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ModelImporter.cpp
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ModelImporter.cpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#include "ModelImporter.hpp"
#include "OnnxAttrs.hpp"
#include "onnx2trt_utils.hpp"
#include "onnx_utils.hpp"
#include "toposort.hpp"
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>
#include <limits>
#include <functional>
#include <unordered_set>
#include <sys/stat.h>
namespace onnx2trt
{
// Helper for deserializing INetwork
Status setTensorLocations(
IImporterContext* ctx, const std::vector<std::string>& tensors, const std::vector<std::string>& locations)
{
ASSERT( (tensors.size() >= locations.size()) && "The size of tensors misaligns with the size of the attribute trt_outputs_loc.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
for (size_t i = 0; i < locations.size(); ++i)
{
std::string tensor = tensors.at(i);
std::string location = locations.at(i);
nvinfer1::TensorLocation loc
= location == "device" ? nvinfer1::TensorLocation::kDEVICE : nvinfer1::TensorLocation::kHOST;
if (ctx->tensorLocations().count(tensor) > 0)
{
ASSERT( (ctx->tensorLocations()[tensor] == loc) && "The tensor location cannot be changed.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
}
else
{
ctx->tensorLocations()[tensor] = loc;
}
}
return Status::success();
}
// Helper for deserializing INetwork
template <typename T>
Status setStringMap(
IImporterContext* ctx, const std::vector<std::string>& tensors, const std::vector<T>& data, string_map<T>& map)
{
ASSERT( (tensors.size() >= data.size()) && "The size of tensors misaligns with the size of the attribute trt_outputs_range_min/max.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
for (size_t i = 0; i < data.size(); ++i)
{
std::string name = tensors.at(i);
T dataName = data.at(i);
if (map.count(name) > 0)
{
ASSERT( (map[name] == dataName) && "The order of tensorRangeMin/Max in context misaligns with the order of the attribute trt_outputs_range_min/max.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
}
else
{
map[name] = dataName;
}
}
return Status::success();
}
//! Make error explanation from TensorRT error recorder.
static std::string makeErrorExplanation(IImporterContext* ctx, const std::string& nodeName)
{
std::ostringstream result;
result << "Invalid Node - " << nodeName;
if (auto* errorRecorder = ctx->getErrorRecorder())
{
// Append information that might help the user understand the error.
const int32_t nbErrors = errorRecorder->getNbErrors();
for (int32_t i = 0; i < nbErrors; ++i)
{
result << "\n" << errorRecorder->getErrorDesc(i);
}
}
return result.str();
}
//! Make error explanation from an exception.
static std::string makeErrorExplanation(const std::exception& e, const std::string& nodeName)
{
std::ostringstream result;
result << "Invalid Node - " << nodeName << "\n" << e.what();
return result.str();
}
Status parseGraph(IImporterContext* ctx, const ::ONNX_NAMESPACE::GraphProto& graph, bool deserializingINetwork, int* currentNode)
{
// Import initializers.
for (const ::ONNX_NAMESPACE::TensorProto& initializer : graph.initializer())
{
LOG_VERBOSE("Importing initializer: " << initializer.name());
ShapedWeights weights;
ASSERT(convertOnnxWeights(initializer, &weights, ctx) && "Failed to import initializer.", ErrorCode::kUNSUPPORTED_NODE);
ctx->registerTensor(TensorOrWeights{std::move(weights)}, initializer.name());
}
std::vector<size_t> topoOrder;
ASSERT(toposort(graph.node(), &topoOrder) && "Failed to sort the model topologically.", ErrorCode::kINVALID_GRAPH);
const string_map<NodeImporter>& opImporters = getBuiltinOpImporterMap();
for (const auto& nodeIndex : topoOrder)
{
if (currentNode)
{
*currentNode = nodeIndex;
}
const auto& node = graph.node(nodeIndex);
const std::string& nodeName = getNodeName(node);
LOG_VERBOSE("Parsing node: " << nodeName << " [" << node.op_type() << "]");
// Assemble node inputs. These may come from outside the subgraph.
