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!4554 fix anf exporter problem 

Merge pull request !4554 from wangchangkai/master
This commit is contained in:
mindspore-ci-bot 2020-08-17 11:27:54 +08:00 committed by Gitee
parent f20e68a879 892668389f
commit de857161ed
11 changed files with 126 additions and 14 deletions
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1
mindspore/lite/test/models_mindspore.cfg Normal file
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@ -0,0 +1 @@
ssd.pb

32
mindspore/lite/test/run_benchmark_nets.sh
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@ -65,6 +65,26 @@ function Run_x86() {
fi
done < ${models_onnx_config}
# Run mindspore converted models:
while read line; do
model_name=${line}
if [[ $model_name == \#* ]]; then
continue
fi
echo ${model_name}
echo 'cd '${convertor_path}'/MSLite-*-linux_x86_64'
cd ${convertor_path}/MSLite-*-linux_x86_64 || return 1
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib;./benchmark/benchmark --modelPath='${ms_models_path}'/'${model_name}'.ms --inDataPath=/home/workspace/mindspore_dataset/mslite/models/hiai/input_output/input/'${model_name}'.ms.bin --calibDataPath=/home/workspace/mindspore_dataset/mslite/models/hiai/input_output/output/'${model_name}'.ms.out --warmUpLoopCount=1 --loopCount=1' || return 1
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib;./benchmark/benchmark --modelPath=${ms_models_path}/${model_name}.ms --inDataPath=/home/workspace/mindspore_dataset/mslite/models/hiai/input_output/input/${model_name}.ms.bin --calibDataPath=/home/workspace/mindspore_dataset/mslite/models/hiai/input_output/output/${model_name}.ms.out --warmUpLoopCount=1 --loopCount=1 --accuracyThreshold=1.5
if [ $? = 0 ]; then
run_result='Run_x86: '${model_name}' pass'
echo ${run_result} >> ${run_benchmark_result_file}
else
run_result='Run_x86: '${model_name}' fail <<===========================this is the failed case'
echo ${run_result} >> ${run_benchmark_result_file}
return 1
fi
done < ${models_mindspore_config}
}
# Run on arm64 platform:
@ -176,6 +196,7 @@ models_tflite_config=${basepath}/models_tflite.cfg
models_caffe_config=${basepath}/models_caffe.cfg
models_tflite_posttraining_config=${basepath}/models_tflite_posttraining.cfg
models_onnx_config=${basepath}/models_onnx.cfg
models_mindspore_config=${basepath}/models_mindspore.cfg
rm -rf ${basepath}/ms_models
mkdir -p ${basepath}/ms_models
@ -216,6 +237,17 @@ while read line; do
./converter_lite --fmk=ONNX --modelFile=${models_path}/${model_name} --outputFile=${ms_models_path}/${model_name} || exit 1
done < ${models_onnx_config}
# Convert mindspore models:
while read line; do
model_name=${line}
if [[ $model_name == \#* ]]; then
continue
fi
echo ${model_name}
pwd
echo './converter_lite --fmk=MS --modelFile='${models_path}'/'${model_name}' --outputFile='${ms_models_path}'/'${model_name}''
./converter_lite --fmk=MS --modelFile=${models_path}/${model_name} --outputFile=${ms_models_path}/${model_name} || exit 1
done < ${models_mindspore_config}
# Convert TFLite PostTraining models:
while read line; do

4
mindspore/lite/tools/anf_exporter/anf_exporter.cc
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@ -408,6 +408,10 @@ void AnfExporter::SetOpOutputNode(const CNodePtr &cnode, const std::unique_ptr<s
node_id_map_[name] = meta_graphT->allTensors.size();
}
meta_graphT->allTensors.emplace_back(msTensor);
if (IsPrimitiveCNode(cnode, schema::PrimitiveType_Conv2D)
|| IsPrimitiveCNode(cnode, schema::PrimitiveType_DepthwiseConv2D)) {
break;
}
}
} else {
auto ms_tensor = new schema::TensorT();

