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Merge pull request #11867 from dkurt:dnn_ie_layers

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This commit is contained in:
Vadim Pisarevsky 2018-07-06 13:13:20 +00:00
commit 523b6f32ba
13 changed files with 761 additions and 518 deletions
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4
modules/dnn/src/dnn.cpp
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@ -2730,9 +2730,9 @@ void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*>
} }
else if (targetId == DNN_TARGET_OPENCL) else if (targetId == DNN_TARGET_OPENCL)
{ {
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
if (outW == 1 && outH == 1) if (outW == 1 && outH == 1)
{ {
int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC;
top.split(c, co, ci, c_split) top.split(c, co, ci, c_split)
.fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile) .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile)
.gpu_blocks(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 x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW;
int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH; 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) top.split(x, xo, xi, x_split).split(y, yo, yi, y_split)
.split(c, co, ci, c_split) .split(c, co, ci, c_split)
.gpu_blocks(xo, yo, co) .gpu_blocks(xo, yo, co)

16
modules/dnn/src/layers/convolution_layer.cpp
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@ -82,7 +82,21 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE virtual bool supportBackend(int backendId) CV_OVERRIDE
{ {
if (backendId == DNN_BACKEND_INFERENCE_ENGINE) 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 else
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE; return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
} }

4
modules/dnn/src/layers/eltwise_layer.cpp
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@ -97,8 +97,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE virtual bool supportBackend(int backendId) CV_OVERRIDE
{ {
return backendId == DNN_BACKEND_OPENCV || return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine(); backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty());
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,

21
modules/dnn/src/layers/reorg_layer.cpp
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@ -41,9 +41,9 @@
//M*/ //M*/
#include "../precomp.hpp" #include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp> #include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp> #include <opencv2/dnn/all_layers.hpp>
#include <iostream>
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp" #include "opencl_kernels_dnn.hpp"
@ -85,6 +85,11 @@ public:
return false; return false;
} }
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
}
#ifdef HAVE_OPENCL #ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals) 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, virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE const std::vector<MatShape> &outputs) const CV_OVERRIDE
{ {

21
modules/dnn/src/layers/resize_layer.cpp
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@ -192,6 +192,11 @@ public:
return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]); 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 virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{ {
if (!outWidth && !outHeight) if (!outWidth && !outHeight)
@ -204,6 +209,22 @@ public:
scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f; scaleHeight = (outHeight > 1) ? (static_cast<float>(inpHeight - 1) / (outHeight - 1)) : 0.f;
scaleWidth = (outWidth > 1) ? (static_cast<float>(inpWidth - 1) / (outWidth - 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) Ptr<Layer> InterpLayer::create(const LayerParams& params)

16
modules/dnn/src/layers/slice_layer.cpp
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@ -266,7 +266,21 @@ public:
std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp)); std::shared_ptr<InferenceEngine::CropLayer> ieLayer(new InferenceEngine::CropLayer(lp));
CV_Assert(sliceRanges.size() == 1); 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->axis.push_back(i);
ieLayer->offset.push_back(sliceRanges[0][i].start); ieLayer->offset.push_back(sliceRanges[0][i].start);

41
modules/dnn/test/test_backends.cpp
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@ -10,18 +10,9 @@
namespace opencv_test { namespace { namespace opencv_test { namespace {
class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> > class DNNTestNetwork : public DNNTestLayer
{ {
public: 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, void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer = "", Size inpSize, const std::string& outputLayer = "",
const std::string& halideScheduler = "", const std::string& halideScheduler = "",
@ -40,32 +31,10 @@ public:
std::string halideScheduler = "", std::string halideScheduler = "",
double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2) 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)) checkBackend();
{ l1 = l1 ? l1 : default_l1;
#ifdef HAVE_OPENCL lInf = lInf ? lInf : default_lInf;
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;
}
weights = findDataFile(weights, false); weights = findDataFile(weights, false);
if (!proto.empty()) if (!proto.empty())
proto = findDataFile(proto, false); proto = findDataFile(proto, false);

