
// Generate inputs.
int blobSize0[] = {2, 6, 75, 113};
Mat input0(4, &blobSize0[0], CV_32F);
randu(input0, 0, 255);
net0.setInput(input0);
Mat ref = net0.forward().clone();
int blobSize1[] = {1, 6, 10, 9};
Mat input1(4, &blobSize1[0], CV_32F);
randu(input1, 0, 255);
net1.setInput(input1);
Mat out = net1.forward();
EXPECT_NE(out.size, ref.size);
net1.setInput(input0);
out = net1.forward();
normAssert(ref, out, 0, 0);
}
TEST_P(Test_Model_Optimizer, readONNX)
{
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
const std::string& model = findDataFile("dnn/onnx/models/convolution.onnx");
std::vector<Net> nets = {
// Old API
readNetFromModelOptimizer(model, ""),
readNet("", model, "dldt"),
// New API
readNetFromModelOptimizer(model),
readNet(model, "", "openvino")
};
Mat inp = blobFromNPY(findDataFile("dnn/onnx/data/input_convolution.npy"));
Mat ref = blobFromNPY(findDataFile("dnn/onnx/data/output_convolution.npy"));
for (int i = 0; i < nets.size(); ++i) {
nets[i].setPreferableTarget(targetId);
nets[i].setInput(inp);
Mat out = nets[i].forward();
normAssert(out, ref, format("Index: %d", i).c_str());
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
dnnBackendsAndTargetsIE()
);
#endif // HAVE_INF_ENGINE
typedef testing::TestWithParam<tuple<MatDepth, MatDepth, tuple<Backend, Target> > > Test_two_inputs;
TEST_P(Test_two_inputs, basic)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
int type1 = get<0>(GetParam());
int type2 = get<1>(GetParam());
if (backendId == DNN_BACKEND_VKCOM && !(type1 == CV_32F && type2 == CV_32F))
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
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], type1);
Mat secondInp(4, &inpSize[0], type2);
randu(firstInp, 0, 100);
randu(secondInp, 0, 100);
#ifndef CV_CXX11
std::vector<String> input_names;
input_names.push_back("data");
input_names.push_back("second_input");
net.setInputsNames(input_names);
#else
net.setInputsNames({"data", "second_input"});
#endif
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_CUDA_FP16) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_CUDA_FP16) ? 0.3 : 1e-5;
normAssert(out, ref, "", l1, lInf);
if (cvtest::debugLevel > 0 || HasFailure())
{
std::cout << "input1 scale=" << kScale << " input2 scale=" << kScaleInv << std::endl;
std::cout << "input1: " << firstInp.size << " " << firstInp.reshape(1, 1) << std::endl;
std::cout << "input2: " << secondInp.size << " " << secondInp.reshape(1, 1) << std::endl;
std::cout << "ref: " << ref.reshape(1, 1) << std::endl;
std::cout << "out: " << out.reshape(1, 1) << std::endl;
}
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_two_inputs, Combine(
Values(CV_32F, CV_8U),
Values(CV_32F, CV_8U),
dnnBackendsAndTargets()
));
}} // namespace