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提交 5a6c358d 编写于 作者: H hangq
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fix onnx | mindir read protobuf bug in windows

上级 fd82e4f8
隐藏空白更改
内联 并排
Showing with 88 addition and 205 deletion
mindspore/lite/test/CMakeLists.txt
浏览文件 @ 5a6c358d
......@@ -230,6 +230,7 @@ if(BUILD_CONVERTER)
${TEST_LITE_SRC}
${TEST_CASE_TFLITE_PARSERS_SRC}
${TOP_DIR}/mindspore/core/utils/flags.cc
${LITE_DIR}/tools/common/protobuf_utils.cc
${LITE_DIR}/tools/converter/optimizer.cc
${LITE_DIR}/tools/converter/anf_transform.cc
${LITE_DIR}/tools/converter/graphdef_transform.cc
......
mindspore/lite/tools/anf_importer/import_from_protobuf.cc
浏览文件 @ 5a6c358d
......@@ -27,7 +27,6 @@
#include <vector>
#include "src/ops/primitive_c.h"
#include "frontend/operator/ops.h"
#include "google/protobuf/io/zero_copy_stream_impl.h"
#include "include/errorcode.h"
#include "ir/anf.h"
#include "ir/func_graph.h"
......@@ -37,6 +36,7 @@
#include "src/param_value_lite.h"
#include "tools/converter/parser/onnx/onnx.pb.h"
#include "utils/log_adapter.h"
#include "tools/common/protobuf_utils.h"
using string = std::string;
using int32 = int32_t;
......@@ -651,31 +651,11 @@ int AnfImporterFromProtobuf::Import(const schema::QuantType &quantType) {
}
onnx::ModelProto *AnfImporterFromProtobuf::ReadOnnxFromBinary(const std::string &model_path) {
std::unique_ptr<char[]> onnx_file(new (std::nothrow) char[PATH_MAX]{0});
#ifdef _WIN32
if (_fullpath(onnx_file.get(), model_path.c_str(), 1024) == nullptr) {
MS_LOG(ERROR) << "open file failed.";
return nullptr;
}
#else
if (realpath(model_path.c_str(), onnx_file.get()) == nullptr) {
MS_LOG(ERROR) << "open file failed.";
return nullptr;
}
#endif
int fd = open(onnx_file.get(), O_RDONLY);
google::protobuf::io::FileInputStream input(fd);
google::protobuf::io::CodedInputStream code_input(&input);
code_input.SetTotalBytesLimit(INT_MAX, 536870912);
auto onnx_model = new onnx::ModelProto;
bool ret = onnx_model->ParseFromCodedStream(&code_input);
if (!ret) {
MS_LOG(ERROR) << "load onnx file failed";
delete onnx_model;
if (ReadProtoFromBinaryFile((const char *)model_path.c_str(), onnx_model) != RET_OK) {
MS_LOG(ERROR) << "Read onnx model file failed, model path: " << model_path;
return nullptr;
}
(void)close(fd);
MS_LOG(INFO) << "enter ReadProtoFromBinary success!" << std::endl;
return onnx_model;
}
......
mindspore/lite/tools/converter/parser/caffe/caffe_parse_utils.cc → mindspore/lite/tools/common/protobuf_utils.cc
浏览文件 @ 5a6c358d
......@@ -14,7 +14,7 @@
* limitations under the License.
