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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin 2020-09-28 21:33:29 +00:00
commit 295afd5882
12 changed files with 19 additions and 23 deletions
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2
cmake/OpenCVDetectCUDA.cmake
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@ -100,7 +100,7 @@ if(CUDA_FOUND)
set(_arch_pascal "6.0;6.1")
set(_arch_volta "7.0")
set(_arch_turing "7.5")
set(_arch_ampere "8.0")
set(_arch_ampere "8.0;8.6")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND _generations "Auto")
endif()

1
cmake/OpenCVFindIPP.cmake
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@ -252,6 +252,7 @@ if(NOT DEFINED IPPROOT)
else()
ocv_install_3rdparty_licenses(ippicv "${ICV_PACKAGE_ROOT}/EULA.txt")
endif()
ocv_install_3rdparty_licenses(ippicv "${ICV_PACKAGE_ROOT}/third-party-programs.txt")
endif()
file(TO_CMAKE_PATH "${IPPROOT}" __IPPROOT)

8
modules/calib3d/src/ippe.cpp
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@ -77,18 +77,18 @@ void PoseSolver::solveGeneric(InputArray _objectPoints, InputArray _normalizedIn
OutputArray _Ma, OutputArray _Mb)
{
//argument checking:
size_t n = static_cast<size_t>(_objectPoints.rows() * _objectPoints.cols()); //number of points
size_t n = static_cast<size_t>(_normalizedInputPoints.rows()) * static_cast<size_t>(_normalizedInputPoints.cols()); //number of points
int objType = _objectPoints.type();
int type_input = _normalizedInputPoints.type();
CV_CheckType(objType, objType == CV_32FC3 || objType == CV_64FC3,
"Type of _objectPoints must be CV_32FC3 or CV_64FC3" );
CV_CheckType(type_input, type_input == CV_32FC2 || type_input == CV_64FC2,
"Type of _normalizedInputPoints must be CV_32FC3 or CV_64FC3" );
"Type of _normalizedInputPoints must be CV_32FC2 or CV_64FC2" );
CV_Assert(_objectPoints.rows() == 1 || _objectPoints.cols() == 1);
CV_Assert(_objectPoints.rows() >= 4 || _objectPoints.cols() >= 4);
CV_Assert(_normalizedInputPoints.rows() == 1 || _normalizedInputPoints.cols() == 1);
CV_Assert(static_cast<size_t>(_objectPoints.rows() * _objectPoints.cols()) == n);
CV_Assert(static_cast<size_t>(_objectPoints.rows()) * static_cast<size_t>(_objectPoints.cols()) == n);
Mat normalizedInputPoints;
if (type_input == CV_32FC2)
@ -101,7 +101,7 @@ void PoseSolver::solveGeneric(InputArray _objectPoints, InputArray _normalizedIn
}
Mat objectInputPoints;
if (type_input == CV_32FC3)
if (objType == CV_32FC3)
{
_objectPoints.getMat().convertTo(objectInputPoints, CV_64F);
}

2
modules/core/include/opencv2/core/check.hpp
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@ -63,7 +63,7 @@ struct CheckContext {
#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
{ CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
{ CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, "" message, "" p1_str, "" p2_str }
CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);

3
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
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@ -607,6 +607,7 @@ void OCL4DNNConvSpatial<Dtype>::calculateBenchmark(const UMat &bottom, UMat &ver
{
options_.str(""); options_.clear(); // clear contents and state flags
createBasicKernel(1, 1, 1);
CV_Assert(!kernelQueue.empty()); // basic kernel must be available
kernel_index_ = kernelQueue.size() - 1;
convolve(bottom, verifyTop, weight, bias, numImages, kernelQueue[kernel_index_]);
CV_Assert(phash.find(kernelQueue[kernel_index_]->kernelName) != phash.end());
@ -1713,6 +1714,7 @@ void OCL4DNNConvSpatial<float>::useFirstAvailable(const UMat &bottom,
tunerItems[i]->blockHeight,
tunerItems[i]->blockDepth))
{
CV_Assert(!kernelQueue.empty()); // basic kernel must be available
int kernelIdx = kernelQueue.size() - 1;
kernelConfig* config = kernelQueue[kernelIdx].get();
bool failed = false;
@ -1883,6 +1885,7 @@ void OCL4DNNConvSpatial<float>::setupConvolution(const UMat &bottom,
CV_LOG_INFO(NULL, "fallback to basic kernel");
options_.str(""); options_.clear(); // clear contents and state flags
createBasicKernel(1, 1, 1);
CV_Assert(!kernelQueue.empty()); // basic kernel must be available
kernel_index_ = kernelQueue.size() - 1;
}
this->bestKernelConfig = kernelQueue[kernel_index_];

