remove op_parameter's infer_flag_

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/add_sub_grad_infer.c

@@ -33,7 +33,7 @@ int AddSubGradInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   TensorC *dx1 = outputs[0];
   TensorC *dx2 = outputs[1];
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/addn_infer.c

@@ -32,7 +32,7 @@ int AddnInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
     return NNACL_ERR;
   }
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/argmin_max_infer.c

@@ -49,7 +49,7 @@ int ArgMinMaxInferShape(const TensorC *const *inputs, const size_t inputs_size,
   if (output_2 != NULL) {
     SetDataTypeFormat(output_2, input);
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int output_shape[MAX_SHAPE_SIZE] = {0};

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/arithmetic_infer.c

@@ -83,7 +83,7 @@ int ArithmeticInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   size_t input_shape1_size = input1->shape_size_;
   SetOutputDtypeFormat(input0, input1, output);
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input_shape0_size >= MAX_SHAPE_SIZE || input_shape1_size >= MAX_SHAPE_SIZE) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/audio_spectrogram_infer.c

@@ -50,7 +50,7 @@ int AudioSpectrogramInferShape(const TensorC *const *inputs, size_t inputs_size,
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ != 2) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/batch_to_space_infer.c

@@ -115,7 +115,7 @@ int BatchToSpaceInferShape(const TensorC *const *inputs, size_t inputs_size, Ten
     return NNACL_ERR;
   }
   SetDataTypeFormat(outputs[0], input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/broadcast_to_infer.c

@@ -28,7 +28,7 @@ int BroadcastToInferShape(const TensorC *const *inputs, size_t inputs_size, Tens
 
   const TensorC *input = inputs[0];
   SetDataTypeFormat(outputs[0], input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   BroadcastToParameter *param = (BroadcastToParameter *)parameter;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/cast_infer.c

@@ -31,7 +31,7 @@ int CastInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   output->format_ = input->format_;
   const TensorC *dst_type = inputs[1];
   output->data_type_ = *((int *)dst_type->data_);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->data_type_ != kNumberTypeBool && input->data_type_ != kNumberTypeUInt8 &&

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/common_infer.c

@@ -343,7 +343,7 @@ int CommonInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
     return NNACL_NULL_PTR;
   }
   SetDataTypeFormat(outputs[0], inputs[0]);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(outputs[0], inputs[0]);
@@ -356,7 +356,7 @@ int FftInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **ou
   TensorC *output = outputs[0];
   output->data_type_ = kNumberTypeFloat32;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int input_shape[MAX_SHAPE_SIZE] = {0};
@@ -454,6 +454,30 @@ void VectorCFree(VectorC *vc) {
   vc->data_ = NULL;
 }
 
+bool InferFlag(const TensorC *const *inputs, size_t inputs_size) {
+  if (inputs == NULL) {
+    return false;
+  }
+  for (size_t i = 0; i < inputs_size; i++) {
+    if (inputs[i] == NULL) {
+      return false;
+    }
+    if (inputs[i]->data_type_ == kObjectTypeTensorType) {
+      TensorListC *input_tensor_list = (TensorListC *)inputs[i];
+      if (input_tensor_list->shape_value_ == -1) {
+        return false;
+      }
+    } else {
+      for (size_t j = 0; j < inputs[i]->shape_size_; ++j) {
+        if (inputs[i]->shape_[j] == -1) {
+          return false;
+        }
+      }
+    }
+  }
+  return true;
+}
+
 REG_INFER(Abs, PrimType_Abs, CommonInferShape)
 REG_INFER(AbsGrad, PrimType_AbsGrad, CommonInferShape)
 REG_INFER(Activation, PrimType_Activation, CommonInferShape)

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/common_infer.h

@@ -139,7 +139,7 @@ typedef struct TensorListC {
   bool is_ready_;
   int data_type_;
   int format_;
-
+  int shape_value_;
   int tensors_data_type_;  // element_data_type_, keep same as c++
   int max_elements_num_;
   int element_shape_[8];
@@ -204,6 +204,7 @@ int VectorCInsert(VectorC *vc, int index, int value);
 void VectorCErase(VectorC *vc, int index);
 bool VectorCEqual(VectorC *vc1, VectorC *vc2);
 void VectorCFree(VectorC *vc);
+bool InferFlag(const TensorC *const *inputs, size_t inputs_size);
 