std::vector<TensorOrWeights> nodeInputs;
std::ostringstream ssInputs{};
ssInputs << nodeName << " [" << node.op_type() << "] inputs: ";
for (const auto& inputName : node.input())
{
// Empty input names indicate optional inputs which have not been supplied.
if (inputName.empty())
{
nodeInputs.emplace_back(nullptr);
ssInputs << "[optional input, not set], ";
}
else
{
LOG_VERBOSE("Searching for input: " << inputName);
ASSERT( (ctx->tensors().count(inputName)) && "Node input was not registered.", ErrorCode::kINVALID_GRAPH);
nodeInputs.push_back(ctx->tensors().at(inputName));
ssInputs << "[" << inputName << " -> " << nodeInputs.back().shape() << "[" << nodeInputs.back().getType() << "]" <<"], ";
}
}
LOG_VERBOSE(ssInputs.str());
// Dispatch to appropriate converter.
const NodeImporter* importFunc{nullptr};
if (opImporters.count(node.op_type()))
{
importFunc = &opImporters.at(node.op_type());
}
else
{
LOG_INFO("No importer registered for op: " << node.op_type() << ". Attempting to import as plugin.");
importFunc = &opImporters.at("FallbackPluginImporter");
}
std::vector<TensorOrWeights> outputs;
try
{
GET_VALUE((*importFunc)(ctx, node, nodeInputs), &outputs);
}
catch (const std::exception& e)
{
return MAKE_ERROR(makeErrorExplanation(e, nodeName), ErrorCode::kINVALID_NODE);
}
if (ctx->hasError())
{
return MAKE_ERROR(makeErrorExplanation(ctx, nodeName), ErrorCode::kINVALID_NODE);
}
for (const auto& output : outputs)
{
if (output.is_tensor())
{
// check that we can resolve output dims
// in the future we may have a network/layer.validate() which will help with that as well
output.tensor().getDimensions();
if (ctx->hasError())
{
return MAKE_ERROR(makeErrorExplanation(ctx, nodeName), ErrorCode::kINVALID_NODE);
}
}
}
if (deserializingINetwork)
{
OnnxAttrs attrs(node, ctx);
// Tensor locations, dynamic ranges and layer precisions will be set after parsing the network
std::vector<std::string> outputsLocation = attrs.get<std::vector<std::string>>("trt_outputs_loc", {});
std::vector<std::string> outputsVec(node.output().begin(), node.output().end());
std::vector<std::string> layerName{nodeName};
CHECK(setTensorLocations(ctx, outputsVec, outputsLocation));
auto outputsRangeMin = attrs.get<std::vector<float>>("trt_outputs_range_min", {});
CHECK(setStringMap<float>(ctx, outputsVec, outputsRangeMin, ctx->tensorRangeMins()));
auto outputsRangeMax = attrs.get<std::vector<float>>("trt_outputs_range_max", {});
CHECK(setStringMap<float>(ctx, outputsVec, outputsRangeMax, ctx->tensorRangeMaxes()));
if (attrs.count("trt_layer_precision"))
{
std::vector<nvinfer1::DataType> layerPrecision{attrs.get<nvinfer1::DataType>("trt_layer_precision")};
CHECK(setStringMap<nvinfer1::DataType>(ctx, layerName, layerPrecision, ctx->layerPrecisions()));
}
}
// Set output names and register outputs with the context.
