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core: repair CV_Assert() messages

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Multi-argument CV_Assert() is accessible via CV_Assert_N() (with malformed messages).
This commit is contained in:
Alexander Alekhin
2018-08-15 14:55:47 +03:00
parent b9b66ca437
commit d2e08a524e
26 changed files with 136 additions and 101 deletions
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modules/dnn/include/opencv2/dnn/shape_utils.hpp
+1 -1
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@@ -209,7 +209,7 @@ inline Range clamp(const Range& r, int axisSize)
{
Range clamped(std::max(r.start, 0),
r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
CV_Assert(clamped.start < clamped.end, clamped.end <= axisSize);
CV_Assert_N(clamped.start < clamped.end, clamped.end <= axisSize);
return clamped;
}
modules/dnn/src/caffe/caffe_importer.cpp
+1 -1
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@@ -359,7 +359,7 @@ public:
{
if (!layerParams.get<bool>("use_global_stats", true))
{
CV_Assert(layer.bottom_size() == 1, layer.top_size() == 1);
CV_Assert_N(layer.bottom_size() == 1, layer.top_size() == 1);
LayerParams mvnParams;
mvnParams.set("eps", layerParams.get<float>("eps", 1e-5));
modules/dnn/src/dnn.cpp
+8 -8
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@@ -134,7 +134,7 @@ void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalef
if (ddepth == CV_8U)
{
CV_CheckEQ(scalefactor, 1.0, "Scaling is not supported for CV_8U blob depth");
CV_Assert(mean_ == Scalar(), "Mean subtraction is not supported for CV_8U blob depth");
CV_Assert(mean_ == Scalar() && "Mean subtraction is not supported for CV_8U blob depth");
}
std::vector<Mat> images;
@@ -451,8 +451,8 @@ struct DataLayer : public Layer
{
double scale = scaleFactors[i];
Scalar& mean = means[i];
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4,
outputs[i].type() == CV_32F);
CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4);
CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, "");
bool singleMean = true;
for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)
@@ -569,7 +569,7 @@ struct DataLayer : public Layer
void finalize(const std::vector<Mat*>&, std::vector<Mat>& outputs) CV_OVERRIDE
{
CV_Assert(outputs.size() == scaleFactors.size(), outputs.size() == means.size(),
CV_Assert_N(outputs.size() == scaleFactors.size(), outputs.size() == means.size(),
inputsData.size() == outputs.size());
skip = true;
for (int i = 0; skip && i < inputsData.size(); ++i)
@@ -1237,7 +1237,7 @@ struct Net::Impl
void initHalideBackend()
{
CV_TRACE_FUNCTION();
CV_Assert(preferableBackend == DNN_BACKEND_HALIDE, haveHalide());
CV_Assert_N(preferableBackend == DNN_BACKEND_HALIDE, haveHalide());
// Iterator to current layer.
MapIdToLayerData::iterator it = layers.begin();
@@ -1330,7 +1330,7 @@ struct Net::Impl
void initInfEngineBackend()
{
CV_TRACE_FUNCTION();
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
#ifdef HAVE_INF_ENGINE
MapIdToLayerData::iterator it;
Ptr<InfEngineBackendNet> net;
@@ -1827,7 +1827,7 @@ struct Net::Impl
// To prevent memory collisions (i.e. when input of
// [conv] and output of [eltwise] is the same blob)
// we allocate a new blob.
CV_Assert(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1);
ld.outputBlobs[0] = ld.outputBlobs[0].clone();
ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]);
@@ -1984,7 +1984,7 @@ struct Net::Impl
}
// Layers that refer old input Mat will refer to the
// new data but the same Mat object.