6
modules/dnn/src/onnx/onnx_importer.cpp
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@ -446,8 +446,8 @@ void ONNXImporter::populateNet(Net dstNet)
avgLp.set("pool", pool);
if (axes.size() == 2)
{
CV_CheckEQ(clamp(axes.get<int>(0), inpShape.size()), 1, ("Unsupported " + layer_type + " mode").c_str());
CV_CheckEQ(clamp(axes.get<int>(1), inpShape.size()), 2, ("Unsupported " + layer_type + " mode").c_str());
CV_CheckEQ(clamp(axes.get<int>(0), inpShape.size()), 1, "Unsupported mode");
CV_CheckEQ(clamp(axes.get<int>(1), inpShape.size()), 2, "Unsupported mode");
avgLp.set("global_pooling", true);
}
else
@ -489,7 +489,7 @@ void ONNXImporter::populateNet(Net dstNet)
}
else if (!layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax"))
{
CV_CheckEQ(layerParams.get<int>("keepdims"), 0, (layer_type + " layer only supports keepdims = false").c_str());
CV_CheckEQ(layerParams.get<int>("keepdims"), 0, "layer only supports keepdims = false");
LayerParams reshapeLp;
reshapeLp.name = layerParams.name + "/reshape";
reshapeLp.type = "Reshape";

2
modules/dnn/src/opencl/conv_layer_spatial.cl
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@ -205,7 +205,7 @@ __kernel void ConvolveBasic(
#if APPLY_BIAS
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
#else
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], biasIndex + kern);
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], kernelNum + kern);
#endif
}
}

4
modules/dnn/src/opencl/ocl4dnn_lrn.cl
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@ -83,7 +83,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
++head;
}
// subtract only
@ -93,7 +93,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
++head;
}
}

7
modules/dnn/test/test_layers.cpp
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@ -2268,13 +2268,6 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
// bug: https://github.com/opencv/opencv/issues/17953
if (actType == "ChannelsPReLU" && bias_term == false &&
backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
}
Net net;
int convId = net.addLayer(convParams.name, convParams.type, convParams);
int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);

4
modules/dnn/test/test_torch_importer.cpp
View File

@ -116,7 +116,7 @@ TEST_P(Test_Torch_layers, run_convolution)
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
l1 = 0.08;
lInf = 0.42;
lInf = 0.43;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
@ -187,7 +187,7 @@ TEST_P(Test_Torch_layers, run_depth_concat)
double lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
lInf = 0.021;
lInf = 0.032;
}
else if (target == DNN_TARGET_CUDA_FP16)
{

2
modules/ml/misc/python/test/test_goodfeatures.py
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@ -11,7 +11,7 @@ from tests_common import NewOpenCVTests
class TestGoodFeaturesToTrack_test(NewOpenCVTests):
def test_goodFeaturesToTrack(self):
arr = self.get_sample('samples/data/lena.jpg', 0)
original = arr.copy(True)
original = arr.copy()
threshes = [ x / 100. for x in range(1,10) ]
numPoints = 20000

1
platforms/ios/build_framework.py
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@ -183,7 +183,6 @@ class Builder:
] if self.debug_info else [])
if len(self.exclude) > 0:
args += ["-DBUILD_opencv_world=OFF"] if not (self.dynamic and not self.build_objc_wrapper) else []
args += ["-DBUILD_opencv_%s=OFF" % m for m in self.exclude]
if len(self.disable) > 0: