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提交 2c63bdb1 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!4595 process Constant op in onnx converter && add two onnx models 

Merge pull request !4595 from wangzhe/master
上级 19192e75 c9cb994e
隐藏空白更改
内联 并排
Showing with 134 addition and 81 deletion
mindspore/lite/test/models_onnx.cfg
浏览文件 @ 2c63bdb1
mtk_detect-mbv2-shortcut-400-400-simplified.onnx
mtk_emotions-d2012-75.8%.onnx
mtk_face_features_v3.onnx
mindspore/lite/test/run_benchmark_nets.sh
浏览文件 @ 2c63bdb1
......@@ -153,6 +153,28 @@ function Run_arm64() {
fi
#sleep 1
done < ${models_caffe_config}
# Run caffe converted models:
while read line; do
model_name=${line}
if [[ $model_name == \#* ]]; then
continue
fi
echo ${model_name}
echo 'cd /data/local/tmp/benchmark_test' > adb_run_cmd.txt
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/benchmark_test;./benchmark --modelPath='${model_name}'.ms --inDataPath=/data/local/tmp/input_output/input/'${model_name}'.ms.bin --calibDataPath=/data/local/tmp/input_output/output/'${model_name}'.ms.out --warmUpLoopCount=1 --loopCount=1'
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/benchmark_test;./benchmark --modelPath='${model_name}'.ms --inDataPath=/data/local/tmp/input_output/input/'${model_name}'.ms.bin --calibDataPath=/data/local/tmp/input_output/output/'${model_name}'.ms.out --warmUpLoopCount=1 --loopCount=1' >> adb_run_cmd.txt
adb -s ${device_id} shell < adb_run_cmd.txt
if [ $? = 0 ]; then
run_result='Run_arm64:'${model_name}' pass'
echo ${run_result} >> ${run_benchmark_result_file}
else
run_result='Run_arm64:'${model_name}' fail <<===========================this is the failed case'
echo ${run_result} >> ${run_benchmark_result_file}
return 1
fi
#sleep 1
done < ${models_onnx_config}
}
# Print start msg before run testcase
......
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc
浏览文件 @ 2c63bdb1
......@@ -76,45 +76,85 @@ STATUS OnnxModelParser::ReadOnnxModelFromBinary(const std::string &modelFile, go
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()) {
auto data_type = GetDataTypeFromOnnx(static_cast<onnx::TensorProto_DataType>(onnx_const_value.data_type()));
if (data_type == kTypeUnknown) {
MS_LOG(ERROR) << "not support onnx data type "
<< static_cast<onnx::TensorProto_DataType>(onnx_const_value.data_type());
return RET_ERROR;
}
std::unique_ptr<schema::TensorT> tensor(new (std::nothrow) schema::TensorT);
if (tensor == nullptr) {
MS_LOG(ERROR) << "new tensor failed";
return RET_ERROR;
}
tensor->dataType = data_type;
tensor->format = schema::Format_NCHW; // onnx use NCHW
std::copy(onnx_const_value.dims().begin(), onnx_const_value.dims().end(), std::back_inserter(tensor->dims));
tensor->nodeType = schema::NodeType_ValueNode;
if (CopyOnnxTensorData(onnx_const_value, tensor.get())) {
MS_LOG(ERROR) << "copy onnx data failed";
return RET_ERROR;
int index;
const auto status = AddTensorProto(onnx_const_value, onnx_const_value.name(), GRAPH_INPUT, tensor_cache, &index);
if (status != RET_OK) {
return status;
}
// TODO(wangzhe) why use GRAPH_INPUT other than CONST(GRAPH_INPUT will add index to graphInputs)
const auto index = tensor_cache->AddTensor(onnx_const_value.name(), tensor.release(), GRAPH_INPUT);
