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Merge pull request #29621 from YangGuanyuhan/christylinux/fix-onnx-gather-cast

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dnn: (bug fix )preserve Cast semantics after ONNX Gather
This commit is contained in:
Abhishek Gola
2026-08-04 01:01:18 +05:30
committed by GitHub
parent d6eb13adb9 143b084be2
commit 68a918b900
2 changed files with 41 additions and 83 deletions
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83
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
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@@ -1402,74 +1402,6 @@ public:
}
};
class GatherCastSubgraph : public Subgraph
{
public:
GatherCastSubgraph()
{
int input = addNodeToMatch("");
int index = addNodeToMatch("Constant");
gather = addNodeToMatch("Gather", input, index);
cast = addNodeToMatch("Cast", gather);
setFusedNode("Gather", input, index);
}
virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
std::vector<int>& matchedNodesIds) CV_OVERRIDE
{
bool retVal = Subgraph::match(net, nodeId, matchedNodesIds);
size_t matchedNodesNum = matchedNodesIds.size();
// Now we check if merging can be made for these Gather and Cast nodes
if (!retVal || matchedNodesNum < 2)
return retVal;
else {
int nodeToMatch = matchedNodesIds[cast];
const Ptr<ImportNodeWrapper> node = net->getNode(nodeToMatch);
if (node->getType() == "Cast") {
int inpNodeId = matchedNodesIds[gather];
const Ptr<ImportNodeWrapper> inpNode = net->getNode(inpNodeId);
if (inpNode->getType() == "Gather") {
int numNodes = net->getNumNodes();
std::string inpNodeName = node->getInputName(0);
for (int i = 0; i < numNodes; ++i) {
const Ptr<ImportNodeWrapper> node_to_check = net->getNode(i);
int numInp = node_to_check->getNumInputs();
for (int inp = 0; inp < numInp; ++inp) {
if (i != nodeToMatch && inpNodeName == node_to_check->getInputName(inp)) {
// Another node has the same input node, so it cannot be merged.
return false;
}
}
}
// extract axis from original Gather node
axis = 0;
opencv_onnx::NodeProto* origGatherNode =
inpNode.dynamicCast<ONNXNodeWrapper>()->node;
for (int i = 0; i < origGatherNode->attribute_size(); i++) {
opencv_onnx::AttributeProto attr = origGatherNode->attribute(i);
if (attr.name() == "axis")
axis = attr.i();
}
}
}
}
return retVal;
}
virtual void finalize(const Ptr<ImportGraphWrapper>& net,
const Ptr<ImportNodeWrapper>& fusedNode,
std::vector<Ptr<ImportNodeWrapper> >& /*inputs*/) CV_OVERRIDE
{
opencv_onnx::NodeProto* node = fusedNode.dynamicCast<ONNXNodeWrapper>()->node;
opencv_onnx::AttributeProto* new_attr = node->add_attribute();
new_attr->set_name("axis");
new_attr->set_i(axis);
}
private:
int cast, gather, axis;
};
/* Constant folding shape for Expand.
Before fusion:
@@ -1629,19 +1561,6 @@ public:
}
};
class MulCastSubgraph : public Subgraph
{
public:
MulCastSubgraph()
{
int input = addNodeToMatch("");
int scaleNode = addNodeToMatch("Constant");
int mul = addNodeToMatch("Mul", input, scaleNode);
addNodeToMatch("Cast", mul);
setFusedNode("Mul", input, scaleNode);
}
};
class ExtractScalesSubgraph : public Subgraph
{
public:
@@ -1922,8 +1841,6 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net, const std::string& basePath
subgraphs.push_back(makePtr<GeluSubGraph>());
subgraphs.push_back(makePtr<GeluApproximationSubGraph>());
subgraphs.push_back(makePtr<LayerNormSubGraph>());
subgraphs.push_back(makePtr<GatherCastSubgraph>());
subgraphs.push_back(makePtr<MulCastSubgraph>());
subgraphs.push_back(makePtr<UpsampleSubgraph>());
subgraphs.push_back(makePtr<ResizeSubgraph1>());
subgraphs.push_back(makePtr<ResizeSubgraph2>());

41
modules/dnn/test/test_layers.cpp
View File

@@ -2347,6 +2347,47 @@ TEST(Layer_Size, onnx_0d_scalar)
EXPECT_EQ(outs[0].at<int64_t>(0), 1);
}
TEST(Layer_GatherCast, preserves_float_cast)
{
const std::string modelname = findDataFile("dnn/onnx/models/gather_cast_float.onnx", true);
Net net = readNetFromONNX(modelname, ENGINE_OPENCV);
ASSERT_FALSE(net.empty());
ASSERT_TRUE(net.getMainGraph());
int inputShape[] = {2, 3};
Mat input(2, inputShape, CV_32F, Scalar(0));
net.setInput(input, "input");
std::vector<Mat> outputs;
net.forward(outputs, std::vector<String>{"output"});
ASSERT_EQ(outputs.size(), 1u);
EXPECT_EQ(outputs[0].total(), (size_t)1);
EXPECT_EQ(outputs[0].type(), CV_32F);
EXPECT_FLOAT_EQ(outputs[0].ptr<float>()[0], 2.f);
}
TEST(Layer_MulCast, preserves_float_cast)
{
const std::string modelname = findDataFile("dnn/onnx/models/mul_cast_float.onnx", true);
Net net = readNetFromONNX(modelname, ENGINE_OPENCV);
ASSERT_FALSE(net.empty());
ASSERT_TRUE(net.getMainGraph());
int inputShape[] = {2, 3};
Mat input(2, inputShape, CV_32F, Scalar(0));
net.setInput(input, "input");
std::vector<Mat> outputs;
net.forward(outputs, std::vector<String>{"output"});
ASSERT_EQ(outputs.size(), 1u);
EXPECT_EQ(outputs[0].total(), (size_t)2);
EXPECT_EQ(outputs[0].type(), CV_32F);
EXPECT_FLOAT_EQ(outputs[0].ptr<float>()[0], 2.f);
EXPECT_FLOAT_EQ(outputs[0].ptr<float>()[1], 2.f);
}
TEST(ConvolutionWinograd, Accuracy)
{
Mat weights({2, 1, 3, 3}, CV_32F);