CV_Assert(curr_output.data == output_slice.data, oldPtr == &curr_output);
CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output);
}
ld.skip = true;
printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str()));
modules/dnn/src/layers/batch_norm_layer.cpp
+1 -1
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@@ -48,7 +48,7 @@ public:
float varMeanScale = 1.f;
if (!hasWeights && !hasBias && blobs.size() > 2 && useGlobalStats) {
CV_Assert(blobs.size() == 3, blobs[2].type() == CV_32F);
CV_Assert(blobs.size() == 3); CV_CheckTypeEQ(blobs[2].type(), CV_32FC1, "");
varMeanScale = blobs[2].at<float>(0);
if (varMeanScale != 0)
varMeanScale = 1/varMeanScale;
modules/dnn/src/layers/convolution_layer.cpp
+7 -6
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@@ -349,8 +349,8 @@ public:
// (conv(I) + b1 ) * w + b2
// means to replace convolution's weights to [w*conv(I)] and bias to [b1 * w + b2]
const int outCn = weightsMat.size[0];
CV_Assert(!weightsMat.empty(), biasvec.size() == outCn + 2,
w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
CV_Assert_N(!weightsMat.empty(), biasvec.size() == outCn + 2,
w.empty() || outCn == w.total(), b.empty() || outCn == b.total());
if (!w.empty())
{
@@ -512,13 +512,14 @@ public:
Size kernel, Size pad, Size stride, Size dilation,
const ActivationLayer* activ, int ngroups, int nstripes )
{
CV_Assert( input.dims == 4 && output.dims == 4,
CV_Assert_N(
input.dims == 4 && output.dims == 4,
input.size[0] == output.size[0],
weights.rows == output.size[1],
weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
input.type() == output.type(),
input.type() == weights.type(),
input.type() == CV_32F,
input.type() == CV_32FC1,
input.isContinuous(),
output.isContinuous(),
biasvec.size() == (size_t)output.size[1]+2);
@@ -1009,8 +1010,8 @@ public:
name.c_str(), inputs[0]->size[0], inputs[0]->size[1], inputs[0]->size[2], inputs[0]->size[3],
kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);*/
CV_Assert(inputs.size() == (size_t)1, inputs[0]->size[1] % blobs[0].size[1] == 0,
outputs.size() == 1, inputs[0]->data != outputs[0].data);
CV_Assert_N(inputs.size() == (size_t)1, inputs[0]->size[1] % blobs[0].size[1] == 0,
outputs.size() == 1, inputs[0]->data != outputs[0].data);
int ngroups = inputs[0]->size[1]/blobs[0].size[1];
CV_Assert(outputs[0].size[1] % ngroups == 0);
modules/dnn/src/layers/crop_and_resize_layer.cpp
+3 -3
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@@ -14,7 +14,7 @@ class CropAndResizeLayerImpl CV_FINAL : public CropAndResizeLayer
public:
CropAndResizeLayerImpl(const LayerParams& params)
{
CV_Assert(params.has("width"), params.has("height"));
CV_Assert_N(params.has("width"), params.has("height"));
outWidth = params.get<float>("width");
outHeight = params.get<float>("height");
}
@@ -24,7 +24,7 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() == 2, inputs[0].size() == 4);
CV_Assert_N(inputs.size() == 2, inputs[0].size() == 4);
if (inputs[0][0] != 1)
CV_Error(Error::StsNotImplemented, "");
outputs.resize(1, MatShape(4));
@@ -56,7 +56,7 @@ public:
const int inpWidth = inp.size[3];
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
CV_Assert(inp.isContinuous(), out.isContinuous());
CV_Assert_N(inp.isContinuous(), out.isContinuous());
for (int b = 0; b < boxes.rows; ++b)
{
modules/dnn/src/layers/eltwise_layer.cpp
+1 -1
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@@ -139,7 +139,7 @@ public:
const std::vector<float>& coeffs, EltwiseOp op,
const ActivationLayer* activ, int nstripes)
{
CV_Assert(1 < dst.dims && dst.dims <= 4, dst.type() == CV_32F, dst.isContinuous());
CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 4, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
for( int i = 0; i > nsrcs; i++ )
modules/dnn/src/layers/padding_layer.cpp
+3 -3
View File
@@ -38,7 +38,7 @@ public:
{
paddings[i].first = paddingsParam.get<int>(i * 2); // Pad before.
paddings[i].second = paddingsParam.get<int>(i * 2 + 1); // Pad after.