std::ostringstream ssOutputs{};
ssOutputs << nodeName << " [" << node.op_type() << "] outputs: ";
for (int i = 0; i < node.output().size(); ++i)
{
const auto& outputName = node.output(i);
auto& output = outputs.at(i);
ssOutputs << "[" << outputName << " -> " << output.shape() << "[" << output.getType() << "]" << "], ";
// Note: This condition is to allow ONNX outputs to be ignored
// Always register output weights (even empty ones) as it may be mapped to an unused input
if ((output || output.is_weights()) && !outputName.empty())
{
ctx->registerTensor(std::move(output), outputName);
}
}
LOG_VERBOSE(ssOutputs.str());
}
return Status::success();
}
Status importInput(ImporterContext* ctx, ::ONNX_NAMESPACE::ValueInfoProto const& input, nvinfer1::ITensor** tensor,
std::vector<NamedDimension>& namedDims)
{
auto const& onnxDtype = input.type().tensor_type();
nvinfer1::DataType trtDtype;
ASSERT_INPUT(convertDtype(onnxDtype.elem_type(), &trtDtype) && "Failed to convert ONNX date type to TensorRT data type.", ErrorCode::kUNSUPPORTED_NODE, input.name());
nvinfer1::Dims trt_dims;
size_t const oldNbNamedDimensions = namedDims.size();
ASSERT_INPUT(convertOnnxDims(onnxDtype.shape().dim(), trt_dims, namedDims) && "Failed to convert ONNX dimensions to TensorRT dimensions.", ErrorCode::kUNSUPPORTED_GRAPH, input.name());
nvinfer1::ITensor* userInput = ctx->getUserInput(input.name().c_str());
if (userInput)
{
ASSERT_INPUT(userInput && "User input is missing.", ErrorCode::kINVALID_VALUE, input.name());
// Intentionally don't check dimensions/dtype here so that users can change the input shape/type if
// they want to. However, equalities implied by dimension names are nonetheless respected.
*tensor = userInput;
}
else
{
LOG_VERBOSE(
"Adding network input: " << input.name() << " with dtype: " << trtDtype << ", dimensions: " << trt_dims);
*tensor = ctx->network()->addInput(input.name().c_str(), trtDtype, trt_dims);
ASSERT_INPUT(*tensor && "Failed to add input to the network.", ErrorCode::kUNSUPPORTED_NODE, input.name());
}
// Fill in field `tensor` for any dimensions that had names in the ONNX.
for (auto i = oldNbNamedDimensions; i < namedDims.size(); ++i)
{
namedDims[i].tensor = *tensor;
}
return Status::success();
}
//! Add equality assertions for dimensions with the same name.
static Status assertDimsWithSameNameAreEqual(ImporterContext* ctx, std::vector<NamedDimension>& namedDims)
{
// Cache for IShapeLayer
std::unordered_map<nvinfer1::ITensor const*, nvinfer1::IShapeLayer*> shapeMap;
// Sort records by name of dimension, using stable_sort for reproducibility.
std::stable_sort(namedDims.begin(), namedDims.end(),
[](const NamedDimension& x, const NamedDimension& y) { return x.dimParam < y.dimParam; });
// Each loop iteration covers a sequence of named dimensions with the same name.
// For each sequence, add IAssertionLayers that assert that the values are equal.
// TensorRT knows about transitive closure of equality, so just add the assertions
// for adjacent records.
decltype(namedDims.begin()) j;
for (auto i = namedDims.begin(); i < namedDims.end(); i = j)
{
// Walk j forward so that [i,j) is indices of named dimensions with the same name.
j = i;
do
{
++j;
} while (j != namedDims.end() && j->dimParam == i->dimParam);
if (j - i < 2)
{
// Single occurrence of name is uninteresting.
continue;
}
std::ostringstream message;
message << "For input: '" << i->tensor->getName()
<< "' all named dimensions that share the same name must be equal. Note: Named dimensions were present on the following axes: ";
// prev is the current end of the daisy chain.
nvinfer1::ITensor* prev = nullptr;
for (auto k = i; k < j; ++k)
{
message << (prev ? ", " : "") << k->index << " (name: "
<< "'" << k->dimParam << "')";
// Create ITensor "next" with dimension length for record k.