MS_LOG(DEBUG) << "add const tensor: " << onnx_const_value.name() << ", index " << index;
}
MS_LOG(DEBUG) << "process onnx Constant ops";
for (int i = 0; i < onnx_graph.node_size(); i++) {
const auto &node = onnx_graph.node(i);
if (node.op_type().compare("Constant") == 0) {
for (const auto &attr : node.attribute()) {
if (attr.name() == "sparse_value") {
MS_LOG(ERROR) << "sparse_value";
}
if (attr.name() == "value") {
const auto &t = attr.t();
int index;
const auto status = AddTensorProto(t, node.output(0), GRAPH_INPUT, tensor_cache, &index);
if (status != RET_OK) {
return status;
}
MS_LOG(DEBUG) << "add const tensor: " << t.name() << ", index " << index;
} else {
MS_LOG(ERROR) << "processing Constant op attr " << attr.name() << " not implemented";
return RET_INVALID_OP_ATTR;
}
}
}
}
return RET_OK;
}
// TODO(wangzhe) seems AddTensorCache should be renamed to prepare tensor to add to tensor_cache
STATUS OnnxModelParser::AddTensorCache(const onnx::ValueInfoProto &proto, schema::TensorT *tensor) {
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 type "
MS_LOG(ERROR) << "not support onnx data type "
<< static_cast<onnx::TensorProto_DataType>(proto.type().tensor_type().elem_type());
return RET_ERROR;
}
std::unique_ptr<schema::TensorT> tensor(new schema::TensorT);
if (tensor == nullptr) {
MS_LOG(ERROR) << "new tensor failed";
return RET_ERROR;
}
tensor->dataType = data_type;
tensor->dims = GetDimsFromOnnxValue(proto);
tensor->format = schema::Format_NCHW;
tensor->nodeType = schema::NodeType_ValueNode;
// TODO(wangzhe) tensor->data and quantParams not set, should we need tensor_cache->AddTensor?
*index = tensor_cache->AddTensor(name, tensor.release(), type);
return RET_OK;
}
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());
return RET_ERROR;
}
std::unique_ptr<schema::TensorT> tensor(new (std::nothrow) schema::TensorT);
if (tensor == nullptr) {
MS_LOG(ERROR) << "new tensor failed";
return RET_ERROR;
}
tensor->dataType = data_type;
std::copy(proto.dims().begin(), proto.dims().end(), std::back_inserter(tensor->dims));
tensor->format = schema::Format_NCHW;
tensor->nodeType = schema::NodeType_ValueNode;
if (CopyOnnxTensorData(proto, tensor.get())) {
MS_LOG(ERROR) << "copy onnx data failed";
return RET_ERROR;
}
if (data_type == kNumberTypeInt64) {
MS_LOG(ERROR) << "INT64" << proto.name();
tensor->dataType = kNumberTypeInt32; // CopyOnnxTensorData will convert int64 to int32
}
*index = tensor_cache->AddTensor(name, tensor.release(), type);
return RET_OK;
}
......@@ -123,15 +163,13 @@ STATUS OnnxModelParser::SetGraphInputTensor(const onnx::GraphProto &onnx_graph,
for (const auto &input_value : onnx_graph.input()) {
auto ret = tensor_cache->FindTensor(input_value.name());
if (ret < 0) {
std::unique_ptr<schema::TensorT> tensor(new schema::TensorT);
// TODO(wangzhe) why there is an addtensorCache?
if (AddTensorCache(input_value, tensor.get())) {
return RET_ERROR;
int index;
const auto status = AddValueInfo(input_value, input_value.name(), GRAPH_INPUT, tensor_cache, &index);
if (status != RET_OK) {
return status;
}
// TODO(wangzhe) why inputTensor is value and should be added into tensor_cache?