 #ifdef __cplusplus
 }

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/concat_infer.c

@@ -29,7 +29,7 @@ int ConcatInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   const TensorC *input0 = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input0);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/constant_of_shape_infer.c

@@ -31,7 +31,7 @@ int ConstantOfShapeInferShape(const TensorC *const *inputs, size_t inputs_size,
   ConstantOfShapeParameter *param = (ConstantOfShapeParameter *)parameter;
   out_tensor->data_type_ = (TypeIdC)(param->data_type_);
   out_tensor->format_ = in_tensor->format_;
-  if (!parameter->infer_flag_ || in_tensor->data_ == NULL) {
+  if (!InferFlag(inputs, inputs_size) || in_tensor->data_ == NULL) {
     return NNACL_INFER_INVALID;
   }
   int size = GetElementNum(in_tensor);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/conv2d_infer.c

@@ -69,7 +69,7 @@ int Conv2dInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
     param->group_ = weight_tensor->shape_[0];
   }
   param->output_channel_ = weight_tensor->shape_[0];
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   const int *in_shape = input_tensor->shape_;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/crop_and_resize_infer.c

@@ -27,16 +27,14 @@ int CropAndResizeInferShape(const TensorC *const *inputs, size_t inputs_size, Te
 #endif
 
   const TensorC *input = inputs[0];
-  if (input->shape_size_ != 0 && input->shape_size_ != 4) {
-    return NNACL_ERR;
-  }
-
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
-
+  if (input->shape_size_ != 0 && input->shape_size_ != 4) {
+    return NNACL_ERR;
+  }
   int output_shape[MAX_SHAPE_SIZE] = {0};
   size_t output_shape_size = 0;
   if (inputs[1]->data_ != NULL) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/crop_infer.c

@@ -27,7 +27,7 @@ int CropInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
 #endif
 
   SetDataTypeFormat(outputs[0], inputs[0]);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(outputs[0], inputs[1]);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/cumsum_infer.c

@@ -29,7 +29,7 @@ int CumsumInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/deconv2d_infer.c

@@ -36,7 +36,7 @@ int Deconv2dInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   if (param->group_ == 0) {
     param->group_ = weight->shape_[0];
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int32_t input_h = GetHeight(input);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/dedepthwise_conv2d_infer.c

@@ -28,7 +28,7 @@ int DeDepthwiseConv2DInferShape(const TensorC *const *inputs, size_t inputs_size
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int input_h = input->shape_[1];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/depth_to_space_infer.c

@@ -32,7 +32,7 @@ int DepthToSpaceInferShape(const TensorC *const *inputs, size_t inputs_size, Ten
   }
   SetDataTypeFormat(outputs[0], input);
   DepthToSpaceParameter *param = (DepthToSpaceParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int input_shape[MAX_SHAPE_SIZE] = {0};

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/depthwise_conv2d_infer.c

@@ -30,7 +30,7 @@ int DepthwiseConv2dInferShape(const TensorC *const *inputs, size_t inputs_size,
   SetDataTypeFormat(output, input);
   ConvParameter *param = (ConvParameter *)parameter;
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int input_h = input->shape_[1];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/detection_post_process_infer.c

@@ -57,7 +57,7 @@ int DetectionPostProcessInferShape(const TensorC *const *inputs, size_t inputs_s
   detected_scores->data_type_ = kNumberTypeFloat32;
   num_det->format_ = boxes->format_;
   num_det->data_type_ = kNumberTypeFloat32;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   const int max_detections = param->max_detections_;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/dropout_grad_infer.c

@@ -29,7 +29,7 @@ int DropoutGradInferShape(const TensorC *const *inputs, size_t inputs_size, Tens
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(output, input);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/dropout_infer.c

@@ -29,7 +29,7 @@ int DropoutInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   const TensorC *input = inputs[0];
   TensorC *output0 = outputs[0];
   SetDataTypeFormat(output0, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(output0, input);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/embedding_lookup_infer.c

