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dnn(onnx): debug dump of inputs/outputs/initializers in importer

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pull/21335/head
Alexander Alekhin 3 years ago
parent f11ab4c569
commit ed4becf007
3 changed files with 99 additions and 32 deletions
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  1. 2
      modules/dnn/src/onnx/onnx_graph_simplifier.cpp
  2. 2
      modules/dnn/src/onnx/onnx_graph_simplifier.hpp
  3. 127
      modules/dnn/src/onnx/onnx_importer.cpp

2
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
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@ -636,7 +636,7 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
simplifySubgraphs(Ptr<ImportGraphWrapper>(new ONNXGraphWrapper(net)), subgraphs);
}
Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto)
Mat getMatFromTensor(const opencv_onnx::TensorProto& tensor_proto)
{
if (tensor_proto.raw_data().empty() && tensor_proto.float_data().empty() &&
tensor_proto.double_data().empty() && tensor_proto.int64_data().empty())

2
modules/dnn/src/onnx/onnx_graph_simplifier.hpp
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@ -31,7 +31,7 @@ void convertInt64ToInt32(const T1& src, T2& dst, int size)
}
}
Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto);
Mat getMatFromTensor(const opencv_onnx::TensorProto& tensor_proto);
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace dnn, namespace cv

127
modules/dnn/src/onnx/onnx_importer.cpp
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@ -10,7 +10,7 @@
#include <opencv2/core/utils/logger.defines.hpp>
#undef CV_LOG_STRIP_LEVEL
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE + 1
#include <opencv2/core/utils/logger.hpp>
#ifdef HAVE_PROTOBUF
@ -193,6 +193,53 @@ inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, co
}
}
static
void dumpValueInfoProto(int i, const opencv_onnx::ValueInfoProto& valueInfoProto, const std::string& prefix)
{
CV_Assert(valueInfoProto.has_name());
CV_Assert(valueInfoProto.has_type());
const opencv_onnx::TypeProto& typeProto = valueInfoProto.type();
CV_Assert(typeProto.has_tensor_type());
const opencv_onnx::TypeProto::Tensor& tensor = typeProto.tensor_type();
CV_Assert(tensor.has_shape());
const opencv_onnx::TensorShapeProto& tensorShape = tensor.shape();
int dim_size = tensorShape.dim_size();
CV_CheckGE(dim_size, 0, "");
MatShape shape(dim_size);
for (int j = 0; j < dim_size; ++j)
{
const opencv_onnx::TensorShapeProto_Dimension& dimension = tensorShape.dim(j);
if (dimension.has_dim_param())
{
CV_LOG_DEBUG(NULL, "DNN/ONNX: " << prefix << "[" << i << "] dim[" << j << "] = <" << dimension.dim_param() << "> (dynamic)");
}
// https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition
if (dimension.has_denotation())
{
CV_LOG_INFO(NULL, "DNN/ONNX: " << prefix << "[" << i << "] dim[" << j << "] denotation is '" << dimension.denotation() << "'");
}
shape[j] = dimension.dim_value();
}
CV_LOG_DEBUG(NULL, "DNN/ONNX: " << prefix << "[" << i << " as '" << valueInfoProto.name() << "'] shape=" << toString(shape));
}
static
void dumpTensorProto(int i, const opencv_onnx::TensorProto& tensorProto, const std::string& prefix)
{
if (utils::logging::getLogLevel() < utils::logging::LOG_LEVEL_VERBOSE)
return;
int dim_size = tensorProto.dims_size();
CV_CheckGE(dim_size, 0, "");
MatShape shape(dim_size);
for (int j = 0; j < dim_size; ++j)
{
int sz = static_cast<int>(tensorProto.dims(j));
shape[j] = sz;
}
CV_LOG_VERBOSE(NULL, 0, "DNN/ONNX: " << prefix << "[" << i << " as '" << tensorProto.name() << "'] shape=" << toString(shape) << " data_type=" << (int)tensorProto.data_type());
}
void releaseONNXTensor(opencv_onnx::TensorProto& tensor_proto)
{
if (!tensor_proto.raw_data().empty()) {
@ -233,17 +280,17 @@ void runLayer(LayerParams& params, const std::vector<Mat>& inputs,
std::map<std::string, Mat> ONNXImporter::getGraphTensors(
const opencv_onnx::GraphProto& graph_proto)
{
opencv_onnx::TensorProto tensor_proto;
std::map<std::string, Mat> layers_weights;
std::map<std::string, Mat> layers_weights;
for (int i = 0; i < graph_proto.initializer_size(); i++)
{
tensor_proto = graph_proto.initializer(i);
Mat mat = getMatFromTensor(tensor_proto);
releaseONNXTensor(tensor_proto);
layers_weights.insert(std::make_pair(tensor_proto.name(), mat));
}
return layers_weights;
for (int i = 0; i < graph_proto.initializer_size(); i++)
{
const opencv_onnx::TensorProto& tensor_proto = graph_proto.initializer(i);
dumpTensorProto(i, tensor_proto, "initializer");
