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dnn: reduce set of ignored warnings

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This commit is contained in:
Alexander Alekhin 2018-11-14 20:25:23 +00:00 committed by Alexander Alekhin
parent 02d2cc58d7
commit 96c71dd3d2
42 changed files with 195 additions and 178 deletions
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1
doc/Doxyfile.in
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@ -252,6 +252,7 @@ PREDEFINED = __cplusplus=1 \
CV_SSE2=1 \
CV__DEBUG_NS_BEGIN= \
CV__DEBUG_NS_END= \
CV_DEPRECATED_EXTERNAL= \
CV_DEPRECATED=
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES

9
modules/core/include/opencv2/core/cvdef.h
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@ -363,6 +363,15 @@ Cv64suf;
# endif
#endif
#ifndef CV_DEPRECATED_EXTERNAL
# if defined(__OPENCV_BUILD)
# define CV_DEPRECATED_EXTERNAL /* nothing */
# else
# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED
# endif
#endif
#ifndef CV_EXTERN_C
# ifdef __cplusplus
# define CV_EXTERN_C extern "C"

4
modules/core/src/matmul.cpp
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@ -1699,7 +1699,7 @@ transform_( const T* src, T* dst, const WT* m, int len, int scn, int dcn )
}
}
#if CV_SIMD128
#if CV_SIMD128 && !defined(__aarch64__)
static inline void
load3x3Matrix(const float* m, v_float32x4& m0, v_float32x4& m1, v_float32x4& m2, v_float32x4& m3)
{
@ -1708,7 +1708,9 @@ load3x3Matrix(const float* m, v_float32x4& m0, v_float32x4& m1, v_float32x4& m2,
m2 = v_float32x4(m[2], m[6], m[10], 0);
m3 = v_float32x4(m[3], m[7], m[11], 0);
}
#endif
#if CV_SIMD128
static inline v_int16x8
v_matmulvec(const v_int16x8 &v0, const v_int16x8 &m0, const v_int16x8 &m1, const v_int16x8 &m2, const v_int32x4 &m3, const int BITS)
{

15
modules/dnn/CMakeLists.txt
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@ -20,11 +20,6 @@ else()
ocv_cmake_hook_append(INIT_MODULE_SOURCES_opencv_dnn "${CMAKE_CURRENT_LIST_DIR}/cmake/hooks/INIT_MODULE_SOURCES_opencv_dnn.cmake")
endif()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses -Wmaybe-uninitialized -Wsign-promo
-Wmissing-declarations -Wmissing-prototypes
)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4701 /wd4100)
if(MSVC)
add_definitions( -D_CRT_SECURE_NO_WARNINGS=1 )
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244 /wd4267 /wd4018 /wd4355 /wd4800 /wd4251 /wd4996 /wd4146
@ -33,12 +28,14 @@ if(MSVC)
)
else()
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-deprecated -Wmissing-prototypes -Wmissing-declarations -Wshadow
-Wunused-parameter -Wunused-local-typedefs -Wsign-compare -Wsign-promo
-Wundef -Wtautological-undefined-compare -Wignored-qualifiers -Wextra
-Wunused-function -Wunused-const-variable -Wdeprecated-declarations
-Wunused-parameter -Wsign-compare
)
endif()
if(NOT HAVE_CXX11)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-undef) # LANG_CXX11 from protobuf files
endif()
if(APPLE_FRAMEWORK)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshorten-64-to-32)
endif()
@ -55,8 +52,6 @@ add_definitions(-DHAVE_PROTOBUF=1)
#suppress warnings in autogenerated caffe.pb.* files
ocv_warnings_disable(CMAKE_CXX_FLAGS
-Wunused-parameter -Wundef -Wignored-qualifiers -Wno-enum-compare
-Wdeprecated-declarations
/wd4125 /wd4267 /wd4127 /wd4244 /wd4512 /wd4702
/wd4456 /wd4510 /wd4610 /wd4800
/wd4701 /wd4703 # potentially uninitialized local/pointer variable 'value' used

4
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -236,7 +236,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
int type;
Size kernel, stride;
int pad_l, pad_t, pad_r, pad_b;
CV_DEPRECATED Size pad;
CV_DEPRECATED_EXTERNAL Size pad;
bool globalPooling;
bool computeMaxIdx;
String padMode;
@ -578,7 +578,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
{
public:
float pnorm, epsilon;
CV_DEPRECATED bool acrossSpatial;
CV_DEPRECATED_EXTERNAL bool acrossSpatial;
static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
};