@@ -33,7 +33,7 @@ int EmbeddingLookupInferShape(const TensorC *const *inputs, size_t inputs_size,
   const TensorC *ids = inputs[inputs_size - 1];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, params_);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/expand_dims_infer.c

@@ -29,7 +29,7 @@ int ExpandDimsInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/fill_infer.c

@@ -35,7 +35,7 @@ int FillInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   for (size_t i = 0; i < dst_shape_tensor->shape_size_; ++i) {
     num_dims *= dst_shape_tensor->shape_[i];
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (num_dims != 0 && dst_shape == NULL) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/flatten_grad_infer.c

@@ -30,7 +30,7 @@ int FlattenGradInferShape(const TensorC *const *inputs, size_t inputs_size, Tens
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/flatten_infer.c

@@ -30,7 +30,7 @@ int FlattenInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/full_connection_infer.c

@@ -31,7 +31,7 @@ int FullConnectionInferShape(const TensorC *const *inputs, size_t inputs_size, T
   TensorC *output = outputs[0];
   MatMulParameter *param = (MatMulParameter *)parameter;
   SetDataTypeFormat(output, input0);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if ((param->has_bias_ && inputs_size != 3) || (!param->has_bias_ && inputs_size != 2)) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/fused_batchnorm_infer.c

@@ -38,7 +38,7 @@ int FusedBatchNormInferShape(const TensorC *const *inputs, size_t inputs_size, T
     outputs[5]->shape_size_ = 1;
     outputs[5]->shape_[0] = 1;
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   return NNACL_OK;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/gather_infer.c

@@ -30,7 +30,7 @@ int GatherInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
     output->data_type_ = kNumberTypeFloat32;
   }
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int axis = *((int *)inputs[2]->data_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/gather_nd_infer.c

@@ -31,7 +31,7 @@ int GatherNdInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int in_rank = input->shape_size_;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/gru_infer.c

@@ -34,7 +34,7 @@ int GruInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **ou
   for (int i = 0; i < 2; i++) {
     SetDataTypeFormat(outputs[i], input);
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/invert_permutation_infer.c

@@ -29,7 +29,7 @@ int InvertPermutationInferShape(const TensorC *const *inputs, size_t inputs_size
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->data_type_ != kNumberTypeInt32) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/layer_norm_infer.c

@@ -34,7 +34,7 @@ int LayerNormInferShape(const TensorC *const *inputs, size_t inputs_size, Tensor
   SetDataTypeFormat(output, input);
 
   LayerNormParameter *param = (LayerNormParameter *)parameter;
-  if (!param->op_parameter_.infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   param->begin_norm_axis_ =

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/lin_space_infer.c

@@ -31,7 +31,7 @@ int LinSpaceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
 
   output->data_type_ = input->data_type_;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int *num = (int *)(inputs[2]->data_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/log_softmax_infer.c

@@ -31,7 +31,7 @@ int LogSoftmaxInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
 
   output->data_type_ = input->data_type_;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ > 5) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/lstm_infer.c

@@ -34,7 +34,7 @@ int LstmInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   }
 
   LstmParameter *param = (LstmParameter *)parameter;
-  if (!param->op_parameter_.infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/matmul_infer.c

@@ -32,7 +32,7 @@ int MatmulInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
 
   SetDataTypeFormat(output, input0);
   MatMulParameter *param = (MatMulParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/max_min_grad_infer.c

@@ -33,7 +33,7 @@ int MaxMinGradInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   TensorC *dx1 = outputs[0];
   TensorC *dx2 = outputs[1];
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/mean_infer.c

@@ -28,7 +28,7 @@ int MeanInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   ReduceParameter *param = (ReduceParameter *)parameter;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/merge_infer.c

@@ -71,11 +71,6 @@ int MergeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
   }
 #endif
 
-  if (!parameter->infer_flag_) {
-    MergeDataTypeInfer((struct TensorC **)inputs, inputs_size, outputs, outputs_size);
-    return NNACL_INFER_INVALID;
-  }
-
   const TensorC *const *left_part_inputs = inputs;
   size_t left_part_inputs_size = inputs_size / 2;
 