CV_Assert(paddings[i].first >= 0, paddings[i].second >= 0);
CV_Assert_N(paddings[i].first >= 0, paddings[i].second >= 0);
}
}
@@ -127,8 +127,8 @@ public:
const int padBottom = outHeight - dstRanges[2].end;
const int padLeft = dstRanges[3].start;
const int padRight = outWidth - dstRanges[3].end;
CV_Assert(padTop < inpHeight, padBottom < inpHeight,
padLeft < inpWidth, padRight < inpWidth);
CV_CheckLT(padTop, inpHeight, ""); CV_CheckLT(padBottom, inpHeight, "");
CV_CheckLT(padLeft, inpWidth, ""); CV_CheckLT(padRight, inpWidth, "");
for (size_t n = 0; n < inputs[0]->size[0]; ++n)
{
modules/dnn/src/layers/pooling_layer.cpp
+5 -4
View File
@@ -216,15 +216,15 @@ public:
switch (type)
{
case MAX:
CV_Assert(inputs.size() == 1, outputs.size() == 2);
CV_Assert_N(inputs.size() == 1, outputs.size() == 2);
maxPooling(*inputs[0], outputs[0], outputs[1]);
break;
case AVE:
CV_Assert(inputs.size() == 1, outputs.size() == 1);
CV_Assert_N(inputs.size() == 1, outputs.size() == 1);
avePooling(*inputs[0], outputs[0]);
break;
case ROI: case PSROI:
CV_Assert(inputs.size() == 2, outputs.size() == 1);
CV_Assert_N(inputs.size() == 2, outputs.size() == 1);
roiPooling(*inputs[0], *inputs[1], outputs[0]);
break;
default:
@@ -311,7 +311,8 @@ public:
Size stride, Size pad, bool avePoolPaddedArea, int poolingType, float spatialScale,
bool computeMaxIdx, int nstripes)
{
CV_Assert(src.isContinuous(), dst.isContinuous(),
CV_Assert_N(
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]),
modules/dnn/src/layers/prior_box_layer.cpp
+3 -2
View File
@@ -254,7 +254,7 @@ public:
}
if (params.has("offset_h") || params.has("offset_w"))
{
CV_Assert(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
CV_Assert_N(!params.has("offset"), params.has("offset_h"), params.has("offset_w"));
getParams("offset_h", params, &_offsetsY);
getParams("offset_w", params, &_offsetsX);
CV_Assert(_offsetsX.size() == _offsetsY.size());
@@ -299,7 +299,8 @@ public:
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
CV_Assert(inputs.size() > 1, inputs[0]->dims == 4, inputs[1]->dims == 4);
CV_CheckGT(inputs.size(), (size_t)1, "");
CV_CheckEQ(inputs[0]->dims, 4, ""); CV_CheckEQ(inputs[1]->dims, 4, "");
int layerWidth = inputs[0]->size[3];
int layerHeight = inputs[0]->size[2];
modules/dnn/src/layers/reshape_layer.cpp
+1 -1
View File
@@ -197,7 +197,7 @@ public:
}
else
{
CV_Assert(inputs.size() == 2, total(inputs[0]) == total(inputs[1]));
CV_Assert_N(inputs.size() == 2, total(inputs[0]) == total(inputs[1]));
outputs.assign(1, inputs[1]);
}
return true;
modules/dnn/src/layers/resize_layer.cpp
+3 -3
View File
@@ -43,7 +43,7 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() == 1, inputs[0].size() == 4);
CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight);
outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth);
@@ -106,7 +106,7 @@ public:
const int inpSpatialSize = inpHeight * inpWidth;
const int outSpatialSize = outHeight * outWidth;
const int numPlanes = inp.size[0] * inp.size[1];
CV_Assert(inp.isContinuous(), out.isContinuous());
CV_Assert_N(inp.isContinuous(), out.isContinuous());
Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
Mat outPlanes = out.reshape(1, numPlanes * outHeight);
@@ -184,7 +184,7 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() == 1, inputs[0].size() == 4);
CV_Assert_N(inputs.size() == 1, inputs[0].size() == 4);
outputs.resize(1, inputs[0]);
outputs[0][2] = outHeight > 0 ? outHeight : (1 + zoomFactorHeight * (outputs[0][2] - 1));
outputs[0][3] = outWidth > 0 ? outWidth : (1 + zoomFactorWidth * (outputs[0][3] - 1));
modules/dnn/src/layers/scale_layer.cpp
+7 -5
View File
@@ -64,7 +64,7 @@ public:
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(outputs.size() == 1, !blobs.empty() || inputs.size() == 2);
