!8649 [MSLITE] Release pointer of split parameter in destructor.

From: @wang_shaocong
Reviewed-by: 
Signed-off-by:

mindspore/lite/src/ops/conv2d.cc

@@ -219,8 +219,8 @@ void Conv2D::PopulaterConv2DSingleGroup(const Primitive &prim, schema::Primitive
   attr->padRight = pad_list[3];
 
   auto dilation = CastToInt(prim.GetAttr("dilation"), true);
-  attr->dilateH = dilation[0];
-  attr->dilateW = dilation[1];
+  attr->dilateH = dilation[2];
+  attr->dilateW = dilation[3];
 
   auto kernel_size = CastToInt(prim.GetAttr("kernel_size"), true);
   attr->kernelH = kernel_size[0];

mindspore/lite/src/ops/primitive_c.cc

@@ -191,7 +191,7 @@ std::vector<int> CastToInt(const ValuePtr value, bool is_vector) {
       MS_LOG(WARNING) << "valueptr is not a sequence, value may be a scalar.";
       return {};
     }
-    if (value->cast<ValueSequeuePtr>()->value().front()->type()->type_name() == "Int64Imm") {
+    if (value->cast<ValueSequeuePtr>()->value().front()->type()->number_type() == kNumberTypeInt64) {
       auto origin_value = GetValue<std::vector<int64_t>>(value);
       for (size_t index = 0; index < origin_value.size(); ++index) {
         cur_value.push_back(static_cast<int>(origin_value[index]));
@@ -200,7 +200,7 @@ std::vector<int> CastToInt(const ValuePtr value, bool is_vector) {
       cur_value = GetValue<std::vector<int>>(value);
     }
   } else {
-    if (value->type_name() == "Int64Imm") {
+    if (value->type()->number_type() == kNumberTypeInt64) {
       cur_value.push_back(static_cast<int>(GetValue<int64_t>(value)));
     } else {
       cur_value.push_back(GetValue<int>(value));
@@ -321,9 +321,9 @@ void PrimitiveC::GetAttrDataFromInput(const AnfNodePtr inputNode, std::vector<in
       auto tuple = val->cast<ValueTuplePtr>();
       MS_ASSERT(tuple != nullptr);
       for (size_t i = 0; i < tuple->size(); i++) {
-        auto elem = tuple->value()[i]->cast<Int32ImmPtr>();
+        auto elem = tuple->value()[i];
         MS_ASSERT(elem != nullptr);
-        data->emplace_back(static_cast<int>(elem->value()));
+        data->emplace_back(CastToInt(elem, false).front());
       }
     }
   }
@@ -556,6 +556,10 @@ std::shared_ptr<PrimitiveC> PrimitiveC::Create(const Primitive &prim, const std:
     return NewPrimitiveC<ExpandDims>(prim, inputs, quantType);
   } else if (op_type == "UnsortedSegmentSum") {
     return NewPrimitiveC<UnsortedSegmentSum>(prim, inputs, quantType);
+  } else if (op_type == "ResizeNearestNeighbor") {
+    return NewPrimitiveC<Resize>(prim, inputs, quantType);
+  } else if (op_type == "ResizeBilinear") {
+    return NewPrimitiveC<Resize>(prim, inputs, quantType);
 
 #ifdef SUPPORT_TRAIN
   } else if (op_type == "SoftmaxCrossEntropyWithLogits") {

mindspore/lite/src/ops/reshape.cc

@@ -58,9 +58,9 @@ int Reshape::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &in
         auto tuple = val->cast<ValueTuplePtr>();
         MS_ASSERT(tuple != nullptr);
         for (size_t i = 0; i < tuple->size(); ++i) {
-          auto elem = tuple->value()[i]->cast<Int32ImmPtr>();
+          auto elem = tuple->value()[i];
           MS_ASSERT(elem != nullptr);
-          attr->shape.emplace_back(static_cast<int>(elem->value()));
+          attr->shape.emplace_back(CastToInt(elem, false).front());
         }
       } else {
         int dim = CastToInt(val, false).front();