@@ -90,6 +85,7 @@ int MergeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
     return MergeInfer((TensorC **)right_part_inputs, right_part_inputs_size, outputs, outputs_size);
   }
 
+  MergeDataTypeInfer((struct TensorC **)inputs, inputs_size, outputs, outputs_size);
   return NNACL_INFER_INVALID;
 }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/mfcc_infer.c

@@ -29,7 +29,7 @@ int MfccInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ != 3) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/one_hot_infer.c

@@ -38,7 +38,7 @@ int OneHotInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
     return NNACL_NULL_PTR;
   }
   SetDataTypeFormat(output, on_value);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   OneHotParameter *param = (OneHotParameter *)parameter;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/pad_infer.c

@@ -30,7 +30,7 @@ int PadInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **ou
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
   PadParameter *param = (PadParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/pooling_infer.c

@@ -31,7 +31,7 @@ int PoolingInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
   PoolingParameter *param = (PoolingParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int input_h = input->shape_[1];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/power_infer.c

@@ -40,7 +40,7 @@ int PowerInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
   TensorC *output_tensor = outputs[0];
 
   SetDataTypeFormat(output_tensor, x_tensor);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (exp_tensor != NULL) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/prior_box_infer.c

@@ -31,7 +31,7 @@ int PriorBoxInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   TensorC *output = outputs[0];
   output->data_type_ = kNumberTypeFloat32;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   float different_aspect_ratios[MAX_SHAPE_SIZE * 2 + 1];  // NOTE: flip double the number

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/quant_dtype_cast_infer.c

@@ -32,7 +32,7 @@ int QuantDtypeCastInferShape(const TensorC *const *inputs, size_t inputs_size, T
   QuantDtypeCastParameter *param = (QuantDtypeCastParameter *)parameter;
   output->data_type_ = param->dstT_;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(output, input);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/random_standard_normal_infer.c

@@ -27,7 +27,7 @@ int RandomStandardNormalInferShape(const TensorC *const *inputs, size_t inputs_s
 #endif
   outputs[0]->data_type_ = kNumberTypeFloat32;
   outputs[0]->format_ = inputs[0]->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/range_infer.c

@@ -39,7 +39,7 @@ int RangeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
     output->data_type_ = kNumberTypeInt32;
   }
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/rank_infer.c

@@ -29,7 +29,7 @@ int RankInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   output->shape_size_ = 1;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/reduce_infer.c

@@ -63,7 +63,7 @@ int ReduceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
   ReduceParameter *param = (ReduceParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   bool keep_dims = param->keep_dims_;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/reshape_infer.c

@@ -142,7 +142,7 @@ int ReshapeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
   ReshapeParameter *param = (ReshapeParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/resize_grad_infer.c

@@ -32,7 +32,7 @@ int ResizeGradInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   }
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   const TensorC *input_1 = inputs[1];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/resize_infer.c

@@ -116,17 +116,15 @@ int ResizeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
 #endif
 
   const TensorC *input = inputs[0];
-  if (input->shape_size_ != 0 && input->shape_size_ != 4) {
-    return NNACL_ERR;
-  }
   TensorC *output = outputs[0];
-
   SetDataTypeFormat(output, input);
-  ResizeParameter *param = (ResizeParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
-
+  if (input->shape_size_ != 0 && input->shape_size_ != 4) {
+    return NNACL_ERR;
+  }
+  ResizeParameter *param = (ResizeParameter *)parameter;
   int output_shape[MAX_SHAPE_SIZE] = {0};
   size_t output_shape_size = 0;
   ShapePush(output_shape, &output_shape_size, GetBatch(input));

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/rfft_infer.c

@@ -30,7 +30,7 @@ int RfftInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   TensorC *output = outputs[0];
   output->data_type_ = kNumberTypeComplex64;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   ShapeSet(output->shape_, &(output->shape_size_), input->shape_, input->shape_size_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/roi_pooling_infer.c

@@ -30,7 +30,7 @@ int ROIPoolingInferShape(const TensorC *const *inputs, size_t inputs_size, Tenso
   const TensorC *roi = inputs[1];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/scatter_nd_infer.c

@@ -34,7 +34,7 @@ int ScatterNdInferShape(const TensorC *const *inputs, size_t inputs_size, Tensor
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, update);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int *shape_data = (int *)(shape->data_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/select_infer.c

