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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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modules/dnn/src/layers/batch_norm_layer.cpp
+2 -2
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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
modules/dnn/src/layers/blank_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/concat_layer.cpp
+2 -2
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@@ -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
modules/dnn/src/layers/crop_layer.cpp
+1 -1
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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,
modules/dnn/src/layers/detection_output_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/eltwise_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/flatten_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/fully_connected_layer.cpp
+2 -2
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@@ -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
modules/dnn/src/layers/lrn_layer.cpp
+1 -1
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@@ -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
modules/dnn/src/layers/max_unpooling_layer.cpp
+1 -2
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@@ -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,
modules/dnn/src/layers/padding_layer.cpp
+1 -1
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@@ -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
modules/dnn/src/layers/permute_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/pooling_layer.cpp
+4 -4
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@@ -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;
modules/dnn/src/layers/prior_box_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/proposal_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/recurrent_layers.cpp
+2 -2
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@@ -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];
modules/dnn/src/layers/reshape_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/scale_layer.cpp
+2 -2
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@@ -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
modules/dnn/src/layers/slice_layer.cpp
+1 -1
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@@ -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,
modules/dnn/src/layers/softmax_layer.cpp
+2 -2
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@@ -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