mindspore/lite/src/ops/transpose.cc

@@ -62,9 +62,9 @@ int Transpose::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &
         auto tuple = val->cast<ValueTuplePtr>();
         MS_ASSERT(tuple != nullptr);
         for (size_t i = 0; i < tuple->size(); i++) {
-          auto elem = tuple->value()[i]->cast<Int32ImmPtr>();
+          auto elem = tuple->value()[i];
           MS_ASSERT(elem != nullptr);
-          attr->perm.emplace_back(static_cast<int>(elem->value()));
+          attr->perm.emplace_back(CastToInt(elem, false).front());
         }
       }
     }

mindspore/lite/src/runtime/kernel/arm/base/split_base.h

@@ -32,7 +32,12 @@ class SplitBaseCPUKernel : public LiteKernel {
       : LiteKernel(parameter, inputs, outputs, ctx, primitive), ctx_(ctx), thread_count_(ctx->thread_num_) {
     param = reinterpret_cast<SplitParameter *>(op_parameter_);
   }
-  ~SplitBaseCPUKernel() = default;
+  ~SplitBaseCPUKernel() override {
+    if (param != nullptr && param->split_sizes_ != nullptr) {
+      free(param->split_sizes_);
+      param->split_sizes_ = nullptr;
+    }
+  }
 
   int Init() override;
   int ReSize() override;

mindspore/lite/test/models_onnx.cfg

@@ -31,3 +31,4 @@ mosaic-9.onnx
 pointilism-9.onnx
 rain-princess-9.onnx
 udnie-9.onnx
+adversarial_pruning.onnx

mindspore/lite/test/run_benchmark_nets.sh

@@ -220,13 +220,13 @@ function Run_Converter() {
         fi
     done < ${models_mindspore_mixbit_config}
 
-    # Convert models which do not need to be cared about the accuracy:
+    # Convert models which has several inputs or does not need to be cared about the accuracy:
     while read line; do
-        model_name=${line}
-        model_type=${line##*.}
-        if [[ $model_name == \#* ]] || [[ $model_type == \#* ]]; then
+        if [[ $line == \#* ]]; then
           continue
         fi
+        model_name=${line%%;*}
+        model_type=${model_name##*.}
         case $model_type in
           tflite)
             model_fmk="TFLITE"
@@ -462,17 +462,37 @@ function Run_x86() {
         fi
     done < ${models_mindspore_mixbit_config}
 
-    # Run converted models which do not need to be cared about the accuracy:
+    # Run converted models which has several inputs or does not need to be cared about the accuracy:
     while read line; do
-        model_name=${line}
-        if [[ ${line##*.} == "caffemodel" ]]; then
-          model_name=${line%.*}
+        if [[ $line == \#* ]]; then
+          continue
+        fi
+        model_name=${line%%;*}
+        model_name_len=${#model_name}
+        input_params=${line:model_name_len+1}
+        input_num=${input_params%%;*}
+        input_shape=${input_params##*;}
+        input_files=''
+        output_file=''
+        if [[ -z "$input_files" || $input_files == 1 ]] && [ -e ${ms_models_path}/${model_name}'.ms.bin' ]; then
+          input_files=$model_name'.ms.bin'
+        elif [[ ! -z "$input_files" && $input_files > 1 ]]; then
+          for i in $(seq 1 $input_num)
+          do
+            input_files=$input_files$model_name'.ms.bin_'$i','
+          done
+        fi
+        if [ -e ${ms_models_path}/${model_name}'.ms.out' ]; then
+          output_file=${ms_models_path}/${model_name}'.ms.out'
+        fi
+        if [[ ${model_name##*.} == "caffemodel" ]]; then
+          model_name=${model_name%.*}
         fi
         echo ${model_name} >> "${run_x86_log_file}"
         echo 'cd  '${x86_path}'/mindspore-lite-'${version}'-runtime-x86-'${process_unit_x86} >> "{run_x86_log_file}"
         cd ${x86_path}/mindspore-lite-${version}-runtime-x86-${process_unit_x86} || return 1
-        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile='${ms_models_path}'/'${model_name}'.ms --loopCount=1 --warmUpLoopCount=0' >> "${run_x86_log_file}"
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile=${ms_models_path}/${model_name}.ms  --loopCount=1 --warmUpLoopCount=0 >> "${run_x86_log_file}"
+        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile='${ms_models_path}'/'${model_name}'.ms --inDataFile='${input_files}' --benchmarkDataFile='${output_file}' --loopCount=1 --warmUpLoopCount=0' >> "${run_x86_log_file}"
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile=${ms_models_path}/${model_name}.ms --inDataFile=${input_files} --benchmarkDataFile=${output_file} --loopCount=1 --warmUpLoopCount=0 >> "${run_x86_log_file}"
         if [ $? = 0 ]; then
             run_result='x86: '${model_name}' pass'; echo ${run_result} >> ${run_benchmark_result_file}
         else
@@ -690,17 +710,34 @@ function Run_x86_sse() {
         fi
     done < ${models_mindspore_mixbit_config}
 
