Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge pull request #11867 from dkurt:dnn_ie_layers

Browse Source
pull/11907/head
Vadim Pisarevsky 7 years ago
parent 3b01777c98 019c2f2115
commit 523b6f32ba
13 changed files with 761 additions and 518 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 4
      modules/dnn/src/dnn.cpp
  2. 16
      modules/dnn/src/layers/convolution_layer.cpp
  3. 4
      modules/dnn/src/layers/eltwise_layer.cpp
  4. 21
      modules/dnn/src/layers/reorg_layer.cpp
  5. 21
      modules/dnn/src/layers/resize_layer.cpp
  6. 16
      modules/dnn/src/layers/slice_layer.cpp
  7. 41
      modules/dnn/test/test_backends.cpp
  8. 200
      modules/dnn/test/test_darknet_importer.cpp
  9. 330
      modules/dnn/test/test_halide_layers.cpp
  10. 244
      modules/dnn/test/test_layers.cpp
  11. 87
      modules/dnn/test/test_precomp.hpp
  12. 294
      modules/dnn/test/test_tf_importer.cpp
  13. 1
      modules/dnn/test/test_torch_importer.cpp

4
modules/dnn/src/dnn.cpp
Unescape View File

@ -2730,9 +2730,9 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
}
else if (targetId == DNN_TARGET_OPENCL)
{
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
if (outW == 1 && outH == 1)
{
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
top.split(c, co, ci, c_split)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.gpu_blocks(tile)
@ -2742,6 +2742,8 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
{
int x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW;
int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH;
// Supported vectorization widths: 2, 3, 4, 8, 16
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : std::min(4, outC);
top.split(x, xo, xi, x_split).split(y, yo, yi, y_split)
.split(c, co, ci, c_split)
.gpu_blocks(xo, yo, co)

16
modules/dnn/src/layers/convolution_layer.cpp
Unescape View File

@ -82,7 +82,21 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return preferableTarget != DNN_TARGET_MYRIAD || type != "Deconvolution" || adjustPad == Size();
{
if (type == "Convolution")
return preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height;
else
{
CV_Assert(type == "Deconvolution");
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
if (group != 1)
return false;
if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
return dilation.width == 1 && dilation.height == 1;
return true;
}
}
else
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}

4
modules/dnn/src/layers/eltwise_layer.cpp
Unescape View File

@ -97,8 +97,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

21
modules/dnn/src/layers/reorg_layer.cpp
Unescape View File

@ -41,9 +41,9 @@
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <iostream>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
@ -85,6 +85,11 @@ public:
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
@ -169,6 +174,20 @@ public:
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ReorgYolo";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["stride"] = format("%d", reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

21
modules/dnn/src/layers/resize_layer.cpp
Unescape View File

@ -192,6 +192,11 @@ public:
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
if (!outWidth && !outHeight)
@ -204,6 +209,22 @@ public:
scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f;
scaleWidth = (outWidth > 1) ? (static_cast<float>(inpWidth - 1) / (outWidth - 1)) : 0.f;
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Interp";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["pad_beg"] = "0";
ieLayer->params["pad_end"] = "0";
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
};
Ptr<Layer> InterpLayer::create(const LayerParams& params)

16
modules/dnn/src/layers/slice_layer.cpp
Unescape View File

@ -266,7 +266,21 @@ public:
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
CV_Assert(sliceRanges.size() == 1);
for (int i = sliceRanges[0].size() - 1; i >= 0; --i)
int from, to, step;
if (preferableTarget == DNN_TARGET_MYRIAD)
{
from = 1;
to = sliceRanges[0].size() + 1;
step = 1;
}
else
{
from = sliceRanges[0].size() - 1;
to = -1;
step = -1;
}
for (int i = from; i != to; i += step)
{
ieLayer->axis.push_back(i);
ieLayer->offset.push_back(sliceRanges[0][i].start);

41
modules/dnn/test/test_backends.cpp
Unescape View File

@ -10,18 +10,9 @@
namespace opencv_test { namespace {
class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
class DNNTestNetwork : public DNNTestLayer
{
public:
dnn::Backend backend;
dnn::Target target;
DNNTestNetwork()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
}
void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer = "",
const std::string& halideScheduler = "",
@ -40,32 +31,10 @@ public:
std::string halideScheduler = "",
double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2)
{
if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
}
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
l1 = l1 == 0.0 ? 4e-3 : l1;
lInf = lInf == 0.0 ? 2e-2 : lInf;
}
else
{
l1 = l1 == 0.0 ? 1e-5 : l1;
lInf = lInf == 0.0 ? 1e-4 : lInf;
}
checkBackend();
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);

200
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -65,76 +65,84 @@ TEST(Test_Darknet, read_yolo_voc)
ASSERT_FALSE(net.empty());
}
// Test object detection network from Darknet framework.
static void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<cv::String>& outNames,
const std::vector<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& refBoxes,
int backendId, int targetId, float scoreDiff = 0.0,
float iouDiff = 0.0, float confThreshold = 0.24)
class Test_Darknet_layers : public DNNTestLayer
{
if (backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL)
public:
void testDarknetLayer(const std::string& name, bool hasWeights = false)
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights", false);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
checkBackend(&inp, &ref);
Net net = readNet(cfg, model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
}
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, outNames);
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
for (int i = 0; i < outs.size(); ++i)
};
class Test_Darknet_nets : public DNNTestLayer
{
public:
// Test object detection network from Darknet framework.
void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<cv::String>& outNames,
const std::vector<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24)
{
Mat& out = outs[i];
for (int j = 0; j < out.rows; ++j)
checkBackend();
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, outNames);
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
for (int i = 0; i < outs.size(); ++i)
{
Mat scores = out.row(j).colRange(5, out.cols);
double confidence;
Point maxLoc;
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
float* detection = out.ptr<float>(j);
double centerX = detection[0];
double centerY = detection[1];
double width = detection[2];
double height = detection[3];
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
width, height));
confidences.push_back(confidence);
classIds.push_back(maxLoc.x);
Mat& out = outs[i];
for (int j = 0; j < out.rows; ++j)
{
Mat scores = out.row(j).colRange(5, out.cols);
double confidence;
Point maxLoc;
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
float* detection = out.ptr<float>(j);
double centerX = detection[0];
double centerY = detection[1];
double width = detection[2];
double height = detection[3];
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
width, height));
confidences.push_back(confidence);
classIds.push_back(maxLoc.x);
}
}
normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
confidences, boxes, "", confThreshold, scoreDiff, iouDiff);
}
normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
confidences, boxes, "", confThreshold, scoreDiff, iouDiff);
}
typedef testing::TestWithParam<tuple<DNNBackend, DNNTarget> > Test_Darknet_nets;
};
TEST_P(Test_Darknet_nets, YoloVoc)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
std::vector<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(3);
@ -143,34 +151,28 @@ TEST_P(Test_Darknet_nets, YoloVoc)
classIds[0] = 6; confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418); // a car
classIds[1] = 1; confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bicycle
classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028); // a dog
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames,
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
classIds, confidences, boxes, scoreDiff, iouDiff);
}
TEST_P(Test_Darknet_nets, TinyYoloVoc)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
std::vector<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(2);
std::vector<float> confidences(2);
std::vector<Rect2d> boxes(2);
classIds[0] = 6; confidences[0] = 0.761967f; boxes[0] = Rect2d(0.579042, 0.159161, 0.31544, 0.160779); // a car
classIds[1] = 11; confidences[1] = 0.780595f; boxes[1] = Rect2d(0.129696, 0.386467, 0.315579, 0.534527); // a dog
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-5;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-5;
testDarknetModel("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights", outNames,
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
classIds, confidences, boxes, scoreDiff, iouDiff);
}
TEST_P(Test_Darknet_nets, YOLOv3)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
std::vector<cv::String> outNames(3);
outNames[0] = "yolo_82";
outNames[1] = "yolo_94";
@ -182,55 +184,41 @@ TEST_P(Test_Darknet_nets, YOLOv3)
classIds[0] = 7; confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994); // a truck
classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327); // a bicycle
classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752); // a dog (COCO)
double scoreDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
testDarknetModel("yolov3.cfg", "yolov3.weights", outNames,
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff);
classIds, confidences, boxes, scoreDiff, iouDiff);
}
const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, testing::ValuesIn(testCases));
TEST_P(Test_Darknet_layers, shortcut)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
throw SkipTestException("");
testDarknetLayer("shortcut");
}
static void testDarknetLayer(const std::string& name, bool hasWeights = false)
TEST_P(Test_Darknet_layers, upsample)
{
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights", false);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
Net net = readNet(cfg, model);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref);
testDarknetLayer("upsample");
}
TEST(Test_Darknet, shortcut)
TEST_P(Test_Darknet_layers, avgpool_softmax)
{
testDarknetLayer("shortcut");
testDarknetLayer("avgpool_softmax");
}
TEST(Test_Darknet, upsample)
TEST_P(Test_Darknet_layers, region)
{
testDarknetLayer("upsample");
testDarknetLayer("region");
}
TEST(Test_Darknet, avgpool_softmax)
TEST_P(Test_Darknet_layers, reorg)
{
testDarknetLayer("avgpool_softmax");
testDarknetLayer("reorg");
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
}} // namespace

330
modules/dnn/test/test_halide_layers.cpp
Unescape View File

@ -12,32 +12,60 @@
namespace opencv_test { namespace {
#ifdef HAVE_HALIDE
using namespace cv;
using namespace cv::dnn;
using namespace testing;
static void test(LayerParams& params, Mat& input)
static void test(Mat& input, Net& net, int backendId, int targetId)
{
DNNTestLayer::checkBackend(backendId, targetId);
randu(input, -1.0f, 1.0f);
Net net;
int lid = net.addLayer(params.name, params.type, params);
net.connect(0, 0, lid, 0);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward(params.name).clone();
Mat outputDefault = net.forward().clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(params.name).clone();
normAssert(outputDefault, outputHalide);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat outputHalide = net.forward().clone();
double l1, lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf);
normAssert(outputDefault, outputHalide, "", l1, lInf);
}
static void test(LayerParams& params, Mat& input, int backendId, int targetId)
{
Net net;
net.addLayerToPrev(params.name, params.type, params);
test(input, net, backendId, targetId);
}
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargetsWithHalide()
{
static const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class Test_Halide_layers : public DNNTestLayer {};
////////////////////////////////////////////////////////////////////////////////
// Padding
////////////////////////////////////////////////////////////////////////////////
TEST(Padding_Halide, Accuracy)
TEST_P(Test_Halide_layers, Padding)
{
static const int kNumRuns = 10;
std::vector<int> paddings(8);
@ -52,15 +80,16 @@ TEST(Padding_Halide, Accuracy)
lp.type = "Padding";
lp.name = "testLayer";
Mat input({1 + rng(10), 1 + rng(10), 1 + rng(10), 1 + rng(10)}, CV_32F);
test(lp, input);
int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backend, target);
}
}
////////////////////////////////////////////////////////////////////////////////
// Convolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool> > Convolution;
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<DNNBackend, DNNTarget> > > Convolution;
TEST_P(Convolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -72,8 +101,15 @@ TEST_P(Convolution, Accuracy)
Size pad = get<4>(GetParam());
Size dilation = get<5>(GetParam());
bool hasBias = get<6>(GetParam());
int backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam()));
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD) ||
(backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, CV_32F);
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
@ -93,12 +129,13 @@ TEST_P(Convolution, Accuracy)
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias({outChannels}, CV_32F);
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
@ -110,13 +147,14 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
/*stride*/ Values(Size(1, 1), Size(2, 2)),
/*pad*/ Values(Size(1, 0), Size(0, 1)),
/*dilation*/ Values(Size(1, 1), Size(2, 2)),
/*has bias*/ Bool()
/*has bias*/ Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Deconvolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool> > Deconvolution;
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<DNNBackend, DNNTarget> > > Deconvolution;
TEST_P(Deconvolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -129,8 +167,14 @@ TEST_P(Deconvolution, Accuracy)
Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
bool hasBias = get<6>(GetParam());
Mat weights({inChannels, outChannels / group, kernel.height, kernel.width}, CV_32F);
int backendId = get<0>(get<7>(GetParam()));
int targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
dilation.width == 2 && dilation.height == 2)
throw SkipTestException("");
int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
@ -152,12 +196,13 @@ TEST_P(Deconvolution, Accuracy)
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias({outChannels}, CV_32F);
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
@ -168,13 +213,14 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
/*pad*/ Values(Size(1, 0), Size(0, 1)),
/*dilation*/ Values(Size(1, 1), Size(2, 2)),
/*stride, adj. pad*/ Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)),
/*has bias*/ Bool()
/*has bias*/ Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// LRN
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string> > LRN;
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<DNNBackend, DNNTarget> > > LRN;
TEST_P(LRN, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -185,6 +231,10 @@ TEST_P(LRN, Accuracy)
float bias = get<2>(GetParam())[2];
bool normBySize = get<3>(GetParam());
std::string nrmType = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
LayerParams lp;
lp.set("norm_region", nrmType);
@ -196,8 +246,9 @@ TEST_P(LRN, Accuracy)
lp.type = "LRN";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
@ -207,19 +258,24 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
/*alpha, beta,*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
/*bias */ Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
/*norm_by_size*/ Bool(),
/*norm_type*/ Values("ACROSS_CHANNELS", "WITHIN_CHANNEL")
/*norm_type*/ Values("ACROSS_CHANNELS", "WITHIN_CHANNEL"),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Average pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size> > AvePooling;
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > AvePooling;
TEST_P(AvePooling, Accuracy)
{
int inChannels = get<0>(GetParam());
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
const int inWidth = (outSize.width - 1) * stride.width + kernel.width;
const int inHeight = (outSize.height - 1) * stride.height + kernel.height;
@ -233,21 +289,23 @@ TEST_P(AvePooling, Accuracy)
lp.type = "Pooling";
lp.name = "testLayer";
Mat input({1, inChannels, inHeight, inWidth}, CV_32F);
test(lp, input);
int sz[] = {1, inChannels, inHeight, inWidth};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine(
/*in channels*/ Values(3, 4),
/*out size*/ Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)),
/*kernel*/ Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2))
/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Maximum pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, Size> > MaxPooling;
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > MaxPooling;
TEST_P(MaxPooling, Accuracy)
{
int inChannels = get<0>(GetParam());
@ -255,6 +313,8 @@ TEST_P(MaxPooling, Accuracy)
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
int backendId = get<0>(get<5>(GetParam()));
int targetId = get<1>(get<5>(GetParam()));
LayerParams lp;
lp.set("pool", "max");
@ -267,8 +327,9 @@ TEST_P(MaxPooling, Accuracy)
lp.type = "Pooling";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
@ -276,19 +337,25 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
/*in size*/ Values(Size(5, 5), Size(7, 6)),
/*kernel*/ Values(Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)),
/*pad*/ Values(Size(0, 0), Size(1, 1), Size(0, 1))
/*pad*/ Values(Size(0, 0), Size(1, 1), Size(0, 1)),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Fully-connected
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, int, bool> > FullyConnected;
typedef TestWithParam<tuple<int, Size, int, bool, tuple<DNNBackend, DNNTarget> > > FullyConnected;
TEST_P(FullyConnected, Accuracy)
{
int inChannels = get<0>(GetParam());
Size inSize = get<1>(GetParam());
int outChannels = get<2>(GetParam());
bool hasBias = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE ||
(backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
randu(weights, -1.0f, 1.0f);
@ -304,39 +371,50 @@ TEST_P(FullyConnected, Accuracy)
lp.type = "InnerProduct";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
/*in channels*/ Values(3, 4),
/*in size*/ Values(Size(5, 4), Size(4, 5), Size(1, 1)),
/*out channels*/ Values(3, 4),
/*has bias*/ Bool()
/*has bias*/ Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// SoftMax
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int> > SoftMax;
typedef TestWithParam<tuple<int, tuple<DNNBackend, DNNTarget> > > SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.type = "SoftMax";
lp.name = "testLayer";
Mat input({1, inChannels, 1, 1}, CV_32F);
test(lp, input);
int sz[] = {1, inChannels, 1, 1};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Values(3, 4, 5, 1024));
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Combine(
Values(3, 4, 5, 1024),
dnnBackendsAndTargetsWithHalide()
));
//////////////////////////////////////////////////////////////////////////////
// Max pooling - unpooling
//////////////////////////////////////////////////////////////////////////////
TEST(MaxPoolUnpool_Halide, Accuracy)
