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Merge pull request #22229 from zihaomu:bug_fix_22195_3_4

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Alexander Alekhin 2022-07-14 20:27:25 +00:00
commit a9354fc743
3 changed files with 34 additions and 15 deletions
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10
modules/dnn/src/layers/scale_layer.cpp
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@ -91,6 +91,16 @@ public:
if (hasWeights && hasBias) if (hasWeights && hasBias)
CV_CheckEQ(weights.total(), bias.total(), "Incompatible weights/bias blobs"); CV_CheckEQ(weights.total(), bias.total(), "Incompatible weights/bias blobs");
if (weights.total() == 1)
{
// The total() of bias should be same as weights.
if (hasBias)
inpBlob.convertTo(outBlob, CV_32F, weights.at<float>(0), bias.at<float>(0));
else
inpBlob.convertTo(outBlob, CV_32F, weights.at<float>(0));
return;
}
int endAxis; int endAxis;
for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis) for (endAxis = axis + 1; endAxis <= inpBlob.dims; ++endAxis)
{ {

37
modules/dnn/src/onnx/onnx_importer.cpp
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@ -1818,6 +1818,8 @@ void ONNXImporter::parseMatMul(LayerParams& layerParams, const opencv_onnx::Node
void findBroadAxis(const MatShape& broadShape, const MatShape& outShape, size_t& axis, int& broadAxis) void findBroadAxis(const MatShape& broadShape, const MatShape& outShape, size_t& axis, int& broadAxis)
{ {
// Currently, this function can only complete 1-dimensional expansion of broadShape.
// If there are two dimensions in broadShape that need to be expended, it will fail.
const size_t diff = outShape.size() - broadShape.size(); const size_t diff = outShape.size() - broadShape.size();
// find the first non-one element of the broadcasting shape // find the first non-one element of the broadcasting shape
@ -1982,25 +1984,30 @@ void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodePro
const MatShape& outShape = outShapes[node_proto.input(0)]; const MatShape& outShape = outShapes[node_proto.input(0)];
size_t axis = 0; size_t axis = 0;
int broadAxis = -1; if (total(broadShape) != 1)
findBroadAxis(broadShape, outShape, axis, broadAxis);
// if there is a one dimension in the middle that should be broadcasted, broadcast it
if (broadAxis != -1)
{ {
opencv_onnx::NodeProto concat_node_proto = node_proto; // If broadShape is a scalar, we set axis as 0.
const std::string& input1 = concat_node_proto.input(1); // Other-wise, we check broadcast is available.
int broadAxis = -1;
findBroadAxis(broadShape, outShape, axis, broadAxis);
expandMid(layerParams.name, concat_node_proto, input1, outShape[broadAxis]); // if there is a one dimension in the middle that should be broadcasted, broadcast it
if (broadAxis != -1)
{
opencv_onnx::NodeProto concat_node_proto = node_proto;
const std::string& input1 = concat_node_proto.input(1);
LayerParams concatLP; expandMid(layerParams.name, concat_node_proto, input1, outShape[broadAxis]);
concatLP.name = layerParams.name + "/concat";
concatLP.set("axis", broadAxis);
concatLP.type = "Concat";
concat_node_proto.set_output(0, concatLP.name);
addLayer(concatLP, concat_node_proto); LayerParams concatLP;
node_proto.set_input(1, concatLP.name); concatLP.name = layerParams.name + "/concat";
concatLP.set("axis", broadAxis);
concatLP.type = "Concat";
concat_node_proto.set_output(0, concatLP.name);
addLayer(concatLP, concat_node_proto);
node_proto.set_input(1, concatLP.name);
}
} }
CV_Assert(axis != outShape.size()); CV_Assert(axis != outShape.size());

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

@ -725,6 +725,8 @@ TEST_P(Test_ONNX_layers, Div)
normAssert(ref, out, "", default_l1, default_lInf); normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net); expectNoFallbacksFromIE(net);
testONNXModels("div_test_1x1",npy, 0, 0, false, true, 2);
} }
TEST_P(Test_ONNX_layers, DynamicReshape) TEST_P(Test_ONNX_layers, DynamicReshape)