*/
#include "mindspore/lite/tools/converter/parser/caffe/caffe_parse_utils.h"
#include "tools/common/protobuf_utils.h"
#include <fstream>
#include <string>
#include "google/protobuf/io/zero_copy_stream_impl.h"
......@@ -37,15 +37,14 @@ bool ReadProtoFromCodedInputStream(google::protobuf::io::CodedInputStream *coded
return proto->ParseFromCodedStream(coded_stream);
}
STATUS ReadProtoFromText(const char *file,
google::protobuf::Message *message) {
STATUS ReadProtoFromText(const char *file, google::protobuf::Message *message) {
if (file == nullptr || message == nullptr) {
return RET_ERROR;
}
std::string realPath = RealPath(file);
if (realPath.empty()) {
MS_LOG(ERROR) << "Proto file path " << file <<" is not valid";
MS_LOG(ERROR) << "Proto file path " << file << " is not valid";
return RET_ERROR;
}
......@@ -67,8 +66,7 @@ STATUS ReadProtoFromText(const char *file,
return RET_OK;
}
STATUS ReadProtoFromBinaryFile(const char *file,
google::protobuf::Message *message) {
STATUS ReadProtoFromBinaryFile(const char *file, google::protobuf::Message *message) {
if (file == nullptr || message == nullptr) {
return RET_ERROR;
}
......@@ -100,4 +98,3 @@ STATUS ReadProtoFromBinaryFile(const char *file,
}
} // namespace lite
} // namespace mindspore
mindspore/lite/tools/converter/parser/caffe/caffe_parse_utils.h → mindspore/lite/tools/common/protobuf_utils.h
浏览文件 @ 5a6c358d
......@@ -29,13 +29,10 @@ namespace lite {
bool ReadProtoFromCodedInputStream(google::protobuf::io::CodedInputStream *coded_stream,
google::protobuf::Message *proto);
STATUS ReadProtoFromText(const char *file,
google::protobuf::Message *message);
STATUS ReadProtoFromText(const char *file, google::protobuf::Message *message);
STATUS ReadProtoFromBinaryFile(const char *file,
google::protobuf::Message *message);
STATUS ReadProtoFromBinaryFile(const char *file, google::protobuf::Message *message);
} // namespace lite
} // namespace mindspore
#endif // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_CAFFE_CAFFE_PARSE_UTILS_H_
mindspore/lite/tools/converter/CMakeLists.txt
浏览文件 @ 5a6c358d
......@@ -94,6 +94,7 @@ file(GLOB_RECURSE CONVERTER_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
${CMAKE_CURRENT_SOURCE_DIR}/../common/graph_util.cc
${CMAKE_CURRENT_SOURCE_DIR}/../common/node_util.cc
${CMAKE_CURRENT_SOURCE_DIR}/../common/tensor_util.cc
${CMAKE_CURRENT_SOURCE_DIR}/../common/protobuf_utils.cc
${CMAKE_CURRENT_SOURCE_DIR}/../common/flag_parser.cc
${CMAKE_CURRENT_SOURCE_DIR}/../common/storage.cc
${CMAKE_CURRENT_SOURCE_DIR}/../../src/ir/primitive_t_value.cc
......
mindspore/lite/tools/converter/parser/caffe/CMakeLists.txt
浏览文件 @ 5a6c358d
......@@ -15,7 +15,6 @@ add_library(caffe_parser_mid OBJECT
${CMAKE_CURRENT_SOURCE_DIR}/caffe_model_parser.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_node_parser.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_node_parser_registry.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_parse_utils.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_pooling_parser.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_power_parser.cc
${CMAKE_CURRENT_SOURCE_DIR}/caffe_prelu_parser.cc
......
mindspore/lite/tools/converter/parser/caffe/caffe_converter.cc
浏览文件 @ 5a6c358d
......@@ -15,7 +15,6 @@
*/
#include "mindspore/lite/tools/converter/parser/caffe/caffe_converter.h"
#include "mindspore/lite/tools/converter/parser/caffe/caffe_parse_utils.h"
namespace mindspore {
namespace lite {
......
mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.cc
浏览文件 @ 5a6c358d
......@@ -14,14 +14,14 @@
* limitations under the License.