@@ -28,7 +28,7 @@ int SelectInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   }
 #endif
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   for (size_t i = 0; i < outputs_size; i++) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/shape_infer.c

@@ -31,7 +31,7 @@ int ShapeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
 
   out_tensor->data_type_ = kNumberTypeInt32;
   out_tensor->format_ = in_tensor->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   out_tensor->shape_size_ = 1;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/size_infer.c

@@ -30,7 +30,7 @@ int SizeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   TensorC *out_tensor = outputs[0];
   out_tensor->data_type_ = kNumberTypeInt32;
   out_tensor->format_ = in_tensor->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/slice_infer.c

@@ -26,7 +26,7 @@ int SliceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/softmax_infer.c

@@ -31,7 +31,7 @@ int SoftMaxInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
 
   output->data_type_ = input->data_type_;
   output->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ > 5) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/space_to_batch_infer.c

@@ -32,7 +32,7 @@ int SpaceToBatchInferShape(const TensorC *const *inputs, size_t inputs_size, Ten
   }
   SetDataTypeFormat(outputs[0], input);
   SpaceToBatchParameter *param = (SpaceToBatchParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ != 4) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/space_to_batch_nd_infer.c

@@ -110,7 +110,7 @@ int SpaceToBatchNdInferShape(const TensorC *const *inputs, size_t inputs_size, T
   }
   outputs[0]->data_type_ = input->data_type_;
   outputs[0]->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/space_to_depth_infer.c

@@ -33,7 +33,7 @@ int SpaceToDepthInferShape(const TensorC *const *inputs, size_t inputs_size, Ten
   }
   SetDataTypeFormat(outputs[0], input);
   SpaceToDepthParameter *param = (SpaceToDepthParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input->shape_size_ != 4) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/sparse_to_dense_infer.c

@@ -30,7 +30,7 @@ int SparseToDenseInferShape(const TensorC *const *inputs, size_t inputs_size, Te
   const TensorC *input1 = inputs[1];
   const TensorC *input2 = inputs[2];
   SetDataTypeFormat(output, input2);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int *input1_data = (int *)(input1->data_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/splice_infer.c

@@ -29,7 +29,7 @@ int SpliceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   const TensorC *input = inputs[0];
   TensorC *output = outputs[0];
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/split_infer.c

@@ -41,7 +41,7 @@ int SplitInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
 
   size_t num_split_ = param->num_split_ == 0 ? (int)(outputs_size) : param->num_split_;
   param->num_split_ = num_split_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/split_with_over_lap_infer.c

@@ -25,7 +25,7 @@ int SplitWithOverlapInferShape(const TensorC *const *inputs, size_t inputs_size,
     return check_ret;
   }
 #endif
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   const TensorC *input = inputs[0];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/squeeze_infer.c

@@ -29,7 +29,7 @@ int SqueezeInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
   const TensorC *input = inputs[0];
   SqueezeParameter *param = (SqueezeParameter *)parameter;
   SetDataTypeFormat(outputs[0], input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int out_shape[MAX_SHAPE_SIZE] = {0};

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/stack_infer.c

@@ -28,7 +28,7 @@ int StackInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
   const TensorC *input = inputs[0];
   SetDataTypeFormat(outputs[0], input);
   StackParameter *param = (StackParameter *)parameter;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int32_t output_shape[MAX_SHAPE_SIZE] = {0};

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/strided_slice_grad_infer.c

@@ -37,7 +37,7 @@ int StridedSliceGradInferShape(const TensorC *const *inputs, size_t inputs_size,
 
   const TensorC *input = inputs[0];
   SetDataTypeFormat(outputs[0], input);
-  bool inferflag = parameter->infer_flag_;
+  bool inferflag = InferFlag(inputs, inputs_size);
 
   int in_shape_[MAX_SHAPE_SIZE] = {0};
   size_t in_shape_size = 0;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/strided_slice_infer.c

@@ -313,7 +313,7 @@ int StridedSliceInferShape(const TensorC *const *inputs, size_t inputs_size, Ten
   const TensorC *input = inputs[0];
   SetDataTypeFormat(outputs[0], inputs[0]);
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/switch_infer.c

