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Merge pull request #22290 from fengyuentau:naive_yolov7

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Support for YOLOv7 ONNX (not simplified)
This commit is contained in:
Alexander Smorkalov
2022-09-19 14:43:18 +03:00
committed by GitHub
parent 0ac67ee8d9 4aef9b1c93
commit 6aefb8e86f
2 changed files with 163 additions and 7 deletions
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modules/dnn/src/onnx/onnx_importer.cpp
+79 -7
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@@ -180,6 +180,7 @@ private:
void parseCumSum (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseElementWise (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseDepthToSpace (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseRange (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
void parseSimpleLayers (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
// Domain: com.microsoft
@@ -2427,9 +2428,6 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node
if (!haveVariables)
{
if (broadcast_axes.size() > 1)
CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input");
if (broadcast_axes.empty())
{
addConstant(output_name, getBlob(node_proto, 0));
@@ -2437,10 +2435,15 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node
}
Mat input = getBlob(node_proto, 0);
input = input.reshape(0, total(inpShape, 0, broadcast_axes[0]));
Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]);
output = output.reshape(0, targetShape);
addConstant(output_name, output);
MatShape subTargetShape = inpShape;
for (auto broadcast_axis : broadcast_axes)
{
subTargetShape[broadcast_axis] = targetShape[broadcast_axis];
input = input.reshape(0, total(inpShape, 0, broadcast_axis));
Mat output = cv::repeat(input, 1, subTargetShape[broadcast_axis]);
input = output.reshape(0, subTargetShape);
}
addConstant(output_name, input);
return;
}
@@ -2497,6 +2500,12 @@ void ONNXImporter::parseReshape(LayerParams& layerParams, const opencv_onnx::Nod
std::vector<Mat> inputs(1, getBlob(node_proto, 0)), outputs;
runLayer(layerParams, inputs, outputs);
addConstant(node_proto.output(0), outputs[0]);
if (constBlobsExtraInfo.find(node_proto.input(0)) != constBlobsExtraInfo.end())
{
const int real_ndims_input0 = getBlobExtraInfo(node_proto, 0).real_ndims;
if (real_ndims_input0 == 1 && blob.total() == 1 && blob.at<int>() == -1) // 1D tensor as input0 (data), and shape is -1
constBlobsExtraInfo.insert(std::make_pair(node_proto.output(0), TensorInfo(1)));
}
return;
}
}
@@ -2548,7 +2557,14 @@ void ONNXImporter::parseShape(LayerParams& layerParams, const opencv_onnx::NodeP
CV_Assert(shapeIt != outShapes.end());
const MatShape& inpShape = shapeIt->second;
bool isInput1D = false;
if (constBlobsExtraInfo.find(node_proto.input(0)) != constBlobsExtraInfo.end())
if (getBlobExtraInfo(node_proto, 0).real_ndims == 1)
isInput1D = true;
int dims = static_cast<int>(inpShape.size());
if (isInput1D)
dims = 1;
Mat shapeMat(dims, 1, CV_32S);
bool isDynamicShape = false;
for (int j = 0; j < dims; ++j)
@@ -3080,8 +3096,63 @@ void ONNXImporter::parseDepthToSpace(LayerParams& layerParams, const opencv_onnx
addLayer(layerParams, node_proto);
}
// Currently we only support range with all constant inputs
void ONNXImporter::parseRange(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
CV_Assert(node_proto.input_size() == 3); // 0 - start, 1 - limit, 2 - delta
layerParams.type = "Range";
std::vector<int> const_id;
for (int i = 0; i < node_proto.input_size(); i++)
if (layer_id.find(node_proto.input(i)) == layer_id.end())
const_id.push_back(i);
// only supports the case which all inputs are constant
CV_Assert(const_id.size() == 3);
Mat startMat = getBlob(node_proto, 0);
CV_Assert(startMat.type() == CV_32SC1);
int start = startMat.at<int>(0);
Mat limitMat = getBlob(node_proto, 1);
CV_Assert(limitMat.type() == CV_32SC1);
int limit = limitMat.at<int>(0);
Mat deltaMat = getBlob(node_proto, 2);
CV_Assert(deltaMat.type() == CV_32SC1);
int delta = deltaMat.at<int>(0);
int number_of_elements = std::max(int(std::ceil((limit - start) / delta)), 0);
Mat r(number_of_elements, 1, CV_32SC1);
for (int i = 0; i < number_of_elements; i++)
{
r.at<int>(i) = start + (i * delta);
}
addConstant(node_proto.output(0), r);
constBlobsExtraInfo.insert(std::make_pair(node_proto.output(0), TensorInfo(1)));
}
void ONNXImporter::parseSimpleLayers(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{
bool is_all_input_const = true;
for (int i = 0; i < node_proto.input_size(); i++)
{
if (layer_id.find(node_proto.input(i)) != layer_id.end())
{
is_all_input_const = false;
break;
}
}
if (is_all_input_const && node_proto.output_size() == 1)
{
std::vector<Mat> input, output;
for (int i = 0; i < node_proto.input_size(); i++)
input.push_back(getBlob(node_proto, i));
runLayer(layerParams, input, output);
addConstant(node_proto.output(0), output[0]);
return;
}
for (int j = 0; j < node_proto.input_size(); j++) {
if (layer_id.find(node_proto.input(j)) == layer_id.end())
layerParams.blobs.push_back(getBlob(node_proto, j));
@@ -3685,6 +3756,7 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version)
dispatch["Equal"] = dispatch["Greater"] = dispatch["Less"] = dispatch["Pow"] = dispatch["Add"] =
dispatch["Sub"] = dispatch["Mul"] = dispatch["Div"] = &ONNXImporter::parseElementWise;
dispatch["Sum"] = dispatch["Min"] = dispatch["Max"] = &ONNXImporter::parseElementWise;
dispatch["Range"] = &ONNXImporter::parseRange;
std::vector<std::string> simpleLayers{"Acos", "Acosh", "Asin", "Asinh", "Atan", "Atanh", "Ceil", "Celu", "Cos",
"Cosh", "Dropout", "Erf", "Exp", "Floor", "HardSigmoid", "HardSwish",