TEST_P(Test_Halide_layers, MaxPoolUnpool)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
LayerParams pool;
pool.set("pool", "max");
pool.set("kernel_w", 2);
@ -366,16 +444,9 @@ TEST(MaxPoolUnpool_Halide, Accuracy)
net.connect(poolId, 0, unpoolId, 0);
net.connect(poolId, 1, unpoolId, 1);
Mat input({1, 1, 4, 4}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward("testUnpool").clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
net.setInput(input);
Mat outputHalide = net.forward("testUnpool").clone();
normAssert(outputDefault, outputHalide);
int sz[] = {1, 1, 4, 4};
Mat input(4, &sz[0], CV_32F);
test(input, net, backend, target);
}
////////////////////////////////////////////////////////////////////////////////
@ -383,7 +454,7 @@ TEST(MaxPoolUnpool_Halide, Accuracy)
////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3;
void testInPlaceActivation(LayerParams& lp)
void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
{
EXPECT_FALSE(lp.name.empty());
@ -400,24 +471,19 @@ void testInPlaceActivation(LayerParams& lp)
net.connect(0, 0, poolId, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
Mat input({1, kNumChannels, 10, 10}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward(lp.name).clone();
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(lp.name).clone();
normAssert(outputDefault, outputHalide);
int sz[] = {1, kNumChannels, 10, 10};
Mat input(4, &sz[0], CV_32F);
test(input, net, backendId, targetId);
}
typedef TestWithParam<tuple<bool, bool, float> > BatchNorm;
typedef TestWithParam<tuple<bool, bool, float, tuple<DNNBackend, DNNTarget> > > BatchNorm;
TEST_P(BatchNorm, Accuracy)
{
bool hasWeights = get<0>(GetParam());
bool hasBias = get<1>(GetParam());
float epsilon = get<2>(GetParam());
int backendId = get<0>(get<3>(GetParam()));
int targetId = get<1>(get<3>(GetParam()));
LayerParams lp;
lp.set("has_weight", hasWeights);
@ -428,56 +494,66 @@ TEST_P(BatchNorm, Accuracy)
lp.blobs.reserve(4);
for (int i = 0; i < 3; ++i)
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
if (hasBias || hasWeights)
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
for (Mat& m : lp.blobs)
randu(m, 0.0f, 1.0f);
for (int i = 0; i < lp.blobs.size(); ++i)
randu(lp.blobs[i], 0.0f, 1.0f);
testInPlaceActivation(lp);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine(
/*has weights*/ Bool(),
/*has bias*/ Bool(),
/*epsilon*/ Values(1e-3f, 1e-5f)
/*epsilon*/ Values(1e-3f, 1e-5f),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<float> > ReLU;
typedef TestWithParam<tuple<float, tuple<DNNBackend, DNNTarget> > > ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("negative_slope", negativeSlope);
lp.type = "ReLU";
lp.name = "testLayer";
testInPlaceActivation(lp);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Values(
/*negative slope*/ 2.0f, 0.3f, -0.1f, 0.0f
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
/*negative slope*/ Values(2.0f, 0.3f, -0.1f, 0.0f),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<std::string> > NoParamActivation;
typedef TestWithParam<tuple<std::string, tuple<DNNBackend, DNNTarget> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.type = get<0>(GetParam());
lp.name = "testLayer";
testInPlaceActivation(lp);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Values(
/*type*/ "TanH", "Sigmoid", "AbsVal", "BNLL"
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
/*type*/ Values("TanH", "Sigmoid", "AbsVal", "BNLL"),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<Vec3f> > Power;
typedef TestWithParam<tuple<Vec3f, tuple<DNNBackend, DNNTarget> > > Power;
TEST_P(Power, Accuracy)
{
float power = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2];
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("power", power);
@ -485,46 +561,52 @@ TEST_P(Power, Accuracy)
lp.set("shift", shift);
lp.type = "Power";
lp.name = "testLayer";
testInPlaceActivation(lp);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power,
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power, Combine(
/*power, scale, shift*/ Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f))
);
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
dnnBackendsAndTargetsWithHalide()
));
TEST(ChannelsPReLU, Accuracy)
TEST_P(Test_Halide_layers, ChannelsPReLU)
{
LayerParams lp;
lp.type = "ChannelsPReLU";
lp.name = "testLayer";
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
testInPlaceActivation(lp);
testInPlaceActivation(lp, backend, target);
}
typedef TestWithParam<tuple<bool> > Scale;
typedef TestWithParam<tuple<bool, tuple<DNNBackend, DNNTarget> > > Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get<0>(GetParam());
int backendId = get<0>(get<1>(GetParam()));
int targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("bias_term", hasBias);
lp.type = "Scale";
lp.name = "testLayer";
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
if (hasBias)
{
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[1], -1.0f, 1.0f);
}
testInPlaceActivation(lp);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Values(true, false));
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Combine(
Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Concat layer
@ -534,11 +616,13 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Values(true, false));
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, Vec3i> > Concat;
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<DNNBackend, DNNTarget> > > Concat;
TEST_P(Concat, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
Vec3i numChannels = get<1>(GetParam());
int backendId = get<0>(get<2>(GetParam()));
int targetId = get<1>(get<2>(GetParam()));
Net net;
@ -549,7 +633,8 @@ TEST_P(Concat, Accuracy)
if (!numChannels[i])
break;
Mat weights({numChannels[i], inSize[0], 1, 1}, CV_32F);
int sz[] = {numChannels[i], inSize[0], 1, 1};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
@ -578,21 +663,15 @@ TEST_P(Concat, Accuracy)
net.connect(convLayerIds[i], 0, concatId, i + 1);
}
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward(concatParam.name).clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(concatParam.name).clone();
normAssert(outputDefault, outputHalide);
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
Mat input(4, &sz[0], CV_32F);
test(input, net, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*channels*/ Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2))
/*channels*/ Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
@ -603,20 +682,27 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, std::string, int, bool> > Eltwise;
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<DNNBackend, DNNTarget> > > Eltwise;
TEST_P(Eltwise, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
std::string op = get<1>(GetParam());
int numConv = get<2>(GetParam());
bool weighted = get<3>(GetParam());
int backendId = get<0>(get<4>(GetParam()));
int targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_OPENCV &&
(targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
Net net;
std::vector<int> convLayerIds(numConv);
for (int i = 0; i < numConv; ++i)
{
Mat weights({inSize[0], inSize[0], 1, 1}, CV_32F);
int sz[] = {inSize[0], inSize[0], 1, 1};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
@ -655,28 +741,23 @@ TEST_P(Eltwise, Accuracy)
net.connect(convLayerIds[i], 0, eltwiseId, i + 1);
}
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward(eltwiseParam.name).clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(eltwiseParam.name).clone();
normAssert(outputDefault, outputHalide);
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
Mat input(4, &sz[0], CV_32F);
test(input, net, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Eltwise, Combine(
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*operation*/ Values("prod", "sum", "max"),
/*num convs*/ Values(1, 2, 3),
/*weighted(for sum only)*/ Bool()
/*weighted(for sum only)*/ Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////
// Mixed backends
////////////////////////////////////////////////////////////////////////////
#ifdef HAVE_HALIDE
TEST(MixedBackends_Halide_Default_Halide, Accuracy)
{
// Just a layer that supports Halide backend.
@ -700,7 +781,8 @@ TEST(MixedBackends_Halide_Default_Halide, Accuracy)
net.addLayerToPrev(mvn.name, mvn.type, mvn);
net.addLayerToPrev(lrn2.name, lrn2.type, lrn2);
Mat input({4, 3, 5, 6}, CV_32F);
int sz[] = {4, 3, 5, 6};
Mat input(4, &sz[0], CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
@ -718,4 +800,6 @@ TEST(MixedBackends_Halide_Default_Halide, Accuracy)
}
#endif // HAVE_HALIDE
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_Halide_layers, dnnBackendsAndTargetsWithHalide());
}} // namespace