Mat mat = getMatFromTensor(tensor_proto);
releaseONNXTensor(const_cast<opencv_onnx::TensorProto&>(tensor_proto)); // drop already loaded data
layers_weights.insert(std::make_pair(tensor_proto.name(), mat));
}
return layers_weights;
}
static DictValue parse(const ::google::protobuf::RepeatedField< ::google::protobuf::int64>& src) {
@ -549,6 +596,7 @@ void ONNXImporter::populateNet()
<< " model produced by '" << framework_name << "'"
<< (framework_version.empty() ? cv::String() : cv::format(":%s", framework_version.c_str()))
<< ". Number of nodes = " << graph_proto.node_size()
<< ", initializers = " << graph_proto.initializer_size()
<< ", inputs = " << graph_proto.input_size()
<< ", outputs = " << graph_proto.output_size()
);
@ -560,48 +608,67 @@ void ONNXImporter::populateNet()
const int layersSize = graph_proto.node_size();
CV_LOG_DEBUG(NULL, "DNN/ONNX: graph simplified to " << layersSize << " nodes");
constBlobs = getGraphTensors(graph_proto);
constBlobs = getGraphTensors(graph_proto); // scan GraphProto.initializer
std::vector<String> netInputs; // map with network inputs (without const blobs)
// Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
for (int i = 0; i < graph_proto.input_size(); ++i)
{
const opencv_onnx::ValueInfoProto& valueInfoProto = graph_proto.input(i);
CV_Assert(valueInfoProto.has_name());
const std::string& name = valueInfoProto.name();
CV_Assert(valueInfoProto.has_type());
opencv_onnx::TypeProto typeProto = valueInfoProto.type();
const opencv_onnx::TypeProto& typeProto = valueInfoProto.type();
CV_Assert(typeProto.has_tensor_type());
opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
const opencv_onnx::TypeProto::Tensor& tensor = typeProto.tensor_type();
CV_Assert(tensor.has_shape());
opencv_onnx::TensorShapeProto tensorShape = tensor.shape();
const opencv_onnx::TensorShapeProto& tensorShape = tensor.shape();
MatShape inpShape(tensorShape.dim_size());
for (int j = 0; j < inpShape.size(); ++j)
int dim_size = tensorShape.dim_size();
CV_CheckGE(dim_size, 0, ""); // some inputs are scalars (dims=0), e.g. in Test_ONNX_nets.Resnet34_kinetics test
MatShape inpShape(dim_size);
for (int j = 0; j < dim_size; ++j)
{
inpShape[j] = tensorShape.dim(j).dim_value();
const opencv_onnx::TensorShapeProto_Dimension& dimension = tensorShape.dim(j);
if (dimension.has_dim_param())
{
CV_LOG_DEBUG(NULL, "DNN/ONNX: input[" << i << "] dim[" << j << "] = <" << dimension.dim_param() << "> (dynamic)");
}
// https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition
if (dimension.has_denotation())
{
CV_LOG_INFO(NULL, "DNN/ONNX: input[" << i << "] dim[" << j << "] denotation is '" << dimension.denotation() << "'");
}
inpShape[j] = dimension.dim_value();
// NHW, NCHW(NHWC), NCDHW(NDHWC); do not set this flag if only N is dynamic
if (!tensorShape.dim(j).dim_param().empty() && !(j == 0 && inpShape.size() >= 3))
if (dimension.has_dim_param() && !(j == 0 && inpShape.size() >= 3))
{
hasDynamicShapes = true;
}
}
CV_LOG_DEBUG(NULL, "DNN/ONNX: input[" << i << "] shape=" << toString(inpShape));
if (!inpShape.empty() && !hasDynamicShapes) // FIXIT result is not reliable for models with multiple inputs
bool isInitialized = ((constBlobs.find(name) != constBlobs.end()));
CV_LOG_IF_DEBUG(NULL, !isInitialized, "DNN/ONNX: input[" << i << " as '" << name << "'] shape=" << toString(inpShape));
CV_LOG_IF_VERBOSE(NULL, 0, isInitialized, "DNN/ONNX: pre-initialized input[" << i << " as '" << name << "'] shape=" << toString(inpShape));
if (dim_size > 0 && !hasDynamicShapes) // FIXIT result is not reliable for models with multiple inputs
{
inpShape[0] = std::max(inpShape[0], 1); // It's OK to have undetermined batch size
}
outShapes[valueInfoProto.name()] = inpShape;
}
// create map with network inputs (without const blobs)
// fill map: push layer name, layer id and output id
std::vector<String> netInputs;
for (int j = 0; j < graph_proto.input_size(); j++)
{
const std::string& name = graph_proto.input(j).name();
if (constBlobs.find(name) == constBlobs.end()) {
// fill map: push layer name, layer id and output id
if (!isInitialized)
{
netInputs.push_back(name);
layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
}
}
dstNet.setInputsNames(netInputs);
// dump outputs
for (int i = 0; i < graph_proto.output_size(); ++i)
{
dumpValueInfoProto(i, graph_proto.output(i), "output");
}
for(int li = 0; li < layersSize; li++)
{
const opencv_onnx::NodeProto& node_proto = graph_proto.node(li);

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