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Merge pull request #22199 from zihaomu:bug_fix_22195

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DNN: Reduce Layer (add dynamic batch and ReduceSum support)
pull/22366/head
Alexander Smorkalov 3 years ago committed by GitHub
parent cde18648dc d4640f4647
commit bb71cb200e
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5 changed files with 82 additions and 37 deletions
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  1. 3
      modules/dnn/include/opencv2/dnn/all_layers.hpp
  2. 29
      modules/dnn/src/int8layers/reduce_layer.cpp
  3. 28
      modules/dnn/src/layers/reduce_layer.cpp
  4. 58
      modules/dnn/src/onnx/onnx_importer.cpp
  5. 1
      modules/dnn/test/test_onnx_importer.cpp

3
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -334,7 +334,8 @@ CV__DNN_INLINE_NS_BEGIN
{
public:
int reduceType;
std::vector<size_t> reduceDims;
// reduceDims contains the dimensions that need to be reduced, targetDims is the target output dimension.
std::vector<size_t> reduceDims, targetDims;
static Ptr<ReduceLayer> create(const LayerParams& params);
};

29
modules/dnn/src/int8layers/reduce_layer.cpp
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@ -38,6 +38,15 @@ public:
{
reduceDims[i] = tempDims.get<int>(i);
}
CV_Assert(params.has("target_dims"));
tempDims = params.get("target_dims");
n = tempDims.size();
targetDims.resize(n);
for (i = 0; i < n; i++)
{
targetDims[i] = tempDims.get<int>(i);
}
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
@ -161,18 +170,30 @@ public:
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() > 0);
CV_Assert(reduceDims.size() != 0 && inputs[0].size() >= reduceDims.size());
CV_Assert( reduceDims.size() !=0 && targetDims.size() != 0 && inputs[0].size() >= reduceDims.size());
std::vector<int> outShape;
// outShapeTmp can save the right number of `total(outShapeTmp)`. And the outShape is used as the final output shape.
std::vector<int> outShapeTmp, outShape;
outShape.assign(targetDims.begin(), targetDims.end());
if (inputs[0].size() == reduceDims.size())
outShape.push_back(1);
outShapeTmp.push_back(1);
else
{
for (int i = 0; i < inputs[0].size() - reduceDims.size(); i++)
{
outShape.push_back(inputs[0][i]);
outShapeTmp.push_back(inputs[0][i]);
}
}
// Support dynamic shape of Batch size.
// Note that: when there are multiple dynamic inputs, we will give an error.
if (total(outShape) != total(outShapeTmp))
{
if (outShape[0] != outShapeTmp[0])
outShape[0] = outShapeTmp[0];
}
CV_Assert(total(outShape) == total(outShapeTmp));
outputs.assign(1, outShape);
return false;

28
modules/dnn/src/layers/reduce_layer.cpp
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@ -61,6 +61,15 @@ public:
{
reduceDims[i] = tempDims.get<int>(i);
}
CV_Assert(params.has("target_dims"));
tempDims = params.get("target_dims");
n = tempDims.size();
targetDims.resize(n);
for (i = 0; i < n; i++)
{
targetDims[i] = tempDims.get<int>(i);
}
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
@ -325,18 +334,29 @@ public:
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() > 0);
CV_Assert(reduceDims.size() != 0 && inputs[0].size() >= reduceDims.size());
CV_Assert( reduceDims.size() !=0 && targetDims.size() != 0 && inputs[0].size() >= reduceDims.size());
std::vector<int> outShape;
// outShapeTmp can save the right number of `total(outShapeTmp)`. And the outShape is used as the final output shape.
std::vector<int> outShapeTmp, outShape;
outShape.assign(targetDims.begin(), targetDims.end());
if (inputs[0].size() == reduceDims.size())
outShape.push_back(1);
outShapeTmp.push_back(1);
else
{
for (int i = 0; i < inputs[0].size() - reduceDims.size(); i++)
{
outShape.push_back(inputs[0][i]);
outShapeTmp.push_back(inputs[0][i]);
}
}
// Support dynamic shape of Batch size.
// Note that: when there are multiple dynamic inputs, we will give an error.
if (total(outShape) != total(outShapeTmp) && outShape[0] != outShapeTmp[0])
{
outShape[0] = outShapeTmp[0];
}
CV_Assert(total(outShape) == total(outShapeTmp));
outputs.assign(1, outShape);
return false;

58
modules/dnn/src/onnx/onnx_importer.cpp
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@ -1203,36 +1203,47 @@ 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(axesMat.at<int>(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"))
{
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
{
shouldDelete[i] = true;
for (int i = 0; i < inpShape.size(); i++)
{
shouldDelete[i] = true;
}
}
}
MatShape targetShape;
std::vector<int> targetShape;
for (int i = 0; i < inpShape.size(); ++i)
{
if (!shouldDelete[i])
@ -1302,19 +1313,10 @@ void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::Node
}
}
LayerParams reduceLp = layerParams;
reduceLp.name = layerParams.name + "/reduce";
CV_Assert(layer_id.find(reduceLp.name) == layer_id.end());
reduceLp.set("deleted_dims", DictValue::arrayInt(&deletedDims[0], deletedDims.size()));
layerParams.set("deleted_dims", DictValue::arrayInt(&deletedDims[0], deletedDims.size()));
layerParams.set("target_dims", DictValue::arrayInt(&targetShape[0], targetShape.size()));
node_proto.set_input(0, inputString);
node_proto.set_output(0, reduceLp.name);
addLayer(reduceLp, node_proto);
layerParams.type = (depth == CV_8S) ? "ReshapeInt8" : "Reshape";
layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size()));
node_proto.set_input(0, node_proto.output(0));
node_proto.set_output(0, output_name);
addLayer(layerParams, node_proto);

1
modules/dnn/test/test_onnx_importer.cpp
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@ -411,6 +411,7 @@ TEST_P(Test_ONNX_layers, ReduceMean)
TEST_P(Test_ONNX_layers, ReduceSum)
{
testONNXModels("reduce_sum");
testONNXModels("reduce_sum_axis_dynamic_batch");
}
TEST_P(Test_ONNX_layers, ReduceMax)

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