244
modules/dnn/test/test_layers.cpp
Unescape View File

@ -92,75 +92,84 @@ void runLayer(Ptr<Layer> layer, std::vector<Mat> &inpBlobs, std::vector<Mat> &ou
outBlobs[i] = outp[i];
}
void testLayerUsingCaffeModels(String basename, int targetId = DNN_TARGET_CPU,
bool useCaffeModel = false, bool useCommonInputBlob = true)
class Test_Caffe_layers : public DNNTestLayer
{
String prototxt = _tf(basename + ".prototxt");
String caffemodel = _tf(basename + ".caffemodel");
public:
void testLayerUsingCaffeModels(const String& basename, bool useCaffeModel = false,
bool useCommonInputBlob = true, double l1 = 0.0,
double lInf = 0.0)
{
String prototxt = _tf(basename + ".prototxt");
String caffemodel = _tf(basename + ".caffemodel");
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy");
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy");
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
checkBackend(&inp, &ref);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp, "input");
Mat out = net.forward("output");
net.setInput(inp, "input");
Mat out = net.forward("output");
normAssert(ref, out);
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
}
};
typedef testing::TestWithParam<DNNTarget> Test_Caffe_layers;
TEST_P(Test_Caffe_layers, Softmax)
{
testLayerUsingCaffeModels("layer_softmax", GetParam());
testLayerUsingCaffeModels("layer_softmax");
}
TEST_P(Test_Caffe_layers, LRN_spatial)
{
testLayerUsingCaffeModels("layer_lrn_spatial", GetParam());
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_lrn_spatial");
}
TEST_P(Test_Caffe_layers, LRN_channels)
{
testLayerUsingCaffeModels("layer_lrn_channels", GetParam());
testLayerUsingCaffeModels("layer_lrn_channels");
}
TEST_P(Test_Caffe_layers, Convolution)
{
testLayerUsingCaffeModels("layer_convolution", GetParam(), true);
testLayerUsingCaffeModels("layer_convolution", true);
}
TEST_P(Test_Caffe_layers, DeConvolution)
{
testLayerUsingCaffeModels("layer_deconvolution", GetParam(), true, false);
testLayerUsingCaffeModels("layer_deconvolution", true, false);
}
TEST_P(Test_Caffe_layers, InnerProduct)
{
testLayerUsingCaffeModels("layer_inner_product", GetParam(), true);
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
testLayerUsingCaffeModels("layer_inner_product", true);
}
TEST_P(Test_Caffe_layers, Pooling_max)
{
testLayerUsingCaffeModels("layer_pooling_max", GetParam());
testLayerUsingCaffeModels("layer_pooling_max");
}
TEST_P(Test_Caffe_layers, Pooling_ave)
{
testLayerUsingCaffeModels("layer_pooling_ave", GetParam());
testLayerUsingCaffeModels("layer_pooling_ave");
}
TEST_P(Test_Caffe_layers, MVN)
{
testLayerUsingCaffeModels("layer_mvn", GetParam());
testLayerUsingCaffeModels("layer_mvn");
}
void testReshape(const MatShape& inputShape, const MatShape& targetShape,
@ -210,33 +219,38 @@ TEST(Layer_Test_Reshape, Accuracy)
}
}
TEST(Layer_Test_BatchNorm, Accuracy)
{
testLayerUsingCaffeModels("layer_batch_norm", DNN_TARGET_CPU, true);
}
TEST(Layer_Test_BatchNorm, local_stats)
TEST_P(Test_Caffe_layers, BatchNorm)
{
testLayerUsingCaffeModels("layer_batch_norm_local_stats", DNN_TARGET_CPU, true, false);
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_batch_norm", true);
testLayerUsingCaffeModels("layer_batch_norm_local_stats", true, false);
}
TEST_P(Test_Caffe_layers, ReLU)
{
testLayerUsingCaffeModels("layer_relu", GetParam());
testLayerUsingCaffeModels("layer_relu");
}
TEST(Layer_Test_Dropout, Accuracy)
TEST_P(Test_Caffe_layers, Dropout)
{
testLayerUsingCaffeModels("layer_dropout");
}
TEST_P(Test_Caffe_layers, Concat)
{
testLayerUsingCaffeModels("layer_concat", GetParam());
testLayerUsingCaffeModels("layer_concat");
testLayerUsingCaffeModels("layer_concat_optim", true, false);
testLayerUsingCaffeModels("layer_concat_shared_input", true, false);
}
TEST(Layer_Test_Fused_Concat, Accuracy)
TEST_P(Test_Caffe_layers, Fused_Concat)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL))
throw SkipTestException("");
checkBackend();
// Test case
// input
// |
@ -267,28 +281,32 @@ TEST(Layer_Test_Fused_Concat, Accuracy)
randu(input, 0.0f, 1.0f); // [0, 1] to make AbsVal an identity transformation.
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input);
normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input);
//
testLayerUsingCaffeModels("layer_concat_optim", DNN_TARGET_CPU, true, false);
testLayerUsingCaffeModels("layer_concat_shared_input", DNN_TARGET_CPU, true, false);
normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input, "", default_l1, default_lInf);
normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input, "", default_l1, default_lInf);
}
TEST_P(Test_Caffe_layers, Eltwise)
{
testLayerUsingCaffeModels("layer_eltwise", GetParam());
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_eltwise");
}
TEST_P(Test_Caffe_layers, PReLU)
{
int targetId = GetParam();
testLayerUsingCaffeModels("layer_prelu", targetId, true);
testLayerUsingCaffeModels("layer_prelu_fc", targetId, true, false);
testLayerUsingCaffeModels("layer_prelu", true);
}
// TODO: fix an unstable test case
TEST_P(Test_Caffe_layers, layer_prelu_fc)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_prelu_fc", true, false);
}
//template<typename XMat>
@ -311,13 +329,16 @@ TEST_P(Test_Caffe_layers, PReLU)
// );
//}
static void test_Reshape_Split_Slice_layers(int targetId)
TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input(6, 12, CV_32F);
RNG rng(0);
@ -326,15 +347,10 @@ static void test_Reshape_Split_Slice_layers(int targetId)
net.setInput(input, "input");
Mat output = net.forward("output");
normAssert(input, output);
normAssert(input, output, "", default_l1, default_lInf);
}
TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
{
test_Reshape_Split_Slice_layers(GetParam());
}
TEST(Layer_Conv_Elu, Accuracy)
TEST_P(Test_Caffe_layers, Conv_Elu)
{
Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
ASSERT_FALSE(net.empty());
@ -343,10 +359,11 @@ TEST(Layer_Conv_Elu, Accuracy)
Mat ref = blobFromNPY(_tf("layer_elu_out.npy"));
net.setInput(inp, "input");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(ref, out);
normAssert(ref, out, "", default_l1, default_lInf);
}
class Layer_LSTM_Test : public ::testing::Test
@ -496,37 +513,6 @@ TEST_F(Layer_RNN_Test, get_set_test)
EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
}
void testLayerUsingDarknetModels(String basename, bool useDarknetModel = false, bool useCommonInputBlob = true)
{
String cfg = _tf(basename + ".cfg");
String weights = _tf(basename + ".weights");
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy");
Net net = readNetFromDarknet(cfg, (useDarknetModel) ? weights : String());
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
net.setInput(inp, "data");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(ref, out);
}
TEST(Layer_Test_Region, Accuracy)
{
testLayerUsingDarknetModels("region", false, false);
}
TEST(Layer_Test_Reorg, Accuracy)
{
testLayerUsingDarknetModels("reorg", false, false);
}
TEST(Layer_Test_ROIPooling, Accuracy)
{
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));
@ -546,8 +532,10 @@ TEST(Layer_Test_ROIPooling, Accuracy)
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"));
net.setPreferableTarget(GetParam());
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.npy"));
@ -558,7 +546,8 @@ TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
net.setInput(imInfo, "im_info");
std::vector<Mat> outs;
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.forward(outs, "output");
for (int i = 0; i < 2; ++i)
@ -573,7 +562,6 @@ TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_layers, availableDnnTargets());
typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
TEST_P(Scale_untrainable, Accuracy)
@ -739,8 +727,10 @@ INSTANTIATE_TEST_CASE_P(Layer_Test, Crop, Combine(
// Check that by default average pooling layer should not count zero padded values
// into the normalization area.
TEST(Layer_Test_Average_pooling_kernel_area, Accuracy)
TEST_P(Test_Caffe_layers, Average_pooling_kernel_area)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
LayerParams lp;
lp.name = "testAvePool";
lp.type = "Pooling";
@ -755,17 +745,21 @@ TEST(Layer_Test_Average_pooling_kernel_area, Accuracy)
// ----+--
// 7 8 | 9
Mat inp = (Mat_<float>(3, 3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
Mat target = (Mat_<float>(2, 2) << (1 + 2 + 4 + 5) / 4.f, (3 + 6) / 2.f, (7 + 8) / 2.f, 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(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(out, blobFromImage(target));
normAssert(out, blobFromImage(ref));
}
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
TEST(Layer_PriorBox, squares)
TEST_P(Test_Caffe_layers, PriorBox_squares)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)))
throw SkipTestException("");
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
@ -783,14 +777,15 @@ TEST(Layer_PriorBox, squares)
Mat inp(1, 2, CV_32F);
randu(inp, -1, 1);
net.setInput(blobFromImage(inp));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
Mat target = (Mat_<float>(4, 4) << 0.0, 0.0, 0.75, 1.0,
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);
normAssert(out.reshape(1, 4), target);
normAssert(out.reshape(1, 4), ref);
}
typedef TestWithParam<tuple<int, int> > Layer_Test_DWconv_Prelu;
@ -1056,19 +1051,19 @@ TEST(Test_DLDT, multiple_networks)
#endif // HAVE_INF_ENGINE
// Test a custom layer.
class InterpLayer CV_FINAL : public Layer
class CustomInterpLayer CV_FINAL : public Layer
{
public:
InterpLayer(const LayerParams &params) : Layer(params)
CustomInterpLayer(const LayerParams &params) : Layer(params)
{
zoomFactor = params.get<int>("zoom_factor", 0);
outWidth = params.get<int>("width", 0);
outHeight = params.get<int>("height", 0);
}
static Ptr<InterpLayer> create(LayerParams& params)
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<InterpLayer>(new InterpLayer(params));
return Ptr<Layer>(new CustomInterpLayer(params));
}
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
@ -1142,24 +1137,41 @@ public:
}
}
virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs, outputs, internals);
}
private:
int outWidth, outHeight, zoomFactor;
};
TEST(Layer_Test_Interp_custom, Accuracy)
TEST_P(Test_Caffe_layers, Interp)
{
CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
// Test a cusom layer.
CV_DNN_REGISTER_LAYER_CLASS(Interp, CustomInterpLayer);
try
{
testLayerUsingCaffeModels("layer_interp", false, false);
}
catch (...)
{
LayerFactory::unregisterLayer("Interp");
throw;
}
LayerFactory::unregisterLayer("Interp");
}
TEST(Layer_Test_Interp, Accuracy)
{
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false);
// 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;