202
modules/dnn/test/test_darknet_importer.cpp
View File

@ -65,76 +65,84 @@ TEST(Test_Darknet, read_yolo_voc)
ASSERT_FALSE(net.empty()); ASSERT_FALSE(net.empty());
} }
// Test object detection network from Darknet framework. class Test_Darknet_layers : public DNNTestLayer
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)
{ {
if (backendId == DNN_BACKEND_OPENCV && targetId == DNN_TARGET_OPENCL) public:
void testDarknetLayer(const std::string& name, bool hasWeights = false)
{ {
#ifdef HAVE_OPENCL std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
if (!cv::ocl::useOpenCL()) std::string model = "";
#endif if (hasWeights)
{ model = findDataFile("dnn/darknet/" + name + ".weights", false);
throw SkipTestException("OpenCL is not available/disabled in OpenCV"); 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);
} }
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD) };
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)
{ {
if (!checkMyriadTarget()) 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)
{ {
throw SkipTestException("Myriad is not available/disabled in OpenCV"); 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);
} }
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)
{
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);
}
typedef testing::TestWithParam<tuple<DNNBackend, DNNTarget> > Test_Darknet_nets;
TEST_P(Test_Darknet_nets, YoloVoc) 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<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(3); 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[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[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 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 scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames, 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) 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<cv::String> outNames(1, "detection_out");
std::vector<int> classIds(2); std::vector<int> classIds(2);
std::vector<float> confidences(2); std::vector<float> confidences(2);
std::vector<Rect2d> boxes(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[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 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 scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-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, 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) 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); std::vector<cv::String> outNames(3);
outNames[0] = "yolo_82"; outNames[0] = "yolo_82";
outNames[1] = "yolo_94"; 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[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[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) 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 scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
double iouDiff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
testDarknetModel("yolov3.cfg", "yolov3.weights", outNames, testDarknetModel("yolov3.cfg", "yolov3.weights", outNames,
classIds, confidences, boxes, backendId, targetId, scoreDiff, iouDiff); classIds, confidences, boxes, scoreDiff, iouDiff);
} }
const tuple<DNNBackend, DNNTarget> testCases[] = { INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
#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, testing::ValuesIn(testCases)); TEST_P(Test_Darknet_layers, shortcut)
static void testDarknetLayer(const std::string& name, bool hasWeights = false)
{
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);
}
TEST(Test_Darknet, shortcut)
{ {
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
throw SkipTestException("");
testDarknetLayer("shortcut"); testDarknetLayer("shortcut");
} }
TEST(Test_Darknet, upsample) TEST_P(Test_Darknet_layers, upsample)
{ {
testDarknetLayer("upsample"); testDarknetLayer("upsample");
} }
TEST(Test_Darknet, avgpool_softmax) TEST_P(Test_Darknet_layers, avgpool_softmax)
{ {
testDarknetLayer("avgpool_softmax"); testDarknetLayer("avgpool_softmax");
} }
TEST_P(Test_Darknet_layers, region)
{
testDarknetLayer("region");
}
TEST_P(Test_Darknet_layers, reorg)
{
testDarknetLayer("reorg");
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
}} // namespace }} // namespace