auto& shape = shapeMap[k->tensor];
if (shape == nullptr)
{
shape = ctx->network()->addShape(*k->tensor);
}
auto* slice = ctx->network()->addSlice(*shape->getOutput(0), {1, {k->index}}, {1, {1}}, {1, {1}});
nvinfer1::ITensor* next = slice->getOutput(0);
if (prev)
{
// Add a link to the chain.
auto* equal = ctx->network()->addElementWise(*prev, *next, nvinfer1::ElementWiseOperation::kEQUAL);
auto* assertion = ctx->network()->addAssertion(*equal->getOutput(0), message.str().c_str());
ASSERT(assertion != nullptr && "addAssertion failed", ErrorCode::kMODEL_DESERIALIZE_FAILED);
}
prev = next;
}
}
return Status::success();
}
Status importInputs(ImporterContext* ctx, ::ONNX_NAMESPACE::GraphProto const& graph,
string_map<TensorOrWeights>* tensors)
{
// The weights come from the Initializer list in onnx graph
// Initializers are not really network inputs, so they need to be excluded.
std::unordered_set<std::string> initializers{};
for (const ::ONNX_NAMESPACE::TensorProto& initializer : graph.initializer())
{
initializers.emplace(initializer.name());
}
std::vector<NamedDimension> namedDims;
for (const ::ONNX_NAMESPACE::ValueInfoProto& input : graph.input())
{
TensorOrWeights tensor;
if (!initializers.count(input.name()))
{
nvinfer1::ITensor* tensor_ptr{nullptr};
CHECK(importInput(ctx, input, &tensor_ptr, namedDims));
tensor = tensor_ptr;
}
ctx->registerTensor(std::move(tensor), input.name());
}
return assertDimsWithSameNameAreEqual(ctx, namedDims);
}
Status deserialize_onnx_model(void const* serialized_onnx_model, size_t serialized_onnx_model_size,
bool is_serialized_as_text, ::ONNX_NAMESPACE::ModelProto* model)
{
google::protobuf::io::ArrayInputStream raw_input(serialized_onnx_model, serialized_onnx_model_size);
if (is_serialized_as_text)
{
ASSERT( (google::protobuf::TextFormat::Parse(&raw_input, model)) && "Failed to parse the ONNX model.", ErrorCode::kMODEL_DESERIALIZE_FAILED);
}
else
{
google::protobuf::io::CodedInputStream coded_input(&raw_input);
#if GOOGLE_PROTOBUF_VERSION >= 3011000
// Starting Protobuf 3.11 accepts only single parameter.
coded_input.SetTotalBytesLimit(std::numeric_limits<int>::max());
#else
// Note: This WARs the very low default size limit (64MB)
coded_input.SetTotalBytesLimit(std::numeric_limits<int>::max(), std::numeric_limits<int>::max() / 4);
#endif
ASSERT( (model->ParseFromCodedStream(&coded_input)) && "Failed to parse the ONNX model.", ErrorCode::kMODEL_DESERIALIZE_FAILED);
}
return Status::success();
}
Status deserialize_onnx_model(int fd, bool is_serialized_as_text, ::ONNX_NAMESPACE::ModelProto* model)
{
google::protobuf::io::FileInputStream raw_input(fd);
if (is_serialized_as_text)
{
ASSERT( (google::protobuf::TextFormat::Parse(&raw_input, model)) && "Failed to parse the ONNX model.", ErrorCode::kMODEL_DESERIALIZE_FAILED);
}
else
{
google::protobuf::io::CodedInputStream coded_input(&raw_input);
// Note: This WARs the very low default size limit (64MB)
#if GOOGLE_PROTOBUF_VERSION >= 3011000
// Starting Protobuf 3.11 accepts only single parameter.