*/
#include "mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.h"
#include "tools/converter/parser/caffe/caffe_model_parser.h"
#include <vector>
#include <iostream>
#include <utility>
#include "mindspore/lite/tools/converter/parser/caffe/caffe_node_parser_registry.h"
#include "mindspore/lite/tools/converter/parser/caffe/caffe_parse_utils.h"
#include "mindspore/lite/tools/converter/parser/caffe/caffe_inspector.h"
#include "tools/converter/parser/caffe/caffe_node_parser_registry.h"
#include "tools/converter/parser/caffe/caffe_inspector.h"
#include "tools/common/graph_util.h"
#include "tools/common/protobuf_utils.h"
namespace mindspore {
namespace lite {
......@@ -31,9 +31,8 @@ CaffeModelParser::~CaffeModelParser() {}
const std::set<std::string> CaffeModelParser::skipedLayerType = {"Dropout"};
schema::MetaGraphT *CaffeModelParser::ParseToFb(const std::string &modelFile,
const std::string &weightFile,
const QuantType &quantType) {
schema::MetaGraphT *CaffeModelParser::ParseToFb(const std::string &modelFile, const std::string &weightFile,
const QuantType &quantType) {
if (ValidateFileStr(modelFile, ".prototxt") != RET_OK) {
MS_LOG(ERROR) << "INPUT ILLEGAL: modelFile must be *.prototxt";
return nullptr;
......@@ -89,8 +88,7 @@ schema::MetaGraphT *CaffeModelParser::ParseToFb(const std::string &modelFile,
return metaGraph.release();
}
STATUS CaffeModelParser::SetOpInputIdx(const caffe::LayerParameter &layer,
schema::CNodeT *op,
STATUS CaffeModelParser::SetOpInputIdx(const caffe::LayerParameter &layer, schema::CNodeT *op,
TensorCache *tensorCache) {
for (int i = 0; i < layer.bottom_size(); i++) {
int index = tensorCache->FindTensor(layer.bottom(i));
......@@ -104,8 +102,7 @@ STATUS CaffeModelParser::SetOpInputIdx(const caffe::LayerParameter &layer,
return RET_OK;
}
STATUS CaffeModelParser::SetOpOutputIdx(const caffe::LayerParameter &layer,
schema::CNodeT *op,
STATUS CaffeModelParser::SetOpOutputIdx(const caffe::LayerParameter &layer, schema::CNodeT *op,
TensorCache *tensorCache) {
for (int i = 0; i < layer.top_size(); i++) {
std::unique_ptr<schema::TensorT> msTensor = std::make_unique<schema::TensorT>();
......@@ -114,8 +111,7 @@ STATUS CaffeModelParser::SetOpOutputIdx(const caffe::LayerParameter &layer,
return RET_OK;
}
STATUS CaffeModelParser::SetWeightTensor(const std::vector<schema::TensorT *> &weightVec,
schema::CNodeT *op,
STATUS CaffeModelParser::SetWeightTensor(const std::vector<schema::TensorT *> &weightVec, schema::CNodeT *op,
TensorCache *tensorCache) {
for (auto iter : weightVec) {
op->inputIndex.emplace_back(tensorCache->AddTensor("Weight", iter, CONST));
......@@ -123,8 +119,7 @@ STATUS CaffeModelParser::SetWeightTensor(const std::vector<schema::TensorT *> &w
return RET_OK;
}
STATUS CaffeModelParser::SetAllTensors(const TensorCache &tensorCache,
schema::MetaGraphT *subGraphDef) {
STATUS CaffeModelParser::SetAllTensors(const TensorCache &tensorCache, schema::MetaGraphT *subGraphDef) {
std::vector<schema::TensorT *> tensors = tensorCache.GetCachedTensor();
for (auto iter : tensors) {
std::unique_ptr<schema::TensorT> temp(iter);
......@@ -133,8 +128,7 @@ STATUS CaffeModelParser::SetAllTensors(const TensorCache &tensorCache,
return RET_OK;
}
STATUS CaffeModelParser::SetGraphTensorIndex(const caffe::NetParameter &proto,
TensorCache *tensorCache,
STATUS CaffeModelParser::SetGraphTensorIndex(const caffe::NetParameter &proto, TensorCache *tensorCache,
schema::MetaGraphT *subGraphDef) {
CaffeInspector caffeInspector;
caffeInspector.InspectModel(proto);
......@@ -160,10 +154,8 @@ STATUS CaffeModelParser::SetGraphTensorIndex(const caffe::NetParameter &proto,
return RET_OK;
}
STATUS CaffeModelParser::ParseLayer(const caffe::NetParameter &proto,
const caffe::NetParameter &weight,
TensorCache *tensorCache,
schema::MetaGraphT *subGraphDef) {
STATUS CaffeModelParser::ParseLayer(const caffe::NetParameter &proto, const caffe::NetParameter &weight,
TensorCache *tensorCache, schema::MetaGraphT *subGraphDef) {
for (int i = 0; i < proto.layer_size(); i++) {
auto layer = proto.layer(i);
......@@ -235,8 +227,7 @@ STATUS CaffeModelParser::ParseLayer(const caffe::NetParameter &proto,
return RET_OK;
}
STATUS CaffeModelParser::GetModelInput(const caffe::NetParameter &proto,
TensorCache *tensorCache) {
STATUS CaffeModelParser::GetModelInput(const caffe::NetParameter &proto, TensorCache *tensorCache) {
for (int i = 0; i < proto.input_size(); i++) {
if (proto.input_dim_size() <= 0) {
continue;
......