328
modules/dnn/test/test_halide_layers.cpp
View File

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

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

@ -92,75 +92,84 @@ void runLayer(Ptr<Layer> layer, std::vector<Mat> &inpBlobs, std::vector<Mat> &ou
outBlobs[i] = outp[i]; outBlobs[i] = outp[i];
} }
class Test_Caffe_layers : public DNNTestLayer
void testLayerUsingCaffeModels(String basename, int targetId = DNN_TARGET_CPU,
bool useCaffeModel = false, bool useCommonInputBlob = true)
{ {
String prototxt = _tf(basename + ".prototxt"); public:
String caffemodel = _tf(basename + ".caffemodel"); 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 inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy"); String outfile = _tf(basename + ".npy");
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String()); Mat inp = blobFromNPY(inpfile);
ASSERT_FALSE(net.empty()); Mat ref = blobFromNPY(outfile);
checkBackend(&inp, &ref);
net.setPreferableBackend(DNN_BACKEND_OPENCV); Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
net.setPreferableTarget(targetId); ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(inpfile); net.setPreferableBackend(backend);
Mat ref = blobFromNPY(outfile); net.setPreferableTarget(target);
net.setInput(inp, "input"); net.setInput(inp, "input");
Mat out = net.forward("output"); 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) TEST_P(Test_Caffe_layers, Softmax)
{ {
testLayerUsingCaffeModels("layer_softmax", GetParam()); testLayerUsingCaffeModels("layer_softmax");
} }
TEST_P(Test_Caffe_layers, LRN_spatial) 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) TEST_P(Test_Caffe_layers, LRN_channels)
{ {
testLayerUsingCaffeModels("layer_lrn_channels", GetParam()); testLayerUsingCaffeModels("layer_lrn_channels");
} }
TEST_P(Test_Caffe_layers, Convolution) TEST_P(Test_Caffe_layers, Convolution)
{ {
testLayerUsingCaffeModels("layer_convolution", GetParam(), true); testLayerUsingCaffeModels("layer_convolution", true);
} }
TEST_P(Test_Caffe_layers, DeConvolution) TEST_P(Test_Caffe_layers, DeConvolution)
{ {
testLayerUsingCaffeModels("layer_deconvolution", GetParam(), true, false); testLayerUsingCaffeModels("layer_deconvolution", true, false);
} }
TEST_P(Test_Caffe_layers, InnerProduct) 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) TEST_P(Test_Caffe_layers, Pooling_max)
{ {
testLayerUsingCaffeModels("layer_pooling_max", GetParam()); testLayerUsingCaffeModels("layer_pooling_max");
} }
TEST_P(Test_Caffe_layers, Pooling_ave) TEST_P(Test_Caffe_layers, Pooling_ave)
{ {
testLayerUsingCaffeModels("layer_pooling_ave", GetParam()); testLayerUsingCaffeModels("layer_pooling_ave");
} }
TEST_P(Test_Caffe_layers, MVN) TEST_P(Test_Caffe_layers, MVN)
{ {
testLayerUsingCaffeModels("layer_mvn", GetParam()); testLayerUsingCaffeModels("layer_mvn");
} }
void testReshape(const MatShape& inputShape, const MatShape& targetShape, void testReshape(const MatShape& inputShape, const MatShape& targetShape,
@ -210,33 +219,38 @@ TEST(Layer_Test_Reshape, Accuracy)
} }
} }
TEST(Layer_Test_BatchNorm, Accuracy) TEST_P(Test_Caffe_layers, BatchNorm)
{ {
testLayerUsingCaffeModels("layer_batch_norm", DNN_TARGET_CPU, true); if (backend == DNN_BACKEND_INFERENCE_ENGINE)
} throw SkipTestException("");
testLayerUsingCaffeModels("layer_batch_norm", true);
TEST(Layer_Test_BatchNorm, local_stats) testLayerUsingCaffeModels("layer_batch_norm_local_stats", true, false);
{
testLayerUsingCaffeModels("layer_batch_norm_local_stats", DNN_TARGET_CPU, true, false);
} }
TEST_P(Test_Caffe_layers, ReLU) 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"); testLayerUsingCaffeModels("layer_dropout");
} }
TEST_P(Test_Caffe_layers, Concat) 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 // Test case
// input // 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. randu(input, 0.0f, 1.0f); // [0, 1] to make AbsVal an identity transformation.
net.setInput(input); net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward(); Mat out = net.forward();
normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input); 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); normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input, "", default_l1, default_lInf);
//
testLayerUsingCaffeModels("layer_concat_optim", DNN_TARGET_CPU, true, false);
testLayerUsingCaffeModels("layer_concat_shared_input", DNN_TARGET_CPU, true, false);
} }
TEST_P(Test_Caffe_layers, Eltwise) 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) TEST_P(Test_Caffe_layers, PReLU)
{ {
int targetId = GetParam(); testLayerUsingCaffeModels("layer_prelu", true);
testLayerUsingCaffeModels("layer_prelu", targetId, true); }
testLayerUsingCaffeModels("layer_prelu_fc", targetId, true, false);
// 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> //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")); Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_FALSE(net.empty()); ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(targetId); net.setPreferableTarget(target);
Mat input(6, 12, CV_32F); Mat input(6, 12, CV_32F);
RNG rng(0); RNG rng(0);
@ -326,15 +347,10 @@ static void test_Reshape_Split_Slice_layers(int targetId)
net.setInput(input, "input"); net.setInput(input, "input");
Mat output = net.forward("output"); Mat output = net.forward("output");
normAssert(input, output); normAssert(input, output, "", default_l1, default_lInf);
} }
TEST_P(Test_Caffe_layers, Reshape_Split_Slice) TEST_P(Test_Caffe_layers, Conv_Elu)
{
test_Reshape_Split_Slice_layers(GetParam());
}
