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dnn: improve debug messages, add ONNX opset version

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pull/21322/head
Alexander Alekhin 3 years ago
parent 19ac54277b
commit cc02fcd889
6 changed files with 169 additions and 41 deletions
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  1. 114
      modules/dnn/src/dnn.cpp
  2. 37
      modules/dnn/src/dnn_common.hpp
  3. 2
      modules/dnn/src/onnx/onnx_graph_simplifier.hpp
  4. 54
      modules/dnn/src/onnx/onnx_importer.cpp
  5. 1
      modules/dnn/src/precomp.hpp
  6. 2
      modules/dnn/src/tensorflow/tf_graph_simplifier.hpp

114
modules/dnn/src/dnn.cpp
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@ -247,8 +247,6 @@ std::vector<Target> getAvailableTargets(Backend be)
namespace
{
typedef std::vector<MatShape> ShapesVec;
struct LayerShapes
{
ShapesVec in, out, internal;
@ -2981,20 +2979,24 @@ struct Net::Impl : public detail::NetImplBase
void getLayerShapesRecursively(int id, LayersShapesMap& inOutShapes)
{
std::vector<LayerPin>& inputLayerIds = layers[id].inputBlobsId;
CV_CheckGE(id, 0, "");
CV_CheckLT(id, (int)layers.size(), "");
LayerData& layerData = layers[id];
std::vector<LayerPin>& inputLayerIds = layerData.inputBlobsId;
LayerShapes& layerShapes = inOutShapes[id];
if (id == 0 && inOutShapes[id].in[0].empty())
if (id == 0 && layerShapes.in[0].empty())
{
if (!layers[0].outputBlobs.empty())
if (!layerData.outputBlobs.empty())
{
ShapesVec shapes;
for (int i = 0; i < layers[0].outputBlobs.size(); i++)
for (int i = 0; i < layerData.outputBlobs.size(); i++)
{
Mat& inp = layers[0].outputBlobs[i];
CV_Assert(inp.total());
Mat& inp = layerData.outputBlobs[i];
CV_Assert(!inp.empty());
shapes.push_back(shape(inp));
}
inOutShapes[0].in = shapes;
layerShapes.in = shapes;
}
else
{
@ -3010,17 +3012,17 @@ struct Net::Impl : public detail::NetImplBase
}
if (none)
{
inOutShapes[0].out.clear();
layerShapes.out.clear();
return;
}
else
{
inOutShapes[0].in = inputShapes;
layerShapes.in = inputShapes;
}
}
}
if (inOutShapes[id].in.empty())
if (layerShapes.in.empty())
{
for(int i = 0; i < inputLayerIds.size(); i++)
{
@ -3033,14 +3035,14 @@ struct Net::Impl : public detail::NetImplBase
getLayerShapesRecursively(layerId, inOutShapes);
}
const MatShape& shape = inOutShapes[layerId].out[inputLayerIds[i].oid];
inOutShapes[id].in.push_back(shape);
layerShapes.in.push_back(shape);
}
}
const ShapesVec& is = inOutShapes[id].in;
ShapesVec& os = inOutShapes[id].out;
ShapesVec& ints = inOutShapes[id].internal;
int requiredOutputs = layers[id].requiredOutputs.size();
Ptr<Layer> l = layers[id].getLayerInstance();
const ShapesVec& is = layerShapes.in;
ShapesVec& os = layerShapes.out;
ShapesVec& ints = layerShapes.internal;
int requiredOutputs = layerData.requiredOutputs.size();
Ptr<Layer> l = layerData.getLayerInstance();
CV_Assert(l);
bool layerSupportInPlace = false;
try
@ -3068,13 +3070,38 @@ struct Net::Impl : public detail::NetImplBase
CV_LOG_ERROR(NULL, "Exception message: " << e.what());
throw;
}
inOutShapes[id].supportInPlace = layerSupportInPlace;
layerShapes.supportInPlace = layerSupportInPlace;
for (int i = 0; i < ints.size(); i++)
CV_Assert(total(ints[i]) > 0);
try
{
for (int i = 0; i < ints.size(); i++)
CV_CheckGT(total(ints[i]), 0, "");
for (int i = 0; i < os.size(); i++)
CV_Assert(total(os[i]) > 0);
for (int i = 0; i < os.size(); i++)
CV_CheckGT(total(os[i]), 0, "");
}
catch (const cv::Exception& e)
{
CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() post validation failed." <<
" inputs=" << is.size() <<
" outputs=" << os.size() << "/" << requiredOutputs <<
