!4554 fix anf exporter problem

Merge pull request !4554 from wangchangkai/master

mindspore/lite/test/models_mindspore.cfg

@@ -0,0 +1 @@
+ssd.pb

mindspore/lite/test/run_benchmark_nets.sh

@@ -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

mindspore/lite/tools/anf_exporter/anf_exporter.cc

@@ -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();

mindspore/lite/tools/anf_importer/anf_populater/anf_depthwiseconv2d_populater.cc

@@ -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);
+  MS_ASSERT(inputs.size() == kAnfPopulaterTwo);
-  auto inputNode = inputs[kAnfPopulaterTwo];
+  auto inputNode = inputs[kAnfPopulaterOne];
   MS_ASSERT(inputNode != nullptr);
   if (inputNode->isa<Parameter>()) {
     auto paramNode = inputNode->cast<ParameterPtr>();

mindspore/lite/tools/anf_importer/anf_populater/anf_make_tuple_populater.cc

@@ -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

mindspore/lite/tools/anf_importer/anf_populater/anf_make_tuple_populater.h

@@ -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

mindspore/lite/tools/anf_importer/anf_populater/anf_reducemean_populater.cc

@@ -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) {
+  if (inputs.size() == kAnfPopulaterTwo) {
-    auto inputNode = inputs[kReduceInputIndex];
+    auto inputNode = inputs[kAnfPopulaterOne];
     MS_ASSERT(inputNode != nullptr);
     if (inputNode->isa<ValueNode>()) {
       auto valueNode = inputNode->cast<ValueNodePtr>();

mindspore/lite/tools/anf_importer/anf_populater/anf_transpose_populater.cc

@@ -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);
+  MS_ASSERT(inputs.size() == kAnfPopulaterTwo);
-  auto inputNode = inputs[kAnfPopulaterTwo];
+  auto inputNode = inputs[kAnfPopulaterOne];
   if (inputNode->isa<ValueNode>()) {
     auto valNode = inputNode->cast<ValueNodePtr>();
     MS_ASSERT(valNode != nullptr);

mindspore/lite/tools/anf_importer/anf_populater/anf_tuple_getitem_populater.h

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

mindspore/lite/tools/anf_importer/import_from_protobuf.cc

@@ -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);

mindspore/lite/tools/optimizer/fusion/conv_transform_fusion.cc

@@ -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;
 }