coded_input.SetTotalBytesLimit(std::numeric_limits<int>::max());
#else
// Note: This WARs the very low default size limit (64MB)
coded_input.SetTotalBytesLimit(std::numeric_limits<int>::max(), std::numeric_limits<int>::max()/4);
#endif
ASSERT( (model->ParseFromCodedStream(&coded_input)) && "Failed to parse the ONNX model.", ErrorCode::kMODEL_DESERIALIZE_FAILED);
}
return Status::success();
}
bool ModelImporter::supportsModel(void const* serialized_onnx_model, size_t serialized_onnx_model_size,
SubGraphCollection_t& sub_graph_collection, const char* model_path)
{
::ONNX_NAMESPACE::ModelProto model;
bool is_serialized_as_text = false;
Status status
= deserialize_onnx_model(serialized_onnx_model, serialized_onnx_model_size, is_serialized_as_text, &model);
if (status.is_error())
{
_errors.push_back(status);
return false;
}
if (model_path)
{
_importer_ctx.setOnnxFileLocation(model_path);
}
bool allSupported{true};
// Parse the graph and see if we hit any parsing errors
allSupported = parse(serialized_onnx_model, serialized_onnx_model_size);
int error_node = -1;
std::string input_node{};
if (!allSupported)
{
int nerror = getNbErrors();
for (int i = 0; i < nerror; ++i)
{
nvonnxparser::IParserError const* error = getError(i);
if (error->node() != -1)
{
error_node = error->node();
allSupported = false;
}
// The node that we failed on is one of the input nodes (-1). Get the name of the input node
// that we failed on and remove all nodes that spawn out of it.
else
{
// Node name is extracted through error->file as all errors thrown on input nodes are wrapped
// around MAKE_INPUT_ERROR.
input_node = error->file();
}
}
}
auto* ctx = &_importer_ctx;
auto checkForInput = [&input_node, &ctx](::ONNX_NAMESPACE::NodeProto const& node) {
for (auto input : node.input())
{
if (input_node == input || ctx->loopTensors()[input_node] == input)
{
return true;
}
}
return false;
};
bool newSubGraph(true);
// Sort and partition supported subgraphs
std::vector<size_t> topological_order;
if (!toposort(model.graph().node(), &topological_order))
{
LOG_VERBOSE("Failed to sort model topologically, exiting ...");
return false;
}
for (int node_idx : topological_order)
{
::ONNX_NAMESPACE::NodeProto const& node = model.graph().node(node_idx);
// Add the node to the subgraph if:
// 1. There is an importer function registered for the operator type
// 2. It is not directly connected to an unsupported input
// 3. The importer function did not throw an assertion
bool registered = supportsOperator(node.op_type().c_str());
bool unsupportedInput = (input_node.empty()) ? false : checkForInput(node);
bool unsuccessfulParse = node_idx == error_node;
if (registered && !unsupportedInput && !unsuccessfulParse)
{
if (newSubGraph)
{
// If it is the beginning of a new subGraph, we start a new vector
sub_graph_collection.emplace_back();
// Mark all new graphs as "unknown"
sub_graph_collection.back().second = false;
newSubGraph = false;
}
// We add the new node to the last graph
sub_graph_collection.back().first.emplace_back(node_idx);
}
else
{
// This is not a supported node, reset newSubGraph
newSubGraph = true;
allSupported = false;
}
}
// Only mark the subgraph as supported if there is one supported subgraph.
if (allSupported)
{
sub_graph_collection.back().second = true;
}
return allSupported;
}
// Mark experimental ops as unsupported, mark plugin ops as supported
bool ModelImporter::supportsOperator(const char* op_name) const
{
auto is = [op_name](const char* name) { return std::strcmp(op_name, name) == 0; };
if (is("NonMaxSuppression"))
{
return false;
}
if (is("EfficientNMS_TRT") || is("PyramidROIAlign_TRT") || is("MultilevelCropAndResize_TRT")
|| is("DisentangledAttention_TRT"))
{
return true;
}
return _op_importers.count(op_name);
}
bool ModelImporter::parseWithWeightDescriptors(void const* serialized_onnx_model, size_t serialized_onnx_model_size)
{
_current_node = -1;
// TODO: This function (and its overload below) could do with some cleaning,
// particularly wrt error handling.