auto tensor_index = tensor_cache->AddTensor(input_value.name(), tensor.release(), GRAPH_INPUT);
graph->inputIndex.emplace_back(static_cast<uint32_t>(tensor_index));
MS_LOG(DEBUG) << "input_value name: " << input_value.name() << ", graph input index: " << tensor_index;
MS_LOG(ERROR) << "input_value name: " << input_value.name() << ", graph input index: " << index;
graph->inputIndex.emplace_back(static_cast<uint32_t>(index));
}
}
return RET_OK;
......@@ -140,14 +178,13 @@ STATUS OnnxModelParser::SetGraphInputTensor(const onnx::GraphProto &onnx_graph,
STATUS OnnxModelParser::SetGraphOutputTensor(const onnx::GraphProto &onnx_graph, schema::MetaGraphT *graph,
TensorCache *tensor_cache) {
for (const auto &output_value : onnx_graph.output()) {
std::unique_ptr<schema::TensorT> tensor(new schema::TensorT);
if (AddTensorCache(output_value, tensor.get())) {
return RET_ERROR;
int index;
const auto status = AddValueInfo(output_value, output_value.name(), OP_OUTPUT, tensor_cache, &index);
if (status != RET_OK) {
return status;
}
// TODO(wangzhe) why we need AddTensor at OutputTensor
auto tensor_index = tensor_cache->AddTensor(output_value.name(), tensor.release(), OP_OUTPUT);
graph->outputIndex.emplace_back(tensor_index);
MS_LOG(DEBUG) << "output_value name: " << output_value.name() << ", graph output index: " << tensor_index;
graph->outputIndex.emplace_back(index);
MS_LOG(ERROR) << "output_value name: " << output_value.name() << ", graph output index: " << index;
}
return RET_OK;
}
......@@ -332,32 +369,11 @@ STATUS OnnxModelParser::ParseOnnxNodeAttr(const onnx::GraphProto &onnx_graph, co
STATUS OnnxModelParser::SetOpInputIndex(const std::vector<string> &node_inputs, schema::CNodeT *dst_op,
const onnx::NodeProto &onnx_node, TensorCache *tensor_cache) {
schema::Format format = schema::Format_MAX;
for (const auto &onnx_node_attr : onnx_node.attribute()) {
if (onnx_node_attr.name() == "order") { // do we need this code? onnx doc don't have order attr
MS_LOG(EXCEPTION) << "find order attr";
if (onnx_node_attr.s() == "NHWC") {
format = schema::Format_NHWC;
} else {
MS_LOG(ERROR) << "Unsupported format: " << onnx_node_attr.s();
return RET_ERROR;
}
}
}
for (const auto &onnx_node_input : node_inputs) {
auto index = tensor_cache->FindTensor(onnx_node_input);
// MS_LOG(ERROR) << onnx_node.name() << " input " << onnx_node_input << " index in tensor_cache " << index;
if (index < 0) { // TODO(wangzhe) can this be ignored? because it's no use
/*
std::unique_ptr<schema::TensorT> tensor(new schema::TensorT);
index = tensor_cache->AddTensor(onnx_node_input, tensor.release(), OP_OUTPUT);
*/
MS_LOG(EXCEPTION) << "input " << onnx_node_input << " of node " << onnx_node.name() << " can't be found";
// MS_LOG(INFO) << "new index: " << index;
}
if (format != schema::Format_MAX) { // TODO(wangzhe) also this
auto inTensor = tensor_cache->GetCachedTensor().at(index);
inTensor->format = format;
if (index < 0) {
MS_LOG(ERROR) << "input " << onnx_node_input << " of node " << onnx_node.name() << " can't be found";
return RET_ERROR;
}
MS_LOG(DEBUG) << "node: " << onnx_node_input << ", input index: " << index;
dst_op->inputIndex.emplace_back(index);
......@@ -369,19 +385,12 @@ STATUS OnnxModelParser::SetOpOutputIndex(const std::vector<string> &node_outputs
TensorCache *tensor_cache) {
for (const auto &onnx_node_output : node_outputs) {
auto index = tensor_cache->FindTensor(onnx_node_output);
if (index < 0) {
MS_LOG(INFO) << "output of node " << dst_op->name << " not in tensor_cache, creating";
MS_LOG(INFO) << "total " << node_outputs.size() << " outputs";
if (index < 0) { // when index >= 0, it's graph's output
std::unique_ptr<schema::TensorT> tensor(new schema::TensorT);
// GetDataTypeFromOnnx(static_cast<onnx::TensorProto_DataType>(proto.type().tensor_type().elem_type()));
// tensor->dataType = ;
// tensor->dims = tflite_tensor->shape;
tensor->nodeType = schema::NodeType_Parameter;
index = tensor_cache->AddTensor(onnx_node_output, tensor.release(), OP_OUTPUT);
}
MS_LOG(DEBUG) << "node: " << onnx_node_output << ", input index: " << index;
MS_LOG(DEBUG) << "node: " << onnx_node_output << ", output index: " << index;