87
modules/dnn/test/test_precomp.hpp
Unescape View File

@ -69,6 +69,93 @@ static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
return testing::ValuesIn(targets);
}
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargets()
{
static const tuple<DNNBackend, DNNTarget> testCases[] = {
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class DNNTestLayer : public TestWithParam <tuple<DNNBackend, DNNTarget> >
{
public:
dnn::Backend backend;
dnn::Target target;
double default_l1, default_lInf;
DNNTestLayer()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
getDefaultThresholds(backend, target, &default_l1, &default_lInf);
}
static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
{
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
*l1 = 4e-3;
*lInf = 2e-2;
}
else
{
*l1 = 1e-5;
*lInf = 1e-4;
}
}
static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
{
if (backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
{
#ifdef HAVE_OPENCL
if (!cv::ocl::useOpenCL())
#endif
{
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
{
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
if (inp && ref && inp->size[0] != 1)
{
// Myriad plugin supports only batch size 1. Slice a single sample.
if (inp->size[0] == ref->size[0])
{
std::vector<cv::Range> range(inp->dims, Range::all());
range[0] = Range(0, 1);
*inp = inp->operator()(range);
range = std::vector<cv::Range>(ref->dims, Range::all());
range[0] = Range(0, 1);
*ref = ref->operator()(range);
}
else
throw SkipTestException("Myriad plugin supports only batch size 1");
}
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
}}
#endif