@@ -57,9 +57,12 @@ int SwitchInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
       memcpy(mirror_tensor, inputs[i + 1], sizeof(TensorC));
       outputs[i + outputs_size / 2] = mirror_tensor;
     }
+  }
+  bool infer_flag = InferFlag(inputs, inputs_size);
+  for (size_t i = 0; i < outputs_size / 2; i++) {
     *((const TensorC **)inputs + i + 1) = NULL;
   }
-  if (!parameter->infer_flag_) {
+  if (!infer_flag) {
     return NNACL_INFER_INVALID;
   }
   return NNACL_OK;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tensorlist_fromtensor_infer.c

@@ -32,7 +32,7 @@ int TensorListFromTensorInferShape(const TensorC *const *inputs, size_t inputs_s
   output->format_ = Format_NHWC;
   output->tensors_data_type_ = input0->data_type_;
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tensorlist_getitem_infer.c

@@ -28,14 +28,14 @@ int TensorListGetItemInferShape(const TensorC *const *inputs, size_t inputs_size
 
   TensorListC *input0 = (TensorListC *)(inputs[0]);
   const TensorC *get_index = inputs[1];
+  if (get_index->data_ == NULL) {
+    return NNACL_INFER_INVALID;
+  }
   if (GetElementNum(get_index) != 1) {
     return NNACL_ERR;
   }
   TensorC *output = outputs[0];
-  if (!parameter->infer_flag_ || input0->element_num_ == 0) {
-    return NNACL_INFER_INVALID;
-  }
-  if (get_index->data_ == NULL) {
+  if (!InferFlag(inputs, inputs_size) || input0->element_num_ == 0) {
     return NNACL_INFER_INVALID;
   }
   int index = ((int *)(get_index->data_))[0];
@@ -51,7 +51,7 @@ int TensorListGetItemInferShape(const TensorC *const *inputs, size_t inputs_size
   }
   output->format_ = input0->tensors_[index].format_;
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tensorlist_reserve_infer.c

@@ -49,12 +49,12 @@ int TensorListReserveInferShape(const TensorC *const *inputs, size_t inputs_size
   if (num_ele_type != kNumberTypeInt && ele_shape_type != kNumberTypeInt32) {
     return NNACL_ERR;
   }
-  if (GetElementNum(input1) != 1) {
-    return NNACL_ERR;
-  }
   if (input1->data_ == NULL) {
     return NNACL_INFER_INVALID;
   }
+  if (GetElementNum(input1) != 1) {
+    return NNACL_ERR;
+  }
   int num_elements = ((int *)(input1->data_))[0];
   ShapeSet(output->element_shape_, &(output->element_shape_size_), ele_shape_ptr, GetElementNum(input0));
   output->element_num_ = num_elements;

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tensorlist_setitem_infer.c

@@ -51,7 +51,7 @@ int TensorListSetItemInferShape(const TensorC *const *inputs, size_t inputs_size
   output0->format_ = input0->format_;
   output0->tensors_data_type_ = value_tensor->data_type_;
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tensorlist_stack_infer.c

@@ -29,7 +29,7 @@ int TensorListStackInferShape(const TensorC *const *inputs, size_t inputs_size,
   TensorListC *input0 = (TensorListC *)(inputs[0]);
   output->data_type_ = input0->tensors_data_type_;
   output->format_ = input0->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   if (input0->element_num_ == 0) {

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tile_infer.c

@@ -46,7 +46,7 @@ int TileInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/topk_infer.c

@@ -35,7 +35,7 @@ int TopKInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   SetDataTypeFormat(output0, input);
   output1->data_type_ = kNumberTypeInt32;
   output1->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   const TensorC *input_k_tensor = inputs[1];

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/transpose_infer.c

@@ -42,7 +42,7 @@ int TransposeInferShape(const TensorC *const *inputs, size_t inputs_size, Tensor
   if (parameter->quant_type_ == QuantType_QUANT_WEIGHT) {
     output->data_type_ = kNumberTypeFloat32;
   }
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/uniform_real_infer.c