4
mindspore/lite/tools/anf_importer/anf_populater/anf_depthwiseconv2d_populater.cc
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@ -155,8 +155,8 @@ int AnfDepwiseconv2DPopulater::Populate(const PrimitivePtr &prim, PrimitiveTValu
auto channel_multiplier = GetValue<int>(prim->GetAttr("channel_multiplier"));
attr->channelMultiplier = channel_multiplier;
MS_ASSERT(inputs.size() == kAnfPopulaterThree);
auto inputNode = inputs[kAnfPopulaterTwo];
MS_ASSERT(inputs.size() == kAnfPopulaterTwo);
auto inputNode = inputs[kAnfPopulaterOne];
MS_ASSERT(inputNode != nullptr);
if (inputNode->isa<Parameter>()) {
auto paramNode = inputNode->cast<ParameterPtr>();

35
mindspore/lite/tools/anf_importer/anf_populater/anf_make_tuple_populater.cc Normal file
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@ -0,0 +1,35 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <vector>
#include <memory>
#include "tools/anf_importer/anf_populater/anf_node_populater_registry.h"
#include "tools/anf_importer/anf_populater/anf_make_tuple_populater.h"
#include "ir/func_graph.h"
#include "ir/primitive.h"
namespace mindspore::lite {
int AnfMakeTuplePopulater::Populate(const PrimitivePtr &prim, PrimitiveTValue *primitiveTValuePtr,
const std::vector<AnfNodePtr> &inputs) {
auto primitive = std::make_unique<schema::PrimitiveT>();
auto attr = std::make_unique<schema::MakeTupleT>();
primitive->value.type = schema::PrimitiveType_MakeTuple;
primitive->value.value = attr.release();
MS_ASSERT(primitiveTValuePtr != nullptr);
primitiveTValuePtr->SetPrimitiveT(primitive.release());
return 0;
}
AnfNodePopulaterRegistrar anfMakeTuplePopulater("make_tuple", new AnfMakeTuplePopulater());
} // namespace mindspore::lite

29
mindspore/lite/tools/anf_importer/anf_populater/anf_make_tuple_populater.h Normal file
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@ -0,0 +1,29 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_ANF_MAKE_TUPLE_PARSER_H
#define MINDSPORE_ANF_MAKE_TUPLE_PARSER_H
#include "tools/anf_importer/anf_populater/anf_node_populater.h"
#include <vector>
namespace mindspore::lite {
class AnfMakeTuplePopulater : public AnfNodePopulater {
public:
AnfMakeTuplePopulater() = default;
~AnfMakeTuplePopulater() override = default;
int Populate(const PrimitivePtr &prim, PrimitiveTValue *primitiveTValuePtr,
const std::vector<AnfNodePtr> &inputs) override;
};
} // namespace mindspore::lite
#endif // MINDSPORE_ANF_MAKE_TUPLE_PARSER_H

8
mindspore/lite/tools/anf_importer/anf_populater/anf_reducemean_populater.cc
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@ -21,10 +21,6 @@
#include "ir/primitive.h"
namespace mindspore::lite {
namespace {
constexpr int kReduceInputNum = 3;
constexpr int kReduceInputIndex = 2;
} // namespace
int AnfReduceMeanPopulater::Populate(const PrimitivePtr &prim, PrimitiveTValue *primitiveTValuePtr,
const std::vector<AnfNodePtr> &inputs) {
auto primitive = std::make_unique<schema::PrimitiveT>();
@ -32,8 +28,8 @@ int AnfReduceMeanPopulater::Populate(const PrimitivePtr &prim, PrimitiveTValue *
attr->mode = schema::ReduceMode_ReduceMean;
attr->keepDims = GetValue<bool>(prim->GetAttr("keep_dims"));
if (inputs.size() == kReduceInputNum) {
auto inputNode = inputs[kReduceInputIndex];
if (inputs.size() == kAnfPopulaterTwo) {
auto inputNode = inputs[kAnfPopulaterOne];
MS_ASSERT(inputNode != nullptr);
if (inputNode->isa<ValueNode>()) {
auto valueNode = inputNode->cast<ValueNodePtr>();