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc 100755 → 100644
浏览文件 @ 5a6c358d
......@@ -21,6 +21,7 @@
#include <utility>
#include "tools/common/graph_util.h"
#include "src/common/utils.h"
#include "tools/common/protobuf_utils.h"
namespace mindspore {
namespace lite {
......@@ -54,36 +55,7 @@ std::vector<int32_t> OnnxModelParser::GetDimsFromOnnxValue(const onnx::ValueInfo
return dims;
}
STATUS OnnxModelParser::ReadOnnxModelFromBinary(const std::string &modelFile,
google::protobuf::Message *onnx_model) {
std::unique_ptr<char> onnx_file(new (std::nothrow) char[PATH_MAX]{0});
#ifdef _WIN32
if (_fullpath(onnx_file.get(), modelFile.c_str(), 1024) == nullptr) {
MS_LOG(ERROR) << "get realpath " << modelFile << " fail";
return RET_ERROR;
}
#else
if (realpath(modelFile.c_str(), onnx_file.get()) == nullptr) {
MS_LOG(ERROR) << "get realpath " << modelFile << " fail";
return RET_ERROR;
}
#endif
int fd = open(onnx_file.get(), O_RDONLY);
google::protobuf::io::FileInputStream input(fd);
google::protobuf::io::CodedInputStream code_input(&input);
code_input.SetTotalBytesLimit(INT_MAX, 536870912);
bool ret = onnx_model->ParseFromCodedStream(&code_input);
if (!ret) {
MS_LOG(ERROR) << "load onnx file failed";
return RET_ERROR;
}
(void)close(fd);
onnx_file.release();
return RET_OK;
}
STATUS OnnxModelParser::SetGraphConstTensor(const onnx::GraphProto &onnx_graph,
TensorCache *tensor_cache) {
STATUS OnnxModelParser::SetGraphConstTensor(const onnx::GraphProto &onnx_graph, TensorCache *tensor_cache) {
MS_LOG(DEBUG) << "set onnx constant tensors";
for (const auto &onnx_const_value : onnx_graph.initializer()) {
int index;
......@@ -119,11 +91,8 @@ STATUS OnnxModelParser::SetGraphConstTensor(const onnx::GraphProto &onnx_graph,
return RET_OK;
}
STATUS OnnxModelParser::AddValueInfo(const onnx::ValueInfoProto &proto,
const std::string &name,
const TensorType &type,
TensorCache *tensor_cache,
int *index) {
STATUS OnnxModelParser::AddValueInfo(const onnx::ValueInfoProto &proto, const std::string &name, const TensorType &type,
TensorCache *tensor_cache, int *index) {
auto data_type = GetDataTypeFromOnnx(static_cast<onnx::TensorProto_DataType>(proto.type().tensor_type().elem_type()));
if (data_type == kTypeUnknown) {
MS_LOG(ERROR) << "not support onnx data type "
......@@ -143,11 +112,8 @@ STATUS OnnxModelParser::AddValueInfo(const onnx::ValueInfoProto &proto,
return RET_OK;
}
STATUS OnnxModelParser::AddTensorProto(const onnx::TensorProto &proto,
const std::string &name,
const TensorType &type,
TensorCache *tensor_cache,
int *index) {
STATUS OnnxModelParser::AddTensorProto(const onnx::TensorProto &proto, const std::string &name, const TensorType &type,
TensorCache *tensor_cache, int *index) {
auto data_type = GetDataTypeFromOnnx(static_cast<onnx::TensorProto_DataType>(proto.data_type()));
if (data_type == kTypeUnknown) {
MS_LOG(ERROR) << "not support onnx data type " << static_cast<onnx::TensorProto_DataType>(proto.data_type());
......@@ -174,8 +140,7 @@ STATUS OnnxModelParser::AddTensorProto(const onnx::TensorProto &proto,
return RET_OK;
}
STATUS OnnxModelParser::SetGraphInputTensor(const onnx::GraphProto &onnx_graph,
schema::MetaGraphT *graph,
STATUS OnnxModelParser::SetGraphInputTensor(const onnx::GraphProto &onnx_graph, schema::MetaGraphT *graph,