@@ -21,7 +21,7 @@ int UniformRealInferShape(const TensorC *const *inputs, size_t inputs_size, Tens
                           OpParameter *parameter) {
   outputs[0]->data_type_ = kNumberTypeFloat32;
   outputs[0]->format_ = inputs[0]->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int32_t *input_data = (int32_t *)(inputs[0]->data_);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/unique_infer.c

@@ -33,7 +33,7 @@ int UniqueInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
   SetDataTypeFormat(output0, input);
   output1->data_type_ = kNumberTypeInt32;
   output1->format_ = input->format_;
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   SetShapeTensor(output0, input);

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/unsqueeze_infer.c

@@ -30,7 +30,7 @@ int UnsqueezeInferShape(const TensorC *const *inputs, size_t inputs_size, Tensor
   TensorC *output = outputs[0];
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/unstack_infer.c

@@ -36,7 +36,7 @@ int UnstackInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
     SetDataTypeFormat(outputs[i], input);
   }
 
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
   int output_shape[MAX_SHAPE_SIZE] = {0};

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/where_infer.c

@@ -41,7 +41,7 @@ int WhereInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
   }
 
   SetDataTypeFormat(output, input);
-  if (!parameter->infer_flag_) {
+  if (!InferFlag(inputs, inputs_size)) {
     return NNACL_INFER_INVALID;
   }
 

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/op_base.h

@@ -81,7 +81,6 @@ typedef enum DataOrder {
 
 typedef struct OpParameter {
   char name_[100];
-  bool infer_flag_;
   int type_;
   int thread_num_;
   int quant_type_;

mindspore/lite/micro/coder/opcoders/serializers/nnacl_serializer/nnacl_stream_utils.cc

@@ -33,8 +33,7 @@ std::ostream &operator<<(std::ostream &code, const ::QuantArg &quant_arg) {
 
 std::ostream &operator<<(std::ostream &code, const OpParameter &parameter) {
   code << "{ \"\""
-       << ", " << std::boolalpha << parameter.infer_flag_ << ", " << parameter.type_ << ", " << gThreadNum << ", "
-       << parameter.quant_type_ << "}";
+       << ", " << parameter.type_ << ", " << gThreadNum << ", " << parameter.quant_type_ << "}";
   return code;
 }
 

mindspore/lite/micro/coder/session.cc

@@ -209,12 +209,10 @@ OpParameter *CoderSession::GenParameterAndInfer(const Model::Node *node, const s
   MS_CHECK_PTR_RET_NULL(parame_gen);
   auto parameter = parame_gen(primitive);
   MS_CHECK_PTR_RET_NULL(parameter);
-  parameter->infer_flag_ = true;
   auto ret = KernelInferShape(inputs, outputs, parameter);
   if (ret == RET_INFER_INVALID) {
     MS_LOG(INFO) << "InferShape shouldn't be done before runtime, name: " << node->name_
                  << ", type: " << PrimitiveTypeName(GetPrimitiveType(primitive)) << "flag set to false.";
-    parameter->infer_flag_ = false;
   } else if (ret != RET_OK) {
     MS_LOG(ERROR) << "InferShape failed, name: " << node->name_
                   << ", type: " << PrimitiveTypeName(GetPrimitiveType(primitive));

mindspore/lite/src/common/tensor_util.cc

@@ -108,6 +108,7 @@ int TensorList2TensorListC(TensorList *src, TensorListC *dst) {
   dst->is_ready_ = src->IsReady();
   dst->data_type_ = static_cast<TypeIdC>(src->data_type());
   dst->format_ = src->format();
+  dst->shape_value_ = src->shape().empty() ? 0 : src->shape().front();
   dst->element_num_ = src->shape().empty() ? 0 : src->tensors().size();
 
   dst->tensors_ = reinterpret_cast<TensorC *>(malloc(dst->element_num_ * sizeof(TensorC)));

mindspore/lite/src/lite_kernel.cc

@@ -88,10 +88,8 @@ int LiteKernel::FreeInWorkTensor() const {
 
 int LiteKernel::PreProcess() {
   if (!InferShapeDone()) {
-    op_parameter_->infer_flag_ = true;
     auto ret = lite::KernelInferShape(in_tensors_, &out_tensors_, op_parameter_);
     if (ret != 0) {
-      op_parameter_->infer_flag_ = false;
       MS_LOG(ERROR) << "InferShape fail!";
       return ret;
     }