-    # Run converted models which do not need to be cared about the accuracy:
+    # Run converted models which has several inputs or does not need to be cared about the accuracy:
     while read line; do
-        model_name=${line}
-        if [[ ${line##*.} == "caffemodel" ]]; then
-          model_name=${line%.*}
+        model_name=${line%%;*}
+        model_name_len=${#model_name}
+        input_params=${line:model_name_len+1}
+        input_num=${input_params%%;*}
+        input_shape=${input_params##*;}
+        input_files=''
+        output_file=''
+        if [[ -z "$input_files" || $input_files == 1 ]] && [ -e ${ms_models_path}/${model_name}'.ms.bin' ]; then
+          input_files=$model_name'.ms.bin'
+        elif [[ ! -z "$input_files" && $input_files > 1 ]]; then
+          for i in $(seq 1 $input_num)
+          do
+            input_files=$input_files$model_name'.ms.bin_'$i','
+          done
+        fi
+        if [ -e ${ms_models_path}/${model_name}'.ms.out' ]; then
+          output_file=${ms_models_path}/${model_name}'.ms.out'
+        fi
+        if [[ ${model_name##*.} == "caffemodel" ]]; then
+          model_name=${model_name%.*}
         fi
         echo ${model_name} >> "${run_x86_sse_log_file}"
         echo 'cd  '${x86_path}'/mindspore-lite-'${version}'-runtime-x86-sse-'${process_unit_x86} >> "{run_x86_sse_log_file}"
         cd ${x86_path}/mindspore-lite-${version}-runtime-x86-sse-${process_unit_x86} || return 1
-        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile='${ms_models_path}'/'${model_name}'.ms --loopCount=1 --warmUpLoopCount=0' >> "${run_x86_sse_log_file}"
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile=${ms_models_path}/${model_name}.ms  --loopCount=1 --warmUpLoopCount=0 >> "${run_x86_sse_log_file}"
+        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile='${ms_models_path}'/'${model_name}'.ms --inDataFile='${input_files}' --benchmarkDataFile='${output_file}' --loopCount=1 --warmUpLoopCount=0' >> "${run_x86_sse_log_file}"
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./lib:./third_party/libjpeg-turbo/lib:./third_party/opencv/lib;./benchmark/benchmark --modelFile=${ms_models_path}/${model_name}.ms --inDataFile=${input_files} --benchmarkDataFile=${output_file} --loopCount=1 --warmUpLoopCount=0 >> "${run_x86_sse_log_file}"
         if [ $? = 0 ]; then
             run_result='x86_sse: '${model_name}' pass'; echo ${run_result} >> ${run_benchmark_result_file}
         else
@@ -1083,6 +1120,41 @@ function Run_arm64() {
             run_result='arm64: '${model_name}'_train failed'; echo ${run_result} >> ${run_benchmark_result_file}; return 1
         fi
     done < ${models_mindspore_weightquant_config}
+
+    # Run converted models which has several inputs or does not need to be cared about the accuracy:
+    while read line; do
+        model_name=${line%%;*}
+        model_name_len=${#model_name}
+        input_params=${line:model_name_len+1}
+        input_num=${input_params%%;*}
+        input_shape=${input_params##*;}
+        input_files=''
+        output_file=''
+        if [[ -z "$input_files" || $input_files == 1 ]] && [ -e ${ms_models_path}/${model_name}'.ms.bin' ]; then
+          input_files=$model_name'.ms.bin'
+        elif [[ ! -z "$input_files" && $input_files > 1 ]]; then
+          for i in $(seq 1 $input_num)
+          do
+            input_files=$input_files$model_name'.ms.bin_'$i','
+          done
+        fi
+        if [ -e ${ms_models_path}/${model_name}'.ms.out' ]; then
+          output_file=${ms_models_path}/${model_name}'.ms.out'
+        fi
+        if [[ ${model_name##*.} == "caffemodel" ]]; then
+          model_name=${model_name%.*}
+        fi
+        echo ${model_name} >> "${run_arm64_log_file}"
+        echo 'cd  /data/local/tmp/benchmark_test' > adb_run_cmd.txt
+        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/benchmark_test;./benchmark --modelFile='${model_name}'.ms --inDataFile='${input_files}' --benchmarkDataFile='${output_file} >> "${run_arm64_log_file}"
+        echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp/benchmark_test;./benchmark --modelFile='${model_name}'.ms --inDataFile='${input_files}' --benchmarkDataFile='${output_file} >> adb_run_cmd.txt
+        adb -s ${device_id} shell < adb_run_cmd.txt >> "${run_arm64_log_file}"
+        if [ $? = 0 ]; then
+            run_result='arm64: '${model_name}' pass'; echo ${run_result} >> ${run_benchmark_result_file}
+        else
+            run_result='arm64: '${model_name}' failed'; echo ${run_result} >> ${run_benchmark_result_file}; return 1
+        fi
+    done < ${models_only_for_process_config}
 }
 
