fix bug of parser for mindir models.

mindspore/lite/nnacl/fp32/arithmetic_fp32.c

@@ -509,6 +509,23 @@ int ElementOptDivRelu6(const float *input0, const float *input1, float *output,
   return NNACL_OK;
 }
 
+int ElementOptDivInt(const int *input0, const int *input1, int *output, const int element_size,
+                     const ArithmeticParameter *param) {
+  if (param->in_elements_num0_ == 1) {
+    for (int index = 0; index < element_size; index++) {
+      output[index] = input0[0] / input1[index];
+    }
+  } else {
+    if (input1[0] == 0) {
+      return NNACL_ERRCODE_DIVISOR_ZERO;
+    }
+    for (int index = 0; index < element_size; index++) {
+      output[index] = input0[index] / input1[0];
+    }
+  }
+  return NNACL_OK;
+}
+
 int ElementMul(const float *input0, const float *input1, float *output, const int element_size) {
   int index = 0;
 #ifdef ENABLE_NEON

mindspore/lite/nnacl/fp32/arithmetic_fp32.h

@@ -58,6 +58,8 @@ int ElementOptDivRelu(const float *input0, const float *input1, float *output, c
                       const ArithmeticParameter *param);
 int ElementOptDivRelu6(const float *input0, const float *input1, float *output, const int element_size,
                        const ArithmeticParameter *param);
+int ElementOptDivInt(const int *input0, const int *input1, int *output, const int element_size,
+                     const ArithmeticParameter *param);
 int ElementMul(const float *input0, const float *input1, float *output, const int element_size);
 int ElementMulRelu(const float *input0, const float *input1, float *output, const int element_size);
 int ElementMulRelu6(const float *input0, const float *input1, float *output, const int element_size);

mindspore/lite/src/ops/activation.cc

@@ -59,6 +59,8 @@ int Activation::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr>
     attr->type = schema::ActivationType_HSWISH;
   } else if (prim.name() == "HSigmoid") {
     attr->type = schema::ActivationType_HSIGMOID;
+  } else if (prim.name() == "Tanh") {
+    attr->type = schema::ActivationType_TANH;
   }
   this->primitive_->value.value = attr.release();
   if (this->primitive_->value.value == nullptr) {

mindspore/lite/src/ops/div.cc

@@ -29,6 +29,35 @@ void Div::SetActivationType(int activation_type) {
   this->primitive_->value.AsDiv()->activationType = (schema::ActivationType)activation_type;
 }
 
+int Div::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) {
+  if (this->primitive_ == nullptr) {
+    this->primitive_ = new (std::nothrow) schema::PrimitiveT;
+    if (this->primitive_ == nullptr) {
+      MS_LOG(ERROR) << "new primitiveT failed";
+      return RET_ERROR;
+    }
+    this->primitive_->value.type = schema::PrimitiveType_Div;
+  }
+  if (this->primitive_->value.type != schema::PrimitiveType_Div) {
+    MS_LOG(ERROR) << "Primitive type is error :" << this->primitive_->value.type;
+    return RET_ERROR;
+  }
+  if (this->primitive_->value.value == nullptr) {
+    auto attr = new (std::nothrow) schema::DivT();
+    if (attr == nullptr) {
+      MS_LOG(ERROR) << "new primitiveT value failed";
+      return RET_ERROR;
+    }
+    this->primitive_->value.value = attr;
+    if (this->primitive_->value.value == nullptr) {
+      MS_LOG(ERROR) << "primitive value is nullptr";
+      return RET_ERROR;
+    }
+  }
+
+  return RET_OK;
+}
+
 #else
 int Div::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
   MS_ASSERT(nullptr != primitive);

mindspore/lite/src/ops/div.h

@@ -32,6 +32,7 @@ class Div : public Arithmetic {
   MS_DECLARE_PARENT(Div, Arithmetic);
   explicit Div(schema::PrimitiveT *primitive) : Arithmetic(primitive) {}
   void SetActivationType(int activation_type);
+  int UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) override;
 
 #else
   int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;

mindspore/lite/src/ops/primitive_c.cc

@@ -581,6 +581,10 @@ std::shared_ptr<PrimitiveC> PrimitiveC::Create(const Primitive &prim, const std:
     return NewPrimitiveC<Floor>(prim, inputs, quantType);
   } else if (op_type == "Minimum") {
     return NewPrimitiveC<Minimum>(prim, inputs, quantType);
+  } else if (op_type == "Div") {
+    return NewPrimitiveC<Div>(prim, inputs, quantType);
+  } else if (op_type == "Tanh") {
+    return NewPrimitiveC<Activation>(prim, inputs, quantType);
 #ifdef SUPPORT_TRAIN
   } else if (op_type == "SoftmaxCrossEntropyWithLogits") {
     return NewPrimitiveC<SoftmaxCrossEntropy>(prim, inputs, quantType);

mindspore/lite/src/runtime/kernel/arm/fp32/arithmetic_fp32.cc

@@ -297,6 +297,7 @@ void ArithmeticCPUKernel::InitOptRunFunction() {
           default:
             arithmeticParameter_->broadcasting_ = false;
             arithmetic_opt_run_ = ElementOptDiv;
+            arithmetic_opt_run_int_ = ElementOptDivInt;
             break;
         }
         break;
@@ -554,4 +555,5 @@ REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_FloorDiv, CpuArithmeticFp32Kern
 REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_FloorMod, CpuArithmeticFp32KernelCreator)
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_SquaredDifference, CpuArithmeticFp32KernelCreator)
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Eltwise, CpuArithmeticFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeInt32, PrimitiveType_Div, CpuArithmeticFp32KernelCreator)
 }  // namespace mindspore::kernel

mindspore/lite/tools/anf_exporter/anf_exporter.cc

@@ -445,7 +445,8 @@ int AnfExporter::ConvertInputValueNode(const std::shared_ptr<AnfNode> &input_ano
     auto valueAbstract = valueNode->abstract();
     auto abstractScalar = utils::cast<abstract::AbstractScalarPtr>(valueAbstract);
     auto typePtr = abstractScalar->GetTypeTrack();
-    paramTensor->dataType = typePtr->type_id();
+    // data of int64 is converted to int32 here.
+    paramTensor->dataType = kNumberTypeInt32;
     paramTensor->dims = {1};
     paramTensor->nodeType = schema::NodeType::NodeType_ValueNode;
     int real_data = CastToInt(value).front();

model_zoo/official/lite/style_transfer/README.md

@@ -1,4 +1,4 @@
-# MindSpore Lite 端侧骨骼检测demo（Android）
+# MindSpore Lite 端侧风格迁移demo（Android）
 
 本示例程序演示了如何在端侧利用MindSpore Lite API以及MindSpore Lite风格迁移模型完成端侧推理，根据demo内置的标准图片更换目标图片的艺术风格，并在App图像预览界面中显示出来。