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support ReduceSum with two input and dynamic shape batch size in ReduceLayer.

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This commit is contained in:
Zihao Mu 2022-07-13 13:46:16 +08:00
parent 139c443770
commit 1b8fba8e26
2 changed files with 40 additions and 16 deletions
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54
modules/dnn/src/onnx/onnx_importer.cpp
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@ -1180,32 +1180,43 @@ void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::Node
layerParams.set("reduce", reduceType);
bool keepdims = layerParams.get<int>("keepdims", 1) == 1;
if (layer_type == "ReduceSum" && node_proto.input_size() == 2)
{
// TODO support the opset 13 of ReduceSum.
// in opset 13, the ReduceSum has two input, it takes axes as input instead of attribute
// details:https://github.com/onnx/onnx/issues/3420#issuecomment-844295687
CV_Error(Error::StsNotImplemented, "Unsupported " + layer_type + " operation of opset 13, please try to "
"re-export the onnx model with opset 11.");
}
MatShape inpShape = outShapes[node_proto.input(0)];
std::vector<bool> shouldDelete(inpShape.size(), false);
if (layerParams.has("axes"))
if (layer_type == "ReduceSum" && node_proto.input_size() == 2)
{
DictValue axes = layerParams.get("axes");
for (int i = 0; i < axes.size(); i++)
if (constBlobs.find(node_proto.input(1)) != constBlobs.end())
{
int axis = normalize_axis(axes.get<int>(i), inpShape.size());
shouldDelete[axis] = true;
Mat axesMat = getBlob(node_proto, 1);
int axesNum = axesMat.total();
for (int i = 0; i < axesNum; i++)
{
int axis = normalize_axis(static_cast<int>(axesMat.at<float>(i)), inpShape.size());
shouldDelete[axis] = true;
}
}
else
// in opset 13, the ReduceSum has two input, it takes axes as input instead of attribute
// details:https://github.com/onnx/onnx/issues/3420#issuecomment-844295687
CV_Error(Error::StsNotImplemented, "Non-constant axis values in ReduceSum are not supported.");
}
else
{
for (int i = 0; i < inpShape.size(); i++)
if (layerParams.has("axes"))
{
shouldDelete[i] = true;
DictValue axes = layerParams.get("axes");
for (int i = 0; i < axes.size(); i++)
{
int axis = normalize_axis(axes.get<int>(i), inpShape.size());
shouldDelete[axis] = true;
}
}
else
{
for (int i = 0; i < inpShape.size(); i++)
{
shouldDelete[i] = true;
}
}
}
@ -1291,6 +1302,17 @@ void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::Node
layerParams.type = (depth == CV_8S) ? "ReshapeInt8" : "Reshape";
layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size()));
// Set batchsize dim as dynamic to be compatible with batch size >= 2.
if (targetShape[0] == 1 && targetShape.size() > 1)
{
std::vector<int> dynamicAxes = {0}; // The index of batchsize dim is 0.
std::vector<int> inputIndices = {0};
layerParams.set("has_dynamic_shapes", true);
layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size()));
layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size()));
}
node_proto.set_input(0, node_proto.output(0));
node_proto.set_output(0, output_name);

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

@ -411,6 +411,8 @@ TEST_P(Test_ONNX_layers, ReduceMean)
TEST_P(Test_ONNX_layers, ReduceSum)
{
testONNXModels("reduce_sum");
testONNXModels("reduce_sum_axis");
testONNXModels("reduce_sum_axis_dynamic_batch");
}
TEST_P(Test_ONNX_layers, ReduceMax)