294
modules/dnn/test/test_tf_importer.cpp
Unescape View File

@ -78,141 +78,170 @@ static std::string path(const std::string& file)
return findDataFile("dnn/tensorflow/" + file, false);
}
static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false,
double l1 = 1e-5, double lInf = 1e-4,
bool memoryLoad = false)
class Test_TensorFlow_layers : public DNNTestLayer
{
std::string netPath = path(prefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy");
Net net;
if (memoryLoad)
public:
void runTensorFlowNet(const std::string& prefix, bool hasText = false,
double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false)
{
// Load files into a memory buffers
string dataModel;
ASSERT_TRUE(readFileInMemory(netPath, dataModel));
std::string netPath = path(prefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy");
cv::Mat input = blobFromNPY(inpPath);
cv::Mat ref = blobFromNPY(outPath);
checkBackend(&input, &ref);
Net net;
if (memoryLoad)
{
// Load files into a memory buffers
string dataModel;
ASSERT_TRUE(readFileInMemory(netPath, dataModel));
string dataConfig;
if (hasText)
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());
}
else
net = readNetFromTensorflow(netPath, netConfig);
string dataConfig;
if (hasText)
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
ASSERT_FALSE(net.empty());
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(input);
cv::Mat output = net.forward();
normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
}
else
net = readNetFromTensorflow(netPath, netConfig);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
cv::Mat input = blobFromNPY(inpPath);
cv::Mat target = blobFromNPY(outPath);
net.setInput(input);
cv::Mat output = net.forward();
normAssert(target, output, "", l1, lInf);
}
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_layers;
};
TEST_P(Test_TensorFlow_layers, conv)
{
int targetId = GetParam();
runTensorFlowNet("single_conv", targetId);
runTensorFlowNet("atrous_conv2d_valid", targetId);
runTensorFlowNet("atrous_conv2d_same", targetId);
runTensorFlowNet("depthwise_conv2d", targetId);
runTensorFlowNet("keras_atrous_conv2d_same", targetId);
runTensorFlowNet("conv_pool_nchw", targetId);
runTensorFlowNet("single_conv");
runTensorFlowNet("atrous_conv2d_valid");
runTensorFlowNet("atrous_conv2d_same");
runTensorFlowNet("depthwise_conv2d");
runTensorFlowNet("keras_atrous_conv2d_same");
runTensorFlowNet("conv_pool_nchw");
}
TEST_P(Test_TensorFlow_layers, padding)
{
int targetId = GetParam();
runTensorFlowNet("padding_same", targetId);
runTensorFlowNet("padding_valid", targetId);
runTensorFlowNet("spatial_padding", targetId);
runTensorFlowNet("padding_same");
runTensorFlowNet("padding_valid");
runTensorFlowNet("spatial_padding");
}
TEST_P(Test_TensorFlow_layers, eltwise_add_mul)
{
runTensorFlowNet("eltwise_add_mul", GetParam());
runTensorFlowNet("eltwise_add_mul");
}
TEST_P(Test_TensorFlow_layers, pad_and_concat)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTensorFlowNet("pad_and_concat");
}
TEST_P(Test_TensorFlow_layers, concat)
TEST_P(Test_TensorFlow_layers, concat_axis_1)
{
runTensorFlowNet("pad_and_concat", GetParam());
runTensorFlowNet("concat_axis_1", GetParam());
runTensorFlowNet("concat_axis_1");
}
TEST_P(Test_TensorFlow_layers, batch_norm)
{
int targetId = GetParam();
runTensorFlowNet("batch_norm", targetId);
runTensorFlowNet("fused_batch_norm", targetId);
runTensorFlowNet("batch_norm_text", targetId, true);
runTensorFlowNet("mvn_batch_norm", targetId);
runTensorFlowNet("mvn_batch_norm_1x1", targetId);
runTensorFlowNet("unfused_batch_norm", targetId);
runTensorFlowNet("fused_batch_norm_no_gamma", targetId);
runTensorFlowNet("unfused_batch_norm_no_gamma", targetId);
runTensorFlowNet("batch_norm");
runTensorFlowNet("batch_norm", false, 0.0, 0.0, true);
runTensorFlowNet("fused_batch_norm");
runTensorFlowNet("fused_batch_norm", false, 0.0, 0.0, true);
runTensorFlowNet("batch_norm_text", true);
runTensorFlowNet("batch_norm_text", true, 0.0, 0.0, true);
runTensorFlowNet("unfused_batch_norm");
runTensorFlowNet("fused_batch_norm_no_gamma");
runTensorFlowNet("unfused_batch_norm_no_gamma");
}
TEST_P(Test_TensorFlow_layers, mvn_batch_norm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("mvn_batch_norm");
runTensorFlowNet("mvn_batch_norm_1x1");
}
TEST_P(Test_TensorFlow_layers, pooling)
{
int targetId = GetParam();
cv::ocl::Device d = cv::ocl::Device::getDefault();
bool loosenFlag = targetId == DNN_TARGET_OPENCL && d.isIntel() && d.type() == cv::ocl::Device::TYPE_CPU;
runTensorFlowNet("max_pool_even", targetId);
runTensorFlowNet("max_pool_odd_valid", targetId);
runTensorFlowNet("ave_pool_same", targetId);
runTensorFlowNet("max_pool_odd_same", targetId, false, loosenFlag ? 3e-5 : 1e-5, loosenFlag ? 3e-4 : 1e-4);
runTensorFlowNet("reduce_mean", targetId); // an average pooling over all spatial dimensions.
runTensorFlowNet("max_pool_even");
runTensorFlowNet("max_pool_odd_valid");
runTensorFlowNet("max_pool_odd_same");
runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
}
// TODO: fix tests and replace to pooling
TEST_P(Test_TensorFlow_layers, ave_pool_same)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTensorFlowNet("ave_pool_same");
}
TEST_P(Test_TensorFlow_layers, deconvolution)
{
int targetId = GetParam();
runTensorFlowNet("deconvolution", targetId);
runTensorFlowNet("deconvolution_same", targetId);
runTensorFlowNet("deconvolution_stride_2_same", targetId);
runTensorFlowNet("deconvolution_adj_pad_valid", targetId);
runTensorFlowNet("deconvolution_adj_pad_same", targetId);
runTensorFlowNet("keras_deconv_valid", targetId);
runTensorFlowNet("keras_deconv_same", targetId);
runTensorFlowNet("deconvolution");