5
modules/dnn/include/opencv2/dnn/dict.hpp
View File

@ -60,12 +60,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
struct CV_EXPORTS_W DictValue
{
DictValue(const DictValue &r);
DictValue(bool i) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i ? 1 : 0; } //!< Constructs integer scalar
DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
CV_WRAP DictValue(int i) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
CV_WRAP DictValue(int i) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; } //!< Constructs integer scalar
DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; } //!< Constructs integer scalar
CV_WRAP DictValue(double p) : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; } //!< Constructs floating point scalar
CV_WRAP DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< Constructs string scalar
DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< @overload
DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; } //!< @overload
template<typename TypeIter>
static DictValue arrayInt(TypeIter begin, int size); //!< Constructs integer array

9
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -186,7 +186,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* If this method is called after network has allocated all memory for input and output blobs
* and before inferencing.
*/
CV_DEPRECATED virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
CV_DEPRECATED_EXTERNAL
virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
/** @brief Computes and sets internal parameters according to inputs, outputs and blobs.
* @param[in] inputs vector of already allocated input blobs
@ -203,7 +204,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param[out] output allocated output blobs, which will store results of the computation.
* @param[out] internals allocated internal blobs
*/
CV_DEPRECATED virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals);
CV_DEPRECATED_EXTERNAL
virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals);
/** @brief Given the @p input blobs, computes the output @p blobs.
* @param[in] inputs the input blobs.
@ -223,7 +225,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @overload
* @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
*/
CV_DEPRECATED void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
CV_DEPRECATED_EXTERNAL
void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
/** @brief
* @overload

13
modules/dnn/perf/perf_net.cpp
View File

@ -175,8 +175,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
target == DNN_TARGET_MYRIAD))
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)))
throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3));
@ -185,7 +184,7 @@ PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3));
@ -194,7 +193,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_coco)
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", "",
Mat(cv::Size(368, 368), CV_32FC3));
@ -203,7 +202,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi)
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
@ -230,7 +229,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/dog416.png", false));
Mat inp;
@ -241,7 +240,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
}

12
modules/dnn/src/caffe/caffe_io.cpp
View File

@ -404,7 +404,7 @@ bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection_,
PoolingParameter_PoolMethod_STOCHASTIC);
break;
default:
LOG(ERROR) << "Unknown pool method " << pool;
LOG(ERROR) << "Unknown pool method " << (int)pool;
is_fully_compatible = false;
}
} else {
@ -863,7 +863,7 @@ bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
while (layer_param->param_size() <= i) { layer_param->add_param(); }
layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
}
ParamSpec_DimCheckMode mode;
ParamSpec_DimCheckMode mode = ParamSpec_DimCheckMode_STRICT;
for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
while (layer_param->param_size() <= i) { layer_param->add_param(); }
switch (v1_layer_param.blob_share_mode(i)) {
@ -875,8 +875,8 @@ bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
break;
default:
LOG(FATAL) << "Unknown blob_share_mode: "
<< v1_layer_param.blob_share_mode(i);
break;
<< (int)v1_layer_param.blob_share_mode(i);
CV_Error_(Error::StsError, ("Unknown blob_share_mode: %d", (int)v1_layer_param.blob_share_mode(i)));
}
layer_param->mutable_param(i)->set_share_mode(mode);
}
@ -1102,12 +1102,12 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
case V1LayerParameter_LayerType_THRESHOLD:
return "Threshold";
default:
LOG(FATAL) << "Unknown V1LayerParameter layer type: " << type;
LOG(FATAL) << "Unknown V1LayerParameter layer type: " << (int)type;
return "";
}
}
const int kProtoReadBytesLimit = INT_MAX; // Max size of 2 GB minus 1 byte.
static const int kProtoReadBytesLimit = INT_MAX; // Max size of 2 GB minus 1 byte.
bool ReadProtoFromBinary(ZeroCopyInputStream* input, Message *proto) {
CodedInputStream coded_input(input);

8
modules/dnn/src/dnn.cpp
View File

@ -352,7 +352,7 @@ struct LayerPin
bool operator<(const LayerPin &r) const
{
return lid < r.lid || lid == r.lid && oid < r.oid;
return lid < r.lid || (lid == r.lid && oid < r.oid);
}
bool operator ==(const LayerPin &r) const
@ -427,7 +427,7 @@ struct DataLayer : public Layer
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1);
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@ -1690,8 +1690,8 @@ struct Net::Impl
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
if( !fusion || preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
if( !fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE))
return;
CV_TRACE_FUNCTION();

4
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -151,8 +151,8 @@ 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 && haveHalide()) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
#ifdef HAVE_OPENCL

2
modules/dnn/src/layers/blank_layer.cpp
View File

@ -57,7 +57,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

4
modules/dnn/src/layers/concat_layer.cpp
View File

@ -104,8 +104,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding || // By channels
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding;
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) || // By channels
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding);
}
class ChannelConcatInvoker : public ParallelLoopBody