TEST(Layer_Conv_Elu, Accuracy)
{ {
Net net = readNetFromTensorflow(_tf("layer_elu_model.pb")); Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
ASSERT_FALSE(net.empty()); ASSERT_FALSE(net.empty());
@ -343,10 +359,11 @@ TEST(Layer_Conv_Elu, Accuracy)
Mat ref = blobFromNPY(_tf("layer_elu_out.npy")); Mat ref = blobFromNPY(_tf("layer_elu_out.npy"));
net.setInput(inp, "input"); net.setInput(inp, "input");
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward(); Mat out = net.forward();
normAssert(ref, out); normAssert(ref, out, "", default_l1, default_lInf);
} }
class Layer_LSTM_Test : public ::testing::Test 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)); 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) TEST(Layer_Test_ROIPooling, Accuracy)
{ {
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt")); Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));
@ -546,8 +532,10 @@ TEST(Layer_Test_ROIPooling, Accuracy)
TEST_P(Test_Caffe_layers, FasterRCNN_Proposal) 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 net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
net.setPreferableTarget(GetParam());
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy")); Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.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"); net.setInput(imInfo, "im_info");
std::vector<Mat> outs; std::vector<Mat> outs;
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.forward(outs, "output"); net.forward(outs, "output");
for (int i = 0; i < 2; ++i) 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); 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; typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
TEST_P(Scale_untrainable, Accuracy) 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 // Check that by default average pooling layer should not count zero padded values
// into the normalization area. // 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; LayerParams lp;
lp.name = "testAvePool"; lp.name = "testAvePool";
lp.type = "Pooling"; lp.type = "Pooling";
@ -755,17 +745,21 @@ TEST(Layer_Test_Average_pooling_kernel_area, Accuracy)
// ----+-- // ----+--
// 7 8 | 9 // 7 8 | 9
Mat inp = (Mat_<float>(3, 3) << 1, 2, 3, 4, 5, 6, 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); Mat tmp = blobFromImage(inp);
net.setInput(blobFromImage(inp)); net.setInput(blobFromImage(inp));
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward(); Mat out = net.forward();
normAssert(out, blobFromImage(target)); normAssert(out, blobFromImage(ref));
} }
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals). // 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; LayerParams lp;
lp.name = "testPriorBox"; lp.name = "testPriorBox";
lp.type = "PriorBox"; lp.type = "PriorBox";
@ -783,14 +777,15 @@ TEST(Layer_PriorBox, squares)
Mat inp(1, 2, CV_32F); Mat inp(1, 2, CV_32F);
randu(inp, -1, 1); randu(inp, -1, 1);
net.setInput(blobFromImage(inp)); net.setInput(blobFromImage(inp));
net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward(); 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, 0.25, 0.0, 1.0, 1.0,
0.1f, 0.1f, 0.2f, 0.2f, 0.1f, 0.1f, 0.2f, 0.2f,
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; typedef TestWithParam<tuple<int, int> > Layer_Test_DWconv_Prelu;
@ -1056,19 +1051,19 @@ TEST(Test_DLDT, multiple_networks)
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
// Test a custom layer. // Test a custom layer.
class InterpLayer CV_FINAL : public Layer class CustomInterpLayer CV_FINAL : public Layer
{ {
public: public:
InterpLayer(const LayerParams &params) : Layer(params) CustomInterpLayer(const LayerParams &params) : Layer(params)
{ {
zoomFactor = params.get<int>("zoom_factor", 0); zoomFactor = params.get<int>("zoom_factor", 0);
outWidth = params.get<int>("width", 0); outWidth = params.get<int>("width", 0);
outHeight = params.get<int>("height", 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, virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
@ -1142,23 +1137,40 @@ 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: private:
int outWidth, outHeight, zoomFactor; int outWidth, outHeight, zoomFactor;
}; };
TEST(Layer_Test_Interp_custom, Accuracy) TEST_P(Test_Caffe_layers, Interp)
{ {
CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer); if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false); 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"); LayerFactory::unregisterLayer("Interp");
// Test an implemented layer.
testLayerUsingCaffeModels("layer_interp", false, false);
} }
TEST(Layer_Test_Interp, Accuracy) INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_Caffe_layers, dnnBackendsAndTargets());
{
testLayerUsingCaffeModels("layer_interp", DNN_TARGET_CPU, false, false);
}
TEST(Layer_Test_PoolingIndices, Accuracy) TEST(Layer_Test_PoolingIndices, Accuracy)
{ {

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

@ -69,6 +69,93 @@ static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
return testing::ValuesIn(targets); 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 #endif

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

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

1
modules/dnn/test/test_torch_importer.cpp
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); Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
net.setInput(inputBlob); net.setInput(inputBlob);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward(); Mat out = net.forward();
// Deprocessing. // Deprocessing.