dst_op->outputIndex.emplace_back(index);
}
return RET_OK;
......@@ -390,8 +399,10 @@ STATUS OnnxModelParser::SetOpOutputIndex(const std::vector<string> &node_outputs
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; });
MS_LOG(ERROR) << "const tensor dims " << tensor->dims.size();
size_t data_size = 0;
const void *tensor_data = nullptr;
int32_t *buffer = nullptr;
switch (tensor->dataType) {
case kNumberTypeFloat32:
data_size = data_count * sizeof(float);
......@@ -410,12 +421,23 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
}
break;
case kNumberTypeInt64:
data_size = data_count * sizeof(int64_t);
data_size = data_count * sizeof(int32_t);
buffer = new int32_t[data_count];
const int64_t *in_data;
if (onnx_const_value.int64_data_size() == 0) {
tensor_data = onnx_const_value.raw_data().data();
in_data = reinterpret_cast<const int64_t *>(onnx_const_value.raw_data().data());
} else {
tensor_data = onnx_const_value.int64_data().data();
in_data = onnx_const_value.int64_data().data();
}
for (int i = 0; i < data_count; ++i) {
if (in_data[i] > static_cast<int64_t>(INT32_MAX) || in_data[i] < static_cast<int64_t>(INT32_MIN)) {
MS_LOG(ERROR) << "int64 data " << in_data[i] << "too big to fit into int32";
return RET_ERROR;
} else {
buffer[i] = static_cast<int>(in_data[i]);
}
}
tensor_data = reinterpret_cast<void *>(buffer);
break;
case kNumberTypeUInt8:
case kNumberTypeInt8:
......@@ -431,6 +453,9 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
MS_LOG(ERROR) << "memcpy_s failed";
return RET_ERROR;
}
if (kNumberTypeInt64 == tensor->dataType) {
free(buffer);
}
return RET_OK;
}
......@@ -491,6 +516,9 @@ MetaGraphT *OnnxModelParser::Parse(const std::string &modelFile, const std::stri
}
// init op node input/output tensor, and dst_op attr
for (const auto &onnx_node : onnx_graph.node()) {
if (onnx_node.op_type() == "Constant") {
continue;
}
if (onnx_node.op_type() == "Gemm") {
ParseOnnxGemmNode(onnx_graph, onnx_node, dst_graph.get(), &tensor_cache);
continue;
......
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.h
浏览文件 @ 2c63bdb1
......@@ -48,7 +48,10 @@ class OnnxModelParser : public ModelParser {
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 AddTensorCache(const onnx::ValueInfoProto &proto, schema::TensorT *tensor);
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,
......
mindspore/lite/tools/converter/parser/onnx/onnx_pool_parser.cc
浏览文件 @ 2c63bdb1
......@@ -23,6 +23,7 @@ STATUS OnnxPoolParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
MS_LOG(DEBUG) << "onnx PoolParser";
std::unique_ptr<schema::PoolingT> attr(new schema::PoolingT());
attr->format = schema::Format_NCHW;
const auto &pool_type = onnx_node.op_type();
if (pool_type == "MaxPool") {
attr->poolingMode = schema::PoolMode_MAX_POOLING;
......@@ -37,7 +38,7 @@ STATUS OnnxPoolParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::Nod
attr->poolingMode = schema::PoolMode_MEAN_POOLING;
attr->global = true;
} else {
// MS_LOGE("Pooling param`s PoolingMode is not MAX either AVE. MindSpore support MAX and AVE only.");
MS_LOG(ERROR) << "Pooling param`s PoolingMode is not MAX either AVE. MindSpore support MAX and AVE only.";
return RET_ERROR;
}
......@@ -92,4 +93,3 @@ OnnxNodeRegistrar g_onnxGlobalAveragePoolParser("GlobalAveragePool", new OnnxPoo
OnnxNodeRegistrar g_onnxGlobalMaxPoolParser("GlobalMaxPool", new OnnxPoolParser());
} // namespace lite
} // namespace mindspore
mindspore/lite/tools/converter/parser/onnx/onnx_unsqueeze_parser.cc
浏览文件 @ 2c63bdb1
......@@ -19,8 +19,7 @@
namespace mindspore {
namespace lite {
STATUS OnnxUnSqueezeParser::Parse(const onnx::GraphProto &onnx_graph,
const onnx::NodeProto &onnx_node,
STATUS OnnxUnSqueezeParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *op) {
MS_LOG(DEBUG) << "onnx UnSqueezeParser";
std::unique_ptr<schema::UnsqueezeT> attr(new schema::UnsqueezeT());
......@@ -43,4 +42,3 @@ STATUS OnnxUnSqueezeParser::Parse(const onnx::GraphProto &onnx_graph,
OnnxNodeRegistrar g_onnxUnsqueezeParser("Unsqueeze", new OnnxUnSqueezeParser());
} // namespace lite
} // namespace mindspore
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