" blobs=" << l->blobs.size() <<
" inplace=" << layerSupportInPlace);
for (size_t i = 0; i < is.size(); ++i)
{
CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i]));
}
for (size_t i = 0; i < os.size(); ++i)
{
CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i]));
}
for (size_t i = 0; i < l->blobs.size(); ++i)
{
CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i])));
}
CV_LOG_ERROR(NULL, "Exception message: " << e.what());
throw;
}
}
void getLayersShapes(const ShapesVec& netInputShapes,
@ -3102,42 +3129,57 @@ struct Net::Impl : public detail::NetImplBase
void updateLayersShapes()
{
CV_Assert(!layers[0].outputBlobs.empty());
CV_LOG_DEBUG(NULL, "updateLayersShapes() with layers.size=" << layers.size());
CV_Assert(netInputLayer);
DataLayer& inputLayer = *netInputLayer;
LayerData& inputLayerData = layers[0];
CV_Assert(inputLayerData.layerInstance.get() == &inputLayer);
CV_Assert(!inputLayerData.outputBlobs.empty());
ShapesVec inputShapes;
for(int i = 0; i < layers[0].outputBlobs.size(); i++)
for(int i = 0; i < inputLayerData.outputBlobs.size(); i++)
{
Mat& inp = layers[0].outputBlobs[i];
CV_Assert(inp.total());
if (preferableBackend == DNN_BACKEND_OPENCV &&
Mat& inp = inputLayerData.outputBlobs[i];
CV_Assert(!inp.empty());
if (preferableBackend == DNN_BACKEND_OPENCV && // FIXIT: wrong place for output allocation
preferableTarget == DNN_TARGET_OPENCL_FP16)
{
layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S);
inp.create(inp.dims, inp.size, CV_16S);
}
inputShapes.push_back(shape(inp));
}
CV_LOG_DEBUG(NULL, toString(inputShapes, "Network input shapes"));
LayersShapesMap layersShapes;
layersShapes[0].in = inputShapes;
for (MapIdToLayerData::iterator it = layers.begin();
it != layers.end(); it++)
{
int layerId = it->first;
std::vector<LayerPin>& inputLayerIds = it->second.inputBlobsId;
if (layersShapes[layerId].in.empty())
LayerData& layerData = it->second;
std::vector<LayerPin>& inputLayerIds = layerData.inputBlobsId;
LayerShapes& layerShapes = layersShapes[layerId];
CV_LOG_DEBUG(NULL, "layer " << layerId << ": [" << layerData.type << "]:(" << layerData.name << ") with inputs.size=" << inputLayerIds.size());
if (layerShapes.in.empty())
{
for(int i = 0; i < inputLayerIds.size(); i++)
{
int inputLayerId = inputLayerIds[i].lid;
const LayerPin& inputPin = inputLayerIds[i];
int inputLayerId = inputPin.lid;
CV_LOG_DEBUG(NULL, " input[" << i << "] " << inputLayerId << ":" << inputPin.oid << " as [" << layers[inputLayerId].type << "]:(" << layers[inputLayerId].name << ")");
LayersShapesMap::iterator inputIt = layersShapes.find(inputLayerId);
if(inputIt == layersShapes.end() || inputIt->second.out.empty())
if (inputIt == layersShapes.end() || inputIt->second.out.empty())
{
getLayerShapesRecursively(inputLayerId, layersShapes);
}
const MatShape& shape = layersShapes[inputLayerId].out[inputLayerIds[i].oid];
layersShapes[layerId].in.push_back(shape);
const MatShape& shape = layersShapes[inputLayerId].out[inputPin.oid];
layerShapes.in.push_back(shape);
}
it->second.layerInstance->updateMemoryShapes(layersShapes[layerId].in);
layerData.layerInstance->updateMemoryShapes(layerShapes.in);
}
CV_LOG_DEBUG(NULL, "Layer " << layerId << ": " << toString(layerShapes.in, "input shapes"));
CV_LOG_IF_DEBUG(NULL, !layerShapes.out.empty(), "Layer " << layerId << ": " << toString(layerShapes.out, "output shapes"));
CV_LOG_IF_DEBUG(NULL, !layerShapes.internal.empty(), "Layer " << layerId << ": " << toString(layerShapes.internal, "internal shapes"));
}
CV_LOG_DEBUG(NULL, "updateLayersShapes() - DONE");
}
LayerPin getLatestLayerPin(const std::vector<LayerPin>& pins)