runTensorFlowNet("deconvolution_same");
runTensorFlowNet("deconvolution_stride_2_same");
runTensorFlowNet("deconvolution_adj_pad_valid");
runTensorFlowNet("deconvolution_adj_pad_same");
runTensorFlowNet("keras_deconv_valid");
runTensorFlowNet("keras_deconv_same");
}
TEST_P(Test_TensorFlow_layers, matmul)
{
int targetId = GetParam();
runTensorFlowNet("matmul", targetId);
runTensorFlowNet("nhwc_reshape_matmul", targetId);
runTensorFlowNet("nhwc_transpose_reshape_matmul", targetId);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
runTensorFlowNet("matmul");
runTensorFlowNet("nhwc_reshape_matmul");
runTensorFlowNet("nhwc_transpose_reshape_matmul");
}
TEST_P(Test_TensorFlow_layers, reshape)
{
int targetId = GetParam();
runTensorFlowNet("shift_reshape_no_reorder", targetId);
runTensorFlowNet("reshape_no_reorder", targetId);
runTensorFlowNet("reshape_reduce", targetId);
runTensorFlowNet("flatten", targetId, true);
runTensorFlowNet("unfused_flatten", targetId);
runTensorFlowNet("unfused_flatten_unknown_batch", targetId);
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("shift_reshape_no_reorder");
runTensorFlowNet("reshape_no_reorder");
runTensorFlowNet("reshape_reduce");
}
TEST_P(Test_TensorFlow_layers, flatten)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTensorFlowNet("flatten", true);
runTensorFlowNet("unfused_flatten");
runTensorFlowNet("unfused_flatten_unknown_batch");
}
TEST_P(Test_TensorFlow_layers, l2_normalize)
{
int targetId = GetParam();
runTensorFlowNet("l2_normalize", targetId);
runTensorFlowNet("l2_normalize_3d", targetId);
runTensorFlowNet("l2_normalize");
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, availableDnnTargets());
// TODO: fix it and add to l2_normalize
TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTensorFlowNet("l2_normalize_3d");
}
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets;
@ -359,91 +388,96 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableDnnTargets());
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_fp16;
TEST_P(Test_TensorFlow_fp16, tests)
TEST_P(Test_TensorFlow_layers, fp16_weights)
{
int targetId = GetParam();
const float l1 = 7e-4;
const float lInf = 1e-2;
runTensorFlowNet("fp16_single_conv", targetId, false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", targetId, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", targetId, false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", targetId, false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", targetId, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", targetId, false, l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", targetId, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", targetId, false, l1, lInf);
runTensorFlowNet("fp16_padding_same", targetId, false, l1, lInf);
const float l1 = 0.00071;
const float lInf = 0.012;
runTensorFlowNet("fp16_single_conv", false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", false, l1, lInf);
runTensorFlowNet("fp16_padding_same", false, l1, lInf);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_fp16,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
// TODO: fix pad_and_concat and add this test case to fp16_weights
TEST_P(Test_TensorFlow_layers, fp16_pad_and_concat)
{
const float l1 = 0.00071;
const float lInf = 0.012;
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
}
TEST(Test_TensorFlow, defun)
TEST_P(Test_TensorFlow_layers, defun)
{
runTensorFlowNet("defun_dropout");
}
TEST(Test_TensorFlow, quantized)
TEST_P(Test_TensorFlow_layers, quantized)
{
runTensorFlowNet("uint8_single_conv");
}
TEST(Test_TensorFlow, lstm)
TEST_P(Test_TensorFlow_layers, lstm)
{
runTensorFlowNet("lstm", DNN_TARGET_CPU, true);
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTensorFlowNet("lstm", true);
runTensorFlowNet("lstm", true, 0.0, 0.0, true);
}
TEST(Test_TensorFlow, split)
TEST_P(Test_TensorFlow_layers, split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
runTensorFlowNet("split_equals");
}
TEST(Test_TensorFlow, resize_nearest_neighbor)
TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTensorFlowNet("resize_nearest_neighbor");
runTensorFlowNet("keras_upsampling2d");
}
TEST(Test_TensorFlow, slice)
TEST_P(Test_TensorFlow_layers, slice)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTensorFlowNet("slice_4d");
}
TEST(Test_TensorFlow, softmax)
TEST_P(Test_TensorFlow_layers, softmax)
{
runTensorFlowNet("keras_softmax");
}
TEST(Test_TensorFlow, relu6)
TEST_P(Test_TensorFlow_layers, relu6)
{
runTensorFlowNet("keras_relu6");
runTensorFlowNet("keras_relu6", DNN_TARGET_CPU, /*hasText*/ true);
runTensorFlowNet("keras_relu6", /*hasText*/ true);
}
TEST(Test_TensorFlow, keras_mobilenet_head)
TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
{
runTensorFlowNet("keras_mobilenet_head");
}
TEST(Test_TensorFlow, memory_read)
{
double l1 = 1e-5;
double lInf = 1e-4;
runTensorFlowNet("lstm", DNN_TARGET_CPU, true, l1, lInf, true);
runTensorFlowNet("batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("fused_batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
}
TEST(Test_TensorFlow, resize_bilinear)
TEST_P(Test_TensorFlow_layers, resize_bilinear)
{
runTensorFlowNet("resize_bilinear");
runTensorFlowNet("resize_bilinear_factor");
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets());
TEST(Test_TensorFlow, two_inputs)
{
Net net = readNet(path("two_inputs_net.pbtxt"));

1
modules/dnn/test/test_torch_importer.cpp
Unescape View File

@ -296,7 +296,6 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
net.setInput(inputBlob);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
// Deprocessing.

Write Preview
Loading…
Cancel
Save