TensorCache *tensor_cache) {
for (const auto &input_value : onnx_graph.input()) {
auto ret = tensor_cache->FindTensor(input_value.name());
......@@ -192,8 +157,7 @@ STATUS OnnxModelParser::SetGraphInputTensor(const onnx::GraphProto &onnx_graph,
return RET_OK;
}
STATUS OnnxModelParser::SetGraphOutputTensor(const onnx::GraphProto &onnx_graph,
schema::MetaGraphT *graph,
STATUS OnnxModelParser::SetGraphOutputTensor(const onnx::GraphProto &onnx_graph, schema::MetaGraphT *graph,
TensorCache *tensor_cache) {
for (const auto &output_value : onnx_graph.output()) {
int index;
......@@ -207,10 +171,8 @@ STATUS OnnxModelParser::SetGraphOutputTensor(const onnx::GraphProto &onnx_graph,
return RET_OK;
}
void OnnxModelParser::ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::MetaGraphT *graph,
TensorCache *tensor_cache) {
void OnnxModelParser::ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::MetaGraphT *graph, TensorCache *tensor_cache) {
std::unique_ptr<schema::CNodeT> dst_op_1 = std::make_unique<schema::CNodeT>();
dst_op_1->name = "Gemm_MatMul_" + onnx_node.output(0);
ParseOnnxNodeAttr(onnx_graph, onnx_node, "MatMul", dst_op_1.get());
......@@ -231,8 +193,7 @@ void OnnxModelParser::ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph,
graph->nodes.emplace_back(std::move(dst_op_2));
}
STATUS OnnxModelParser::ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node,
TensorCache *tensor_cache) {
STATUS OnnxModelParser::ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node, TensorCache *tensor_cache) {
// convert GivenTensorFill node to a weight/bias tensor
auto ret = tensor_cache->FindTensor(onnx_node.output(0));
if (ret < 0) {
......@@ -284,10 +245,8 @@ STATUS OnnxModelParser::ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node,
return RET_OK;
}
STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op,
schema::TensorT *dst_tensor,
STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op, schema::TensorT *dst_tensor,
TensorCache *tensor_cache) {
// change op_type() to name(), that is unique
dst_op->name = onnx_node.op_type() + "_" + onnx_node.output(0);
......@@ -319,11 +278,8 @@ STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph,
return RET_OK;
}
void OnnxModelParser::SetOpQuantParams(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op,
schema::TensorT *dst_tensor,
TensorCache *tensor_cache) {
void OnnxModelParser::SetOpQuantParams(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op, schema::TensorT *dst_tensor, TensorCache *tensor_cache) {
MS_ASSERT(dst_op != nullptr);
MS_ASSERT(tensor_cache != nullptr);
std::vector<string> quant_node_name;
......@@ -380,10 +336,8 @@ void OnnxModelParser::SetOpQuantParams(const onnx::GraphProto &onnx_graph,
}
}
STATUS OnnxModelParser::ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
const string &onnx_op_type,
schema::CNodeT *dst_op) {
STATUS OnnxModelParser::ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
const string &onnx_op_type, schema::CNodeT *dst_op) {
auto node_parser = OnnxNodeParserRegistry::GetInstance()->GetNodeParser(onnx_op_type);
if (node_parser == nullptr) {
MS_LOG(EXCEPTION) << "not find " << onnx_op_type << ", node parser is nullptr";