CV_Assert_N(outputs.size() == 1, !blobs.empty() || inputs.size() == 2);
Mat &inpBlob = *inputs[0];
Mat &outBlob = outputs[0];
@@ -76,7 +76,9 @@ public:
weights = weights.reshape(1, 1);
MatShape inpShape = shape(inpBlob);
const int numWeights = !weights.empty() ? weights.total() : bias.total();
CV_Assert(numWeights != 0, !hasWeights || !hasBias || weights.total() == bias.total());
CV_Assert(numWeights != 0);
if (hasWeights && hasBias)
CV_CheckEQ(weights.total(), bias.total(), "Incompatible weights/bias blobs");
int endAxis;
for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
@@ -84,9 +86,9 @@ public:
if (total(inpShape, axis, endAxis) == numWeights)
break;
}
CV_Assert(total(inpShape, axis, endAxis) == numWeights,
!hasBias || numWeights == bias.total(),
inpBlob.type() == CV_32F && outBlob.type() == CV_32F);
CV_Assert(total(inpShape, axis, endAxis) == numWeights);
CV_Assert(!hasBias || numWeights == bias.total());
CV_CheckTypeEQ(inpBlob.type(), CV_32FC1, ""); CV_CheckTypeEQ(outBlob.type(), CV_32FC1, "");
int numSlices = total(inpShape, 0, axis);
float* inpData = (float*)inpBlob.data;
modules/dnn/src/nms.cpp
+2 -2
View File
@@ -25,7 +25,7 @@ void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
const float score_threshold, const float nms_threshold,
std::vector<int>& indices, const float eta, const int top_k)
{
CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
CV_Assert_N(bboxes.size() == scores.size(), score_threshold >= 0,
nms_threshold >= 0, eta > 0);
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
}
@@ -46,7 +46,7 @@ void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>&
const float score_threshold, const float nms_threshold,
std::vector<int>& indices, const float eta, const int top_k)
{
CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
CV_Assert_N(bboxes.size() == scores.size(), score_threshold >= 0,
nms_threshold >= 0, eta > 0);
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rotatedRectIOU);
}
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
+3 -3
View File
@@ -221,7 +221,7 @@ public:
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
fusedNode->mutable_input()->RemoveLast();
fusedNode->clear_attr();
@@ -256,7 +256,7 @@ public:
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat epsMat = getTensorContent(inputNodes.back()->attr().at("value").tensor());
CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
CV_CheckEQ(epsMat.total(), (size_t)1, ""); CV_CheckTypeEQ(epsMat.type(), CV_32FC1, "");
fusedNode->mutable_input()->RemoveLast();
fusedNode->clear_attr();
@@ -593,7 +593,7 @@ public:
std::vector<tensorflow::NodeDef*>& inputNodes) CV_OVERRIDE
{
Mat factorsMat = getTensorContent(inputNodes[1]->attr().at("value").tensor());
CV_Assert(factorsMat.total() == 2, factorsMat.type() == CV_32SC1);
CV_CheckEQ(factorsMat.total(), (size_t)2, ""); CV_CheckTypeEQ(factorsMat.type(), CV_32SC1, "");
// Height scale factor
tensorflow::TensorProto* factorY = inputNodes[1]->mutable_attr()->at("value").mutable_tensor();
modules/dnn/src/tensorflow/tf_importer.cpp
+13 -12
View File
@@ -545,8 +545,8 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe
}
else
{
CV_Assert(nodeIdx < netTxt.node_size(),
netTxt.node(nodeIdx).name() == kernel_inp.name);
CV_Assert_N(nodeIdx < netTxt.node_size(),
netTxt.node(nodeIdx).name() == kernel_inp.name);
return netTxt.node(nodeIdx).attr().at("value").tensor();
}
}
@@ -587,8 +587,8 @@ static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& cons
Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
CV_Assert(qMin.total() == 1, qMin.type() == CV_32FC1,
qMax.total() == 1, qMax.type() == CV_32FC1);
CV_Assert_N(qMin.total() == 1, qMin.type() == CV_32FC1,