// Note: We store a copy of the model so that weight arrays will persist
_onnx_models.emplace_back();
::ONNX_NAMESPACE::ModelProto& model = _onnx_models.back();
bool is_serialized_as_text = false;
Status status
= deserialize_onnx_model(serialized_onnx_model, serialized_onnx_model_size, is_serialized_as_text, &model);
if (status.is_error())
{
_errors.push_back(status);
return false;
}
status = this->importModel(model);
if (status.is_error())
{
status.setNode(_current_node);
_errors.push_back(status);
return false;
}
return true;
}
bool ModelImporter::parse(void const* serialized_onnx_model, size_t serialized_onnx_model_size, const char* model_path)
{
auto* const ctx = &_importer_ctx;
if (ctx->network()->getNbLayers() > 0)
{
LOG_ERROR("Parse was called with a non-empty network definition");
return false;
}
if (model_path)
{
_importer_ctx.setOnnxFileLocation(model_path);
}
return this->parseWithWeightDescriptors(serialized_onnx_model, serialized_onnx_model_size);
}
Status ModelImporter::importModel(
::ONNX_NAMESPACE::ModelProto const& model)
{
ASSERT(!_importer_ctx.network()->hasImplicitBatchDimension() && "This version of the ONNX parser only supports TensorRT INetworkDefinitions with an explicit batch dimension. Please ensure the network was created using the EXPLICIT_BATCH NetworkDefinitionCreationFlag.", ErrorCode::kINVALID_VALUE);
auto* ctx = &_importer_ctx;
_importer_ctx.clearOpsets();
#if ENABLE_STD_PLUGIN
// Initialize plugin registry
initLibNvInferPlugins(static_cast<void*>(&ctx->logger()), "");
#endif // ENABLE_STD_PLUGIN
for (int i = 0; i < model.opset_import().size(); ++i)
{
std::string domain = model.opset_import(i).domain();
int64_t version = model.opset_import(i).version();
// TensorRT requires an ONNX graph to be generated with at least ai.onnx version 7.
// ONNX spec says that the default domain is either an empty string or is "ai.onnx".
if ((domain.empty() || domain == "ai.onnx") && version < 7)
{
LOG_WARNING("TensorRT supports ONNX graphs generated with at least opset 7. Models using older opsets are not guaranteed to work.");
}
_importer_ctx.addOpset(domain, version);
}
::ONNX_NAMESPACE::GraphProto const& graph = model.graph();
// Create a dummy tensors so that we can reserve output names. If the output names are encountered elsewhere
// in the graph, the ctx will know to make the names unique.
for (const ::ONNX_NAMESPACE::ValueInfoProto& output : graph.output())
{
_importer_ctx.registerTensor(TensorOrWeights{}, output.name());
}
_current_node = -1;
CHECK(importInputs(&_importer_ctx, graph, &_importer_ctx.tensors()));
CHECK(parseGraph(&_importer_ctx, graph, model.producer_name() == "TensorRT", &_current_node));
_current_node = -1;
// Mark outputs defined in the ONNX model (unless tensors are user-requested)
for (::ONNX_NAMESPACE::ValueInfoProto const& output : graph.output())
{
ASSERT((_importer_ctx.tensors().count(output.name())) && "The output tensor was not registered.",
ErrorCode::kINVALID_GRAPH);
nvinfer1::ITensor* output_tensor_ptr
= &convertToTensor(_importer_ctx.tensors().at(output.name()), &_importer_ctx);
LOG_VERBOSE("Marking " << output_tensor_ptr->getName() << " as output: " << output.name());
output_tensor_ptr->setName(output.name().c_str());
if (output_tensor_ptr->isNetworkInput())
{
// HACK WAR for TRT not allowing input == output
// TODO: Does this break things by changing the name of the input tensor?