37
modules/dnn/src/dnn_common.hpp
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@ -29,6 +29,43 @@ struct NetImplBase
} // namespace detail
typedef std::vector<MatShape> ShapesVec;
static inline std::string toString(const ShapesVec& shapes, const std::string& name = std::string())
{
std::ostringstream ss;
if (!name.empty())
ss << name << ' ';
ss << '[';
for(size_t i = 0, n = shapes.size(); i < n; ++i)
ss << ' ' << toString(shapes[i]);
ss << " ]";
return ss.str();
}
static inline std::string toString(const Mat& blob, const std::string& name = std::string())
{
std::ostringstream ss;
if (!name.empty())
ss << name << ' ';
if (blob.empty())
{
ss << "<empty>";
}
else if (blob.dims == 1)
{
Mat blob_ = blob;
blob_.dims = 2; // hack
ss << blob_.t();
}
else
{
ss << blob.reshape(1, 1);
}
return ss.str();
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

2
modules/dnn/src/onnx/onnx_graph_simplifier.hpp
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@ -8,8 +8,6 @@
#ifndef __OPENCV_DNN_ONNX_SIMPLIFIER_HPP__
#define __OPENCV_DNN_ONNX_SIMPLIFIER_HPP__
#include "../precomp.hpp"
#if defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wsuggest-override"

54
modules/dnn/src/onnx/onnx_importer.cpp
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@ -65,6 +65,7 @@ public:
ONNXImporter(Net& net, const char *onnxFile)
: dstNet(net), dispatch(buildDispatchMap())
, onnx_opset(0)
{
hasDynamicShapes = false;
CV_Assert(onnxFile);
@ -86,6 +87,7 @@ public:
ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer)
: dstNet(net), dispatch(buildDispatchMap())
, onnx_opset(0)
{
hasDynamicShapes = false;
CV_LOG_DEBUG(NULL, "DNN/ONNX: processing in-memory ONNX model (" << sizeBuffer << " bytes)");
@ -178,6 +180,9 @@ private:
const DispatchMap dispatch;
static const DispatchMap buildDispatchMap();
int onnx_opset; // OperatorSetIdProto for 'onnx' domain
void parseOperatorSet();
};
inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey)
@ -489,10 +494,45 @@ void ONNXImporter::addNegation(const LayerParams& layerParams, opencv_onnx::Node
void ONNXImporter::addConstant(const std::string& name, const Mat& blob)
{
CV_LOG_DEBUG(NULL, "DNN/ONNX: add constant '" << name << "' shape=" << toString(shape(blob)) << ": " << toString(blob));
constBlobs.insert(std::make_pair(name, blob));
outShapes.insert(std::make_pair(name, shape(blob)));
}
void ONNXImporter::parseOperatorSet()
{
int ir_version = model_proto.has_ir_version() ? static_cast<int>(model_proto.ir_version()) : -1;
if (ir_version < 3)
return;
int opset_size = model_proto.opset_import_size();
if (opset_size <= 0)
{
CV_LOG_INFO(NULL, "DNN/ONNX: missing opset information")
return;
}
for (int i = 0; i < opset_size; ++i)
{
const ::opencv_onnx::OperatorSetIdProto& opset_entry = model_proto.opset_import(i);
const std::string& domain = opset_entry.has_domain() ? opset_entry.domain() : std::string();
int version = opset_entry.has_version() ? opset_entry.version() : -1;
if (domain.empty() || domain == "ai.onnx")
{
// ONNX opset covered by specification: https://github.com/onnx/onnx/blob/master/docs/Operators.md
onnx_opset = std::max(onnx_opset, version);
}
else
{
// OpenCV don't know other opsets
// will fail later on unsupported node processing
CV_LOG_WARNING(NULL, "DNN/ONNX: unsupported opset[" << i << "]: domain='" << domain << "' version=" << version);
}
}
CV_LOG_INFO(NULL, "DNN/ONNX: ONNX opset version = " << onnx_opset);
}
void ONNXImporter::populateNet()
{
CV_Assert(model_proto.has_graph());
@ -513,6 +553,8 @@ void ONNXImporter::populateNet()
<< ", outputs = " << graph_proto.output_size()
);
parseOperatorSet();
simplifySubgraphs(graph_proto);
const int layersSize = graph_proto.node_size();
@ -539,7 +581,8 @@ void ONNXImporter::populateNet()
if (!tensorShape.dim(j).dim_param().empty() && !(j == 0 && inpShape.size() >= 3))
hasDynamicShapes = true;
}
if (!inpShape.empty() && !hasDynamicShapes)
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
{
inpShape[0] = std::max(inpShape[0], 1); // It's OK to have undetermined batch size
}
@ -573,6 +616,15 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto)
CV_Assert(node_proto.output_size() >= 1);
std::string name = node_proto.output(0);
const std::string& layer_type = node_proto.op_type();
const std::string& layer_type_domain = node_proto.has_domain() ? node_proto.domain() : std::string();
if (!layer_type_domain.empty() && layer_type_domain != "ai.onnx")
{
CV_LOG_WARNING(NULL, "DNN/ONNX: can't handle node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: "
<< cv::format("[%s@%s]:(%s)", layer_type.c_str(), layer_type_domain.c_str(), name.c_str())
);
CV_Error(Error::StsNotImplemented, cv::format("ONNX: unsupported domain: %s", layer_type_domain.c_str()));
}
CV_LOG_DEBUG(NULL, "DNN/ONNX: processing node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: "
<< cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str())
);

1
modules/dnn/src/precomp.hpp
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@ -60,5 +60,6 @@
#include <opencv2/core/utils/trace.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include "dnn_common.hpp"

2
modules/dnn/src/tensorflow/tf_graph_simplifier.hpp
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@ -8,8 +8,6 @@
#ifndef __OPENCV_DNN_TF_SIMPLIFIER_HPP__
#define __OPENCV_DNN_TF_SIMPLIFIER_HPP__
#include "../precomp.hpp"
#ifdef HAVE_PROTOBUF
#include "tf_io.hpp"

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