qMax.total() == 1, qMax.type() == CV_32FC1);
Mat content = getTensorContent(*tensor);
@@ -1295,8 +1295,9 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(layer.input_size() == 3);
Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
Mat sizes = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert(!begins.empty(), !sizes.empty(), begins.type() == CV_32SC1,
sizes.type() == CV_32SC1);
CV_Assert_N(!begins.empty(), !sizes.empty());
CV_CheckTypeEQ(begins.type(), CV_32SC1, "");
CV_CheckTypeEQ(sizes.type(), CV_32SC1, "");
if (begins.total() == 4 && getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
{
@@ -1665,7 +1666,7 @@ void TFImporter::populateNet(Net dstNet)
if (layer.input_size() == 2)
{
Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(outSize.type() == CV_32SC1, outSize.total() == 2);
CV_CheckTypeEQ(outSize.type(), CV_32SC1, ""); CV_CheckEQ(outSize.total(), (size_t)2, "");
layerParams.set("height", outSize.at<int>(0, 0));
layerParams.set("width", outSize.at<int>(0, 1));
}
@@ -1673,8 +1674,8 @@ void TFImporter::populateNet(Net dstNet)
{
Mat factorHeight = getTensorContent(getConstBlob(layer, value_id, 1));
Mat factorWidth = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert(factorHeight.type() == CV_32SC1, factorHeight.total() == 1,
factorWidth.type() == CV_32SC1, factorWidth.total() == 1);
CV_CheckTypeEQ(factorHeight.type(), CV_32SC1, ""); CV_CheckEQ(factorHeight.total(), (size_t)1, "");
CV_CheckTypeEQ(factorWidth.type(), CV_32SC1, ""); CV_CheckEQ(factorWidth.total(), (size_t)1, "");
layerParams.set("zoom_factor_x", factorWidth.at<int>(0));
layerParams.set("zoom_factor_y", factorHeight.at<int>(0));
}
@@ -1772,7 +1773,7 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(layer.input_size() == 3);
Mat cropSize = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert(cropSize.type() == CV_32SC1, cropSize.total() == 2);
CV_CheckTypeEQ(cropSize.type(), CV_32SC1, ""); CV_CheckEQ(cropSize.total(), (size_t)2, "");
layerParams.set("height", cropSize.at<int>(0));
layerParams.set("width", cropSize.at<int>(1));
@@ -1826,8 +1827,8 @@ void TFImporter::populateNet(Net dstNet)
Mat minValue = getTensorContent(getConstBlob(layer, value_id, 1));
Mat maxValue = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert(minValue.total() == 1, minValue.type() == CV_32F,
maxValue.total() == 1, maxValue.type() == CV_32F);
CV_CheckEQ(minValue.total(), (size_t)1, ""); CV_CheckTypeEQ(minValue.type(), CV_32FC1, "");
CV_CheckEQ(maxValue.total(), (size_t)1, ""); CV_CheckTypeEQ(maxValue.type(), CV_32FC1, "");
layerParams.set("min_value", minValue.at<float>(0));
layerParams.set("max_value", maxValue.at<float>(0));
modules/dnn/src/torch/torch_importer.cpp
+2 -2
View File
@@ -896,8 +896,8 @@ struct TorchImporter
else if (nnName == "SpatialZeroPadding" || nnName == "SpatialReflectionPadding")
{
readTorchTable(scalarParams, tensorParams);
CV_Assert(scalarParams.has("pad_l"), scalarParams.has("pad_r"),
scalarParams.has("pad_t"), scalarParams.has("pad_b"));
CV_Assert_N(scalarParams.has("pad_l"), scalarParams.has("pad_r"),
scalarParams.has("pad_t"), scalarParams.has("pad_b"));
int padTop = scalarParams.get<int>("pad_t");
int padLeft = scalarParams.get<int>("pad_l");
int padRight = scalarParams.get<int>("pad_r");
modules/dnn/test/test_layers.cpp
+1 -1
View File
@@ -814,7 +814,7 @@ TEST_P(Layer_Test_DWconv_Prelu, Accuracy)
const int group = 3; //outChannels=group when group>1
const int num_output = get<1>(GetParam());
const int kernel_depth = num_input/group;
CV_Assert(num_output >= group, num_output % group == 0, num_input % group == 0);
CV_Assert_N(num_output >= group, num_output % group == 0, num_input % group == 0);
Net net;
//layer 1: dwconv