output_tensor_ptr->setName(("__" + output.name()).c_str());
output_tensor_ptr = &identity(&_importer_ctx, output_tensor_ptr).tensor();
ASSERT(output_tensor_ptr && "Failed to add an Identity layer.", ErrorCode::kUNSUPPORTED_NODE);
output_tensor_ptr->setName(output.name().c_str());
}
nvinfer1::ITensor** user_output = _importer_ctx.getUserOutput(output.name().c_str());
if (!user_output)
{
_importer_ctx.network()->markOutput(*output_tensor_ptr);
nvinfer1::DataType output_trt_dtype;
ASSERT(convertDtype(output.type().tensor_type().elem_type(), &output_trt_dtype) && "Failed to convert ONNX date type to TensorRT data type.", ErrorCode::kUNSUPPORTED_NODE);
// For INT32 data type, output type must match tensor type
ASSERT( (output_tensor_ptr->getType() != nvinfer1::DataType::kINT32
|| output_trt_dtype == nvinfer1::DataType::kINT32) && "For INT32 tensors, the output type must also be INT32.",
ErrorCode::kUNSUPPORTED_NODE);
// Note: Without this, output type is always float32
output_tensor_ptr->setType(output_trt_dtype);
}
}
// Return user-requested output tensors
for (auto user_output_entry : _importer_ctx.getUserOutputs())
{
std::string user_output_name = user_output_entry.first;
nvinfer1::ITensor** user_output_ptr = user_output_entry.second;
ASSERT( (_importer_ctx.tensors().count(user_output_name)) && "The user-requested output was not registered.", ErrorCode::kINVALID_VALUE);
TensorOrWeights user_output = _importer_ctx.tensors().at(user_output_name);
ASSERT( (user_output.is_tensor()) && "The user-requested output must be a tensor.", ErrorCode::kINVALID_VALUE);
*user_output_ptr = &user_output.tensor();
}
if (model.producer_name() == "TensorRT")
{
// iterate over all tensors in the network and add them to "tensors" map
string_map<nvinfer1::ITensor*> tensors;
string_map<nvinfer1::ILayer*> layers;
for (int idx = 0; idx < _importer_ctx.network()->getNbInputs(); ++idx)
{
nvinfer1::ITensor* tensor = _importer_ctx.network()->getInput(idx);
if (tensor != nullptr)
{
tensors[tensor->getName()] = tensor;
}
}
for (int idx = 0; idx < _importer_ctx.network()->getNbOutputs(); ++idx)
{
nvinfer1::ITensor* tensor = _importer_ctx.network()->getOutput(idx);
if (tensor != nullptr)
{
tensors[tensor->getName()] = tensor;
}
}
for (int layerIdx = 0; layerIdx < _importer_ctx.network()->getNbLayers(); ++layerIdx)
{
nvinfer1::ILayer* layer = _importer_ctx.network()->getLayer(layerIdx);
for (int idx = 0; idx < layer->getNbInputs(); ++idx)
{
nvinfer1::ITensor* tensor = layer->getInput(idx);
if (tensor != nullptr)
{
tensors[tensor->getName()] = tensor;
}
}
for (int idx = 0; idx < layer->getNbOutputs(); ++idx)
{
nvinfer1::ITensor* tensor = layer->getOutput(idx);
if (tensor != nullptr)
{
tensors[tensor->getName()] = tensor;
}
}
layers[layer->getName()] = layer;
}
// Set locations for all tensors
for (auto const& tensor : ctx->tensorLocations())
{
ASSERT( (tensors.count(tensor.first) > 0) && "The tensor does not have an assigned location.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
tensors.at(tensor.first)->setLocation(tensor.second);
}
// Set dynamic range for all tensors
for (auto const& tensor : ctx->tensorRangeMins())
{
// if there's a min range, there must be a max range as well