2
modules/dnn/src/layers/crop_layer.cpp
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@ -68,7 +68,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

2
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -198,7 +198,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized && !_clip;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized && !_clip);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

2
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -98,7 +98,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty());
(backendId == DNN_BACKEND_INFERENCE_ENGINE && (op != SUM || coeffs.empty()));
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

2
modules/dnn/src/layers/flatten_layer.cpp
View File

@ -65,7 +65,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

4
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -123,8 +123,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1;
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1);
}
virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE

2
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -92,7 +92,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && (preferableTarget != DNN_TARGET_MYRIAD || type == CHANNEL_NRM);
(backendId == DNN_BACKEND_INFERENCE_ENGINE && (preferableTarget != DNN_TARGET_MYRIAD || type == CHANNEL_NRM));
}
#ifdef HAVE_OPENCL

3
modules/dnn/src/layers/max_unpooling_layer.cpp
View File

@ -35,8 +35,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() &&
!poolPad.width && !poolPad.height;
(backendId == DNN_BACKEND_HALIDE && haveHalide() && !poolPad.width && !poolPad.height);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

2
modules/dnn/src/layers/padding_layer.cpp
View File

@ -91,7 +91,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4;
(backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

2
modules/dnn/src/layers/permute_layer.cpp
View File

@ -105,7 +105,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

8
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -154,8 +154,8 @@ public:
}
else
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() &&
(type == MAX || type == AVE && !pad_t && !pad_l && !pad_b && !pad_r);
(backendId == DNN_BACKEND_HALIDE && haveHalide() &&
(type == MAX || (type == AVE && !pad_t && !pad_l && !pad_b && !pad_r)));
}
#ifdef HAVE_OPENCL
@ -341,8 +341,8 @@ public:
src.isContinuous(), dst.isContinuous(),
src.type() == CV_32F, src.type() == dst.type(),
src.dims == 4, dst.dims == 4,
((poolingType == ROI || poolingType == PSROI) && dst.size[0] ==rois.size[0] || src.size[0] == dst.size[0]),
poolingType == PSROI || src.size[1] == dst.size[1],
(((poolingType == ROI || poolingType == PSROI) && dst.size[0] == rois.size[0]) || src.size[0] == dst.size[0]),
poolingType == PSROI || src.size[1] == dst.size[1],
(mask.empty() || (mask.type() == src.type() && mask.size == dst.size)));
PoolingInvoker p;

2
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -271,7 +271,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

2
modules/dnn/src/layers/proposal_layer.cpp
View File

@ -87,7 +87,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && preferableTarget != DNN_TARGET_MYRIAD;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && preferableTarget != DNN_TARGET_MYRIAD);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

4
modules/dnn/src/layers/recurrent_layers.cpp
View File

@ -175,7 +175,7 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(!usePeephole && blobs.size() == 3 || usePeephole && blobs.size() == 6);
CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
CV_Assert(inputs.size() == 1);
const MatShape& inp0 = inputs[0];
@ -221,7 +221,7 @@ public:
std::vector<Mat> input;
inputs_arr.getMatVector(input);
CV_Assert(!usePeephole && blobs.size() == 3 || usePeephole && blobs.size() == 6);
CV_Assert((!usePeephole && blobs.size() == 3) || (usePeephole && blobs.size() == 6));
CV_Assert(input.size() == 1);
const Mat& inp0 = input[0];

2
modules/dnn/src/layers/reshape_layer.cpp
View File

@ -178,7 +178,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

4
modules/dnn/src/layers/scale_layer.cpp
View File

@ -45,13 +45,13 @@ public:
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
hasWeights = blobs.size() == 2 || (blobs.size() == 1 && !hasBias);
CV_Assert(inputs.size() == 2 && blobs.empty() || blobs.size() == (int)hasWeights + (int)hasBias);
CV_Assert((inputs.size() == 2 && blobs.empty()) || blobs.size() == (int)hasWeights + (int)hasBias);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1);
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

2
modules/dnn/src/layers/slice_layer.cpp
View File

@ -111,7 +111,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
(backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,

4
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -89,8 +89,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax;
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax);
}
#ifdef HAVE_OPENCL

2
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
View File

@ -638,7 +638,7 @@ void OCL4DNNConvSpatial<Dtype>::generateKey()
<< "p" << pad_w_ << "x" << pad_h_ << "_"
<< "num" << num_ << "_"
<< "M" << M_ << "_"
<< "activ" << fused_activ_ << "_"
<< "activ" << (int)fused_activ_ << "_"
<< "eltwise" << fused_eltwise_ << "_"
<< precision;