......@@ -392,10 +346,8 @@ STATUS OnnxModelParser::ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph,
return node_parser->Parse(onnx_graph, onnx_node, dst_op);
}
STATUS OnnxModelParser::SetOpInputIndex(const std::vector<string> &node_inputs,
schema::CNodeT *dst_op,
const onnx::NodeProto &onnx_node,
TensorCache *tensor_cache) {
STATUS OnnxModelParser::SetOpInputIndex(const std::vector<string> &node_inputs, schema::CNodeT *dst_op,
const onnx::NodeProto &onnx_node, TensorCache *tensor_cache) {
for (const auto &onnx_node_input : node_inputs) {
auto index = tensor_cache->FindTensor(onnx_node_input);
if (index < 0) {
......@@ -408,8 +360,7 @@ STATUS OnnxModelParser::SetOpInputIndex(const std::vector<string> &node_inputs,
return RET_OK;
}
STATUS OnnxModelParser::SetOpOutputIndex(const std::vector<string> &node_outputs,
schema::CNodeT *dst_op,
STATUS OnnxModelParser::SetOpOutputIndex(const std::vector<string> &node_outputs, schema::CNodeT *dst_op,
TensorCache *tensor_cache) {
for (const auto &onnx_node_output : node_outputs) {
auto index = tensor_cache->FindTensor(onnx_node_output);
......@@ -424,8 +375,7 @@ STATUS OnnxModelParser::SetOpOutputIndex(const std::vector<string> &node_outputs
return RET_OK;
}
STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_value,
schema::TensorT *tensor) {
STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_value, schema::TensorT *tensor) {
size_t data_count = 1;
std::for_each(tensor->dims.begin(), tensor->dims.end(), [&data_count](int dim) { data_count *= dim; });
size_t data_size = 0;
......@@ -484,8 +434,7 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
return RET_OK;
}
STATUS OnnxModelParser::SetAllTensors(const TensorCache &tensor_cache,
schema::MetaGraphT *graphDef) {
STATUS OnnxModelParser::SetAllTensors(const TensorCache &tensor_cache, schema::MetaGraphT *graphDef) {
std::vector<schema::TensorT *> tensors = tensor_cache.GetCachedTensor();
for (auto iter : tensors) {
std::unique_ptr<schema::TensorT> temp(iter);
......@@ -507,17 +456,16 @@ void OnnxModelParser::FindGraphInputAndConst(const onnx::GraphProto &onnx_graph)
}
}
schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile,
const std::string &weightFile,
const QuantType &quantType) {
schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, const std::string &weightFile,
const QuantType &quantType) {
if (ValidateFileStr(modelFile, ".onnx") != RET_OK) {
MS_LOG(ERROR) << "Input illegal: modelFile must be *.onnx";
return nullptr;
}
auto dst_graph = std::make_unique<schema::MetaGraphT>();
onnx::ModelProto onnx_model;
if (ReadOnnxModelFromBinary(modelFile, &onnx_model) != RET_OK) {
MS_LOG(ERROR) << "read onnx model fail";
if (ReadProtoFromBinaryFile((const char *)modelFile.c_str(), &onnx_model) != RET_OK) {
MS_LOG(ERROR) << "Read onnx model file failed, model path: " << modelFile;
return nullptr;
}
const onnx::GraphProto &onnx_graph = onnx_model.graph();
......@@ -531,6 +479,7 @@ schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile,
MS_LOG(ERROR) << "SetGraphConstTensor failed";
return nullptr;
}
auto dst_graph = std::make_unique<schema::MetaGraphT>();
// init onnx model graph input tensor
if (SetGraphInputTensor(onnx_graph, dst_graph.get(), &tensor_cache)) {
MS_LOG(ERROR) << "SetGraphInputTensor failed";
......