ASSERT( (tensors.count(tensor.first) > 0) && "The tensor does not have an assigned location.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
if (!std::isnan(tensor.second))
{
tensors.at(tensor.first)->setDynamicRange(tensor.second, ctx->tensorRangeMaxes().at(tensor.first));
}
}
// Set precisions for all layers
for (auto const& layer : ctx->layerPrecisions())
{
ASSERT( (layers.count(layer.first) > 0) && "The layer does not have an assigned precision.", nvonnxparser::ErrorCode::kINVALID_GRAPH);
layers.at(layer.first)->setPrecision(layer.second);
}
}
return Status::success();
}
bool ModelImporter::parseFromFile(const char* onnxModelFile, int32_t verbosity)
{
auto* ctx = &_importer_ctx;
// Define S_ISREG macro for Windows
#if !defined(S_ISREG)
# define S_ISREG(mode) (((mode) & S_IFMT) == S_IFREG)
#endif
struct stat sb;
if (stat(onnxModelFile, &sb) == 0 && !S_ISREG(sb.st_mode))
{
LOG_ERROR("Input is not a regular file: " << onnxModelFile);
return false;
}
GOOGLE_PROTOBUF_VERIFY_VERSION;
::ONNX_NAMESPACE::ModelProto onnx_model;
const bool is_binary = ParseFromFile_WAR(&onnx_model, onnxModelFile);
if (!is_binary && !ParseFromTextFile(&onnx_model, onnxModelFile))
{
LOG_ERROR("Failed to parse ONNX model from file: " << onnxModelFile);
return false;
}
// Keep track of the absolute path to the ONNX file.
_importer_ctx.setOnnxFileLocation(onnxModelFile);
const int64_t opset_version = (onnx_model.opset_import().size() ? onnx_model.opset_import(0).version() : 0);
LOG_INFO("----------------------------------------------------------------");
LOG_INFO("Input filename: " << onnxModelFile);
LOG_INFO("ONNX IR version: " << onnx_ir_version_string(onnx_model.ir_version()));
LOG_INFO("Opset version: " << opset_version);
LOG_INFO("Producer name: " << onnx_model.producer_name());
LOG_INFO("Producer version: " << onnx_model.producer_version());
LOG_INFO("Domain: " << onnx_model.domain());
LOG_INFO("Model version: " << onnx_model.model_version());
LOG_INFO("Doc string: " << onnx_model.doc_string());
LOG_INFO("----------------------------------------------------------------");
{ //...Read input file, parse it
std::ifstream onnx_file(onnxModelFile, std::ios::binary | std::ios::ate);
auto const file_size = onnx_file.tellg();
onnx_file.seekg(0, std::ios::beg);
std::vector<char> onnx_buf(file_size);
if (!onnx_file.read(onnx_buf.data(), onnx_buf.size()))
{
LOG_ERROR("Failed to read from file: " << onnxModelFile);
return false;
}
if (!parse(onnx_buf.data(), onnx_buf.size()))
{
const int32_t nerror = getNbErrors();
for (int32_t i = 0; i < nerror; ++i)
{
nvonnxparser::IParserError const* error = getError(i);
if (error->node() != -1)
{
::ONNX_NAMESPACE::NodeProto const& node = onnx_model.graph().node(error->node());
LOG_ERROR("While parsing node number " << error->node() << " [" << node.op_type() << " -> \"" << node.output(0) << "\"" << "]:");
LOG_ERROR("--- Begin node ---");
LOG_ERROR(pretty_print_onnx_to_string(node));
LOG_ERROR("--- End node ---");
}
LOG_ERROR("ERROR: " << error->file() << ":" << error->line() << " In function " << error->func() << ":\n"
<< "[" << static_cast<int>(error->code()) << "] " << error->desc());
}
return false;
}
} //...End Reading input file, parsing it
return true;
}
} // namespace onnx2trt