2
modules/dnn/src/op_inf_engine.cpp
View File

@ -559,7 +559,7 @@ bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
bool InfEngineBackendLayer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
}
void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,

2
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -156,6 +156,7 @@ void blobFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
}
}
#if 0
void printList(const tensorflow::AttrValue::ListValue &val)
{
std::cout << "(";
@ -235,6 +236,7 @@ void printLayerAttr(const tensorflow::NodeDef &layer)
std::cout << std::endl;
}
}
#endif
bool hasLayerAttr(const tensorflow::NodeDef &layer, const std::string &name)
{

2
modules/dnn/src/tensorflow/tf_io.cpp
View File

@ -37,8 +37,6 @@ using namespace tensorflow;
using namespace ::google::protobuf;
using namespace ::google::protobuf::io;
const int kProtoReadBytesLimit = INT_MAX; // Max size of 2 GB minus 1 byte.
void ReadTFNetParamsFromBinaryFileOrDie(const char* param_file,
tensorflow::GraphDef* param) {
CHECK(ReadProtoFromBinaryFile(param_file, param))

6
modules/dnn/test/test_backends.cpp
View File

@ -194,7 +194,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368));
@ -203,7 +203,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368));
@ -212,7 +212,7 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp

106
modules/dnn/test/test_common.hpp
View File

@ -56,7 +56,7 @@ static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
case DNN_BACKEND_OPENCV: *os << "OCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
*os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
@ -67,7 +67,7 @@ static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
*os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
}
using opencv_test::tuple;
@ -235,7 +235,8 @@ namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
static inline
testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
@ -283,4 +284,103 @@ static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAnd
} // namespace
namespace opencv_test {
using namespace cv::dnn;
static inline
testing::internal::ParamGenerator<Target> availableDnnTargets()
{
static std::vector<Target> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(DNN_TARGET_OPENCL);
#endif
}
return testing::ValuesIn(targets);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
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 defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
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");
}
#else
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
#endif
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
} // namespace
#endif

2
modules/dnn/test/test_layers.cpp
View File

@ -558,7 +558,9 @@ TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
normAssert(outs[i].rowRange(0, numDets), ref);
if (numDets < outs[i].size[0])
{
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
}
}
}

6
modules/dnn/test/test_misc.cpp
View File

@ -140,9 +140,9 @@ TEST(LayerFactory, custom_layers)
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward();
if (i == 0) EXPECT_EQ(output.at<float>(0), 1);
else if (i == 1) EXPECT_EQ(output.at<float>(0), 2);
else if (i == 2) EXPECT_EQ(output.at<float>(0), 1);
if (i == 0) { EXPECT_EQ(output.at<float>(0), 1); }
else if (i == 1) { EXPECT_EQ(output.at<float>(0), 2); }
else if (i == 2) { EXPECT_EQ(output.at<float>(0), 1); }
}
LayerFactory::unregisterLayer("CustomType");
}

6
modules/dnn/test/test_onnx_importer.cpp
View File

@ -118,8 +118,8 @@ TEST_P(Test_ONNX_layers, Transpose)
TEST_P(Test_ONNX_layers, Multiplication)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 ||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
testONNXModels("mul");
}
@ -296,7 +296,7 @@ TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
TEST_P(Test_ONNX_nets, TinyYolov2)
{
if (cvtest::skipUnstableTests ||
backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) {
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))) {
throw SkipTestException("");
}
// output range: [-11; 8]

96
modules/dnn/test/test_precomp.hpp
View File

@ -49,100 +49,4 @@
#include "opencv2/dnn.hpp"
#include "test_common.hpp"
namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<Target> availableDnnTargets()
{
static std::vector<Target> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL())
targets.push_back(DNN_TARGET_OPENCL);
#endif
}
return testing::ValuesIn(targets);
}
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
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 defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
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");
}
#else
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
#endif
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
} // namespace
#endif

2
modules/dnn/test/test_tf_importer.cpp
View File

@ -101,7 +101,9 @@ public:
string dataConfig;
if (hasText)
{
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
}
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());

2
modules/python/src2/hdr_parser.py
View File

@ -423,7 +423,7 @@ class CppHeaderParser(object):
# it means class methods, not instance methods
decl_str = self.batch_replace(decl_str, [("static inline", ""), ("inline", ""),\
("CV_EXPORTS_W", ""), ("CV_EXPORTS", ""), ("CV_CDECL", ""), ("CV_WRAP ", " "), ("CV_INLINE", ""),
("CV_DEPRECATED", "")]).strip()
("CV_DEPRECATED", ""), ("CV_DEPRECATED_EXTERNAL", "")]).strip()
if decl_str.strip().startswith('virtual'):