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.h
浏览文件 @ 5a6c358d
......@@ -41,78 +41,47 @@ class OnnxModelParser : public ModelParser {
virtual ~OnnxModelParser();
schema::MetaGraphT *ParseToFb(const std::string &modelFile, const std::string &weightFile,
const QuantType &quantType = QuantType_QUANT_NONE) override;
const QuantType &quantType = QuantType_QUANT_NONE) override;
private:
TypeId GetDataTypeFromOnnx(onnx::TensorProto_DataType onnx_type);
std::vector<int32_t> GetDimsFromOnnxValue(const onnx::ValueInfoProto &onnx_value);
STATUS ReadOnnxModelFromBinary(const std::string &modelFile,
google::protobuf::Message *model_proto);
STATUS SetGraphConstTensor(const onnx::GraphProto &onnx_graph,
TensorCache *tensor_cache);
STATUS SetGraphInputTensor(const onnx::GraphProto &onnx_graph,
schema::MetaGraphT *graph,
TensorCache *tensor_cache);
STATUS SetGraphOutputTensor(const onnx::GraphProto &onnx_graph,
schema::MetaGraphT *graph,
TensorCache *tensor_cache);
STATUS AddValueInfo(const onnx::ValueInfoProto &proto,
const std::string &name,
const TensorType &type,
TensorCache *tensor_cache,
int *index);
STATUS AddTensorProto(const onnx::TensorProto &proto,
const std::string &name,
const TensorType &type,
TensorCache *tensor_cache,
int *index);
STATUS ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op,
schema::TensorT *dst_tensor,
TensorCache *tensor_cache);
void ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::MetaGraphT *graph,
TensorCache *tensor_cache);
STATUS ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node,
TensorCache *tensor_cache);
STATUS ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
const string &onnx_op_type,
schema::CNodeT *dst_op);
void SetOpQuantParams(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op,
schema::TensorT *dst_tensor,
TensorCache *tensor_cache);
STATUS SetOpInputIndex(const std::vector<string> &node_inputs,
schema::CNodeT *dst_op,
const onnx::NodeProto &onnx_node,
TensorCache *tensor_cache);
STATUS SetOpOutputIndex(const std::vector<string> &node_outputs,
schema::CNodeT *dst_op,
TensorCache *tensor_cache);
STATUS CopyOnnxTensorData(const onnx::TensorProto &onnx_init_value,
schema::TensorT *tensor);
STATUS SetAllTensors(const TensorCache &tensor_cache,
schema::MetaGraphT *graphDef);
STATUS SetGraphConstTensor(const onnx::GraphProto &onnx_graph, TensorCache *tensor_cache);
STATUS SetGraphInputTensor(const onnx::GraphProto &onnx_graph, schema::MetaGraphT *graph, TensorCache *tensor_cache);
STATUS SetGraphOutputTensor(const onnx::GraphProto &onnx_graph, schema::MetaGraphT *graph, TensorCache *tensor_cache);
STATUS AddValueInfo(const onnx::ValueInfoProto &proto, const std::string &name, const TensorType &type,
TensorCache *tensor_cache, int *index);
STATUS AddTensorProto(const onnx::TensorProto &proto, const std::string &name, const TensorType &type,
TensorCache *tensor_cache, int *index);
STATUS ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op, schema::TensorT *dst_tensor, TensorCache *tensor_cache);
void ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::MetaGraphT *graph, TensorCache *tensor_cache);
STATUS ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node, TensorCache *tensor_cache);
STATUS ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
const string &onnx_op_type, schema::CNodeT *dst_op);
void SetOpQuantParams(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node, schema::CNodeT *dst_op,
schema::TensorT *dst_tensor, TensorCache *tensor_cache);
STATUS SetOpInputIndex(const std::vector<string> &node_inputs, schema::CNodeT *dst_op,
const onnx::NodeProto &onnx_node, TensorCache *tensor_cache);
STATUS SetOpOutputIndex(const std::vector<string> &node_outputs, schema::CNodeT *dst_op, TensorCache *tensor_cache);
STATUS CopyOnnxTensorData(const onnx::TensorProto &onnx_init_value, schema::TensorT *tensor);
STATUS SetAllTensors(const TensorCache &tensor_cache, schema::MetaGraphT *graphDef);
void FindGraphInputAndConst(const onnx::GraphProto &onnx_graph);
......
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