mindspore/lite/src/lite_kernel.h

@@ -188,14 +188,15 @@ class LiteKernel {
   int DecOutTensorRefCount();
 #endif
 
- protected:
-  bool InferShapeDone() {
-    if (op_parameter_ != nullptr) {
-      return op_parameter_->infer_flag_;
+  bool InferShapeDone() const {
+    auto shape = out_tensors_.front()->shape();
+    if (std::find(shape.begin(), shape.end(), -1) != shape.end()) {
+      return false;
     }
-    return false;
+    return true;
   }
 
+ protected:
   KernelKey desc_{};
   std::string name_;
   OpParameter *op_parameter_ = nullptr;

mindspore/lite/src/lite_session.cc

@@ -690,7 +690,6 @@ void LiteSession::ResetInputsShape(const std::vector<std::vector<int>> &dims) {
 }
 
 int LiteSession::ReSizeKernels(const std::vector<kernel::LiteKernel *> &kernels) {
-  bool infer_shape_interrupt = false;
   for (auto kernel : kernels) {
     if (kernel == nullptr) {
       MS_LOG(ERROR) << "input kernel is nullptr!";
@@ -708,11 +707,10 @@ int LiteSession::ReSizeKernels(const std::vector<kernel::LiteKernel *> &kernels)
 #endif
     } else {
       auto sub_graph = reinterpret_cast<kernel::SubGraphKernel *>(kernel);
-      ret = sub_graph->ReSize(infer_shape_interrupt);
+      ret = sub_graph->ReSize();
     }
     if (ret == RET_INFER_INVALID) {
       MS_LOG(INFO) << "InferShape is interrupted";
-      infer_shape_interrupt = true;
       continue;
     }
     if (ret != RET_OK) {

mindspore/lite/src/runtime/infer_manager.cc

@@ -66,6 +66,9 @@ int KernelInferShape(const std::vector<lite::Tensor *> &inputs, std::vector<lite
     } else {
       TensorC2Tensor(out_tensors.at(i), outputs->at(i));
     }
+    if (ret == NNACL_INFER_INVALID) {
+      outputs->at(i)->set_shape({-1});
+    }
   }
 
   FreeAllTensorC(&in_tensors);

mindspore/lite/src/runtime/kernel/arm/base/group_convolution_base.cc

@@ -75,11 +75,8 @@ void GroupConvolutionBaseCPUKernel::FreeSubKernel() {
 
 int GroupConvolutionBaseCPUKernel::PreProcess() {
   if (!InferShapeDone()) {
-    op_parameter_->infer_flag_ = true;
-
     auto ret = lite::KernelInferShape(in_tensors_, &out_tensors_, op_parameter_);
     if (ret != 0) {
-      op_parameter_->infer_flag_ = false;
       MS_LOG(ERROR) << "InferShape fail!";
       return ret;
     }

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

@@ -37,9 +37,10 @@ class GroupConvCreator {
                    const lite::InnerContext *ctx, bool is_quant, TypeId data_type)
       : origin_inputs_(std::move(inputs)),
         origin_outputs_(std::move(outputs)),
-        infered_(op_parameter->infer_flag_),
         is_quant_(is_quant),
         data_type_(data_type) {
+    auto shape = origin_outputs_.front()->shape();
+    infered_ = std::find(shape.begin(), shape.end(), -1) == shape.end();
     conv_param_ = reinterpret_cast<ConvParameter *>(op_parameter);
   }
 

mindspore/lite/src/runtime/kernel/arm/base/resize_base.cc

@@ -108,7 +108,7 @@ int ResizeBaseCPUKernel::Init() {
 
   auto input = in_tensors_.at(0);
   auto input_shape = input->shape();
-  if (!input_shape.empty() && input_shape.size() != COMM_SHAPE_SIZE) {
+  if (InferShapeDone() && input_shape.size() != COMM_SHAPE_SIZE) {
     MS_LOG(ERROR) << "Resize op support input rank 4, got " << input_shape.size();
     return RET_ERROR;
   }