 # Run on arm32 platform:
@@ -1229,7 +1301,7 @@ models_mindspore_weightquant_config=${basepath}/models_mindspore_weightquant.cfg
 models_fp16_gpu_config=${basepath}/models_fp16_gpu.cfg
 models_arm32_config=${basepath}/models_arm32.cfg
 models_compatibility_config=${basepath}/models_compatibility.cfg
-models_only_for_process_config=${basepath}/models_only_for_process.cfg
+models_only_for_process_config=${basepath}/models_with_several_inputs_or_without_outputs.cfg
 
 ms_models_path=${basepath}/ms_models
 

mindspore/lite/tools/anf_exporter/anf_exporter.cc

@@ -313,10 +313,10 @@ int AnfExporter::ConvertInputCNode(const std::shared_ptr<AnfNode> input_anode, s
       MS_LOG(ERROR) << "cast to ValueNode failed";
       return RET_ERROR;
     }
-    auto input_index_key =
-      get_item_input_cnode->fullname_with_scope() + "_o:" +
-      std::to_string(value_node->value()->type_name() == "Int64Imm" ? GetValue<int64_t>(value_node->value())
-                                                                    : GetValue<int>(value_node->value()));
+    auto input_index_key = get_item_input_cnode->fullname_with_scope() + "_o:" +
+                           std::to_string(value_node->value()->type()->number_type() == kNumberTypeInt64
+                                            ? GetValue<int64_t>(value_node->value())
+                                            : GetValue<int>(value_node->value()));
     auto iter = node_id_map_.find(input_index_key);
     if (iter == node_id_map_.end()) {
 #ifdef SUPPORT_TRAIN
@@ -415,8 +415,7 @@ int AnfExporter::ConvertInputValueNode(std::shared_ptr<AnfNode> input_anode,
     paramTensor->dataType = typePtr->type_id();
     paramTensor->dims = {1};
     paramTensor->nodeType = schema::NodeType::NodeType_ValueNode;
-    auto data = value->cast<mindspore::Int32ImmPtr>();
-    int real_data = GetValue<int32_t>(data);
+    int real_data = CastToInt(value, false).front();
     paramTensor->data.resize(sizeof(int32_t));
     memcpy(paramTensor->data.data(), &real_data, sizeof(int32_t));
     node_id_map_[valueNode->fullname_with_scope()] = meta_graphT->allTensors.size();

mindspore/lite/tools/common/graph_util.cc

@@ -669,7 +669,7 @@ STATUS ChangeOpAxis(schema::MetaGraphT *graph, const std::unique_ptr<schema::CNo
   MS_ASSERT(node->primitive->value != nullptr);
   auto type = node->primitive->value.type;
   auto input1_ndim = graph->allTensors.at(node->inputIndex[0])->dims.size();
-  if (input1_ndim != 4 && input1_ndim != 0) {
+  if (input1_ndim != 4) {
     if (node->inputIndex.size() > 1) {
       auto input2_ndim = graph->allTensors.at(node->inputIndex[1])->dims.size();
       if (input2_ndim != 4 && input2_ndim != 0) {