fix output shape error in deconv operator and support parameter passing when doing infershape during inference

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

@@ -22,6 +22,7 @@
 #include "src/runtime/kernel/arm/int8/add_int8.h"
 #include "src/runtime/kernel/arm/int8/mul_int8.h"
 #include "src/runtime/runtime_api.h"
+#include "src/populate_parameter.h"
 
 using mindspore::kernel::KERNEL_ARCH::kCPU;
 using mindspore::lite::KernelRegistrar;
@@ -40,6 +41,31 @@ int ArithmeticCPUKernel::Init() {
   return ReSize();
 }
 
+int ArithmeticCPUKernel::PreProcess() {
+  if (!InferShapeDone()) {
+    (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->SetInferFlag(true);
+    auto ret = (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->InferShape(in_tensors_, out_tensors_);
+    if (ret != 0) {
+      (const_cast<mindspore::lite::PrimitiveC *>(primitive_))->SetInferFlag(false);
+      MS_LOG(ERROR) << "InferShape fail!";
+      return ret;
+    }
+    arithmeticParameter_ = reinterpret_cast<ArithmeticParameter *>(kernel::PopulateArithmetic(primitive_));
+    ret = ReSize();
+    if (ret != 0) {
+      MS_LOG(ERROR) << "ReSize fail!ret: " << ret;
+      return ret;
+    }
+  }
+
+  auto outputs = this->out_tensors();
+  for (auto *output : outputs) {
+    MS_ASSERT(output != nullptr);
+    output->MallocData();
+  }
+  return RET_OK;
+}
+
 int ArithmeticCPUKernel::ReSize() {
   if (in_tensors_[0]->data_type() == kNumberTypeFloat32 || in_tensors_[0]->data_type() == kNumberTypeFloat16) {
     data_type_ = kDataTypeFloat;

mindspore/lite/src/runtime/kernel/arm/fp32/arithmetic.h

@@ -163,6 +163,7 @@ class ArithmeticCPUKernel : public LiteKernel {
   ~ArithmeticCPUKernel() override;
 
   int Init() override;
+  int PreProcess() override;
   int ReSize() override;
   int Run() override;
   int DoArithmetic(int task_id);

mindspore/lite/src/runtime/kernel/arm/int8/add_int8.cc

@@ -81,7 +81,7 @@ int QuantizedAddCPUKernel::Run() {
   input1_data_ = static_cast<int8_t *>(in_tensors_.at(1)->MutableData());
   output_data_ = static_cast<int8_t *>(out_tensors_.at(0)->MutableData());
 
-  elements_num_ = in_tensors_.at(0)->ElementsNum();
+  elements_num_ = out_tensors_.at(0)->ElementsNum();
   count_unit_ = thread_count_ > 1 ? UP_DIV(elements_num_, thread_count_) : elements_num_;
 
   if (in_tensors_.at(0)->ElementsNum() != in_tensors_.at(1)->ElementsNum()) {

mindspore/lite/src/runtime/kernel/arm/int8/mul_int8.cc

@@ -106,7 +106,7 @@ int MulInt8CPUKernel::Run() {
   input1_data_ = static_cast<int8_t *>(in_tensors_.at(1)->MutableData());
   output_data_ = static_cast<int8_t *>(out_tensors_.at(0)->MutableData());
 
-  elements_num_ = in_tensors_.at(0)->ElementsNum();
+  elements_num_ = out_tensors_.at(0)->ElementsNum();
   count_unit_ = thread_count_ > 1 ? UP_DIV(elements_num_, thread_count_) : elements_num_;
   if (in_tensors_.at(0)->ElementsNum() != in_tensors_.at(1)->ElementsNum()) {
     input0_data_ = static_cast<int8_t *>(ctx_->allocator->Malloc(out_tensors_.at(0)->Size()));

mindspore/lite/tools/converter/parser/onnx/onnx_deconv_parser.cc

@@ -87,8 +87,8 @@ STATUS OnnxDeConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::N
         MS_LOG(ERROR) << "dilations size " << onnx_node_attr.ints().size() << " is not 2";
         return RET_ERROR;
       }
-      attr->dilateW = static_cast<int32_t>(onnx_node_attr.ints(0));
-      attr->dilateH = static_cast<int32_t>(onnx_node_attr.ints(1));
+      attr->dilateH = static_cast<int32_t>(onnx_node_attr.ints(0));
+      attr->dilateW = static_cast<int32_t>(onnx_node_attr.ints(1));
     } else if (onnx_node_attr.name() == "kernels") {
       if (onnx_node_attr.ints().size() != 2) {
         MS_LOG(ERROR) << "kernel_shape size " << onnx_node_attr.ints().size() << " is not 2";
@@ -101,8 +101,8 @@ STATUS OnnxDeConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::N
         MS_LOG(ERROR) << "kernel_shape size " << onnx_node_attr.ints().size() << " is not 2";
         return RET_ERROR;
       }
-      attr->kernelW = static_cast<int32_t>(onnx_node_attr.ints(0));
-      attr->kernelH = static_cast<int32_t>(onnx_node_attr.ints(1));
+      attr->kernelH = static_cast<int32_t>(onnx_node_attr.ints(0));
+      attr->kernelW = static_cast<int32_t>(onnx_node_attr.ints(1));
     } else if (onnx_node_attr.name() == "auto_pad") {
       attr->padMode = GetOnnxPadMode(onnx_node_attr);
     } else if (onnx_node_attr.name() == "pads") {
@@ -119,8 +119,8 @@ STATUS OnnxDeConvParser::Parse(const onnx::GraphProto &onnx_graph, const onnx::N
         MS_LOG(ERROR) << "strides size " << onnx_node_attr.ints().size() << " is not 2";
         return RET_ERROR;
       }
-      attr->strideW = static_cast<int32_t>(onnx_node_attr.ints(0));
-      attr->strideH = static_cast<int32_t>(onnx_node_attr.ints(1));
+      attr->strideH = static_cast<int32_t>(onnx_node_attr.ints(0));
+      attr->strideW = static_cast<int32_t>(onnx_node_attr.ints(1));
     } else if (onnx_node_attr.name() == "order") {
       if (onnx_node_attr.s() == "NHWC") {
         attr->format = schema::Format::Format_NHWC;