4
mindspore/lite/tools/anf_importer/anf_populater/anf_transpose_populater.cc
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@ -26,8 +26,8 @@ int AnfTransposePopulater::Populate(const PrimitivePtr &prim, PrimitiveTValue *p
const std::vector<AnfNodePtr> &inputs) {
auto primitive = std::make_unique<schema::PrimitiveT>();
auto attr = std::make_unique<schema::TransposeT>();
MS_ASSERT(inputs.size() == kAnfPopulaterThree);
auto inputNode = inputs[kAnfPopulaterTwo];
MS_ASSERT(inputs.size() == kAnfPopulaterTwo);
auto inputNode = inputs[kAnfPopulaterOne];
if (inputNode->isa<ValueNode>()) {
auto valNode = inputNode->cast<ValueNodePtr>();
MS_ASSERT(valNode != nullptr);

4
mindspore/lite/tools/anf_importer/anf_populater/anf_tuple_getitem_populater.h
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@ -13,8 +13,8 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_ANF_BATCHNORM_PARSER_H
#define MINDSPORE_ANF_BATCHNORM_PARSER_H
#ifndef MINDSPORE_TUPLE_GETITEM_PARSER_H
#define MINDSPORE_TUPLE_GETITEM_PARSER_H
#include "tools/anf_importer/anf_populater/anf_node_populater.h"
#include <vector>
namespace mindspore::lite {

16
mindspore/lite/tools/anf_importer/import_from_protobuf.cc
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@ -718,6 +718,8 @@ bool AnfImporterFromProtobuf::BuildParameterForFuncGraph(const ParameterPtr &nod
MS_EXCEPTION_IF_NULL(param_value);
param_value->set_tensor_addr(tensor_data_buf);
param_value->set_tensor_size(tensor_info->Size());
param_value->set_tensor_type(tensor_info->data_type());
param_value->set_tensor_shape(tensor_info->shape());
node->set_default_param(param_value);
}
anfnode_build_map_[value_proto.name()] = node;
@ -1088,7 +1090,12 @@ bool AnfImporterFromProtobuf::BuildReturnForFuncGraph(const FuncGraphPtr &output
std::vector<AnfNodePtr> inputs;
if (importProto.output_size() > 1) {
inputs.clear();
inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
auto primitiveT = std::make_unique<schema::PrimitiveT>();
MS_ASSERT(primitiveT != nullptr);
primitiveT->value.type = schema::PrimitiveType_MakeTuple;
std::shared_ptr<PrimitiveTValue> primitiveTValuePtr = std::make_shared<PrimitiveTValue>(primitiveT.release());
MS_ASSERT(primitiveTValuePtr != nullptr);
inputs.push_back(NewValueNode(primitiveTValuePtr));
AbstractBasePtrList elem;
for (int out_size = 0; out_size < importProto.output_size(); ++out_size) {
const onnx::ValueInfoProto &output_node = importProto.output(out_size);
@ -1099,7 +1106,12 @@ bool AnfImporterFromProtobuf::BuildReturnForFuncGraph(const FuncGraphPtr &output
auto maketuple_ptr = outputFuncGraph->NewCNode(inputs);
maketuple_ptr->set_abstract(std::make_shared<abstract::AbstractTuple>(elem));
inputs.clear();
inputs.push_back(NewValueNode(prim::kPrimReturn));
auto primReturn = std::make_unique<schema::PrimitiveT>();
MS_ASSERT(primReturn != nullptr);
primReturn->value.type = schema::PrimitiveType_Return;
std::shared_ptr<PrimitiveTValue> primitiveTReturnValuePtr = std::make_shared<PrimitiveTValue>(primReturn.release());
MS_ASSERT(primitiveTReturnValuePtr != nullptr);
inputs.push_back(NewValueNode(primitiveTReturnValuePtr));
inputs.push_back(maketuple_ptr);
auto return_node = outputFuncGraph->NewCNode(inputs);
MS_EXCEPTION_IF_NULL(return_node);

3
mindspore/lite/tools/optimizer/fusion/conv_transform_fusion.cc
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@ -69,6 +69,8 @@ const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, co
if (IsMultiOutputTensors(func_graph, conv_node)) {
return transform_node;
}
auto abstr = transform_node->abstract();
int kernel_nums = Get_Kenrnel_nums(conv_node);
if (kernel_nums <= 0) {
MS_LOG(ERROR) << "Unsupported conv node, " << conv_node->DebugString();
@ -90,6 +92,7 @@ const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, co
} else {
MS_LOG(EXCEPTION) << "Unsupported opType, " << type;
}
pre_node->set_abstract(abstr);
return pre_node;
}