[MSLITE][Develop] return RET_INFET_INVALID when infer_flag is false for op infershape

mindspore/lite/src/ops/addn.cc

@@ -86,7 +86,7 @@ int AddN::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outputs
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   output->set_shape(input->shape());
 

mindspore/lite/src/ops/argmax.cc

@@ -74,7 +74,7 @@ int ArgMax::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outp
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   std::vector<int> output_shape(input->shape());
   auto input_shape_size = input->shape().size();

mindspore/lite/src/ops/argmin.cc

@@ -72,7 +72,7 @@ int ArgMin::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::Te
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape_size = input->shape().size();
   auto axis = GetAxis() < 0 ? GetAxis() + input_shape_size : GetAxis();

mindspore/lite/src/ops/arithmetic.cc

@@ -45,7 +45,7 @@ int Arithmetic::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite
   output->set_format(format);
   output->set_data_type(input0->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   if (input_shape0.size() > 10 || input_shape1.size() > 10) {
     int wrong_dim = input_shape0.size() > input_shape1.size() ? input_shape0.size() : input_shape1.size();

mindspore/lite/src/ops/arithmetic_self.cc

@@ -33,7 +33,7 @@ int ArithmeticSelf::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   output->set_shape(input->shape());
   return RET_OK;

mindspore/lite/src/ops/audio_spectrogram.cc

@@ -77,7 +77,7 @@ int AudioSpectrogram::InferShape(std::vector<Tensor *> inputs_, std::vector<Tens
   output->set_data_type(input->data_type());
   output->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape = input->shape();
   if (input_shape.size() != 2) {

mindspore/lite/src/ops/batch_to_space.cc

@@ -98,7 +98,7 @@ int BatchToSpace::InferShape(std::vector<lite::Tensor *> inputs, std::vector<lit
   outputs[0]->set_format(input->format());
   outputs[0]->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape = input->shape();
   if (input_shape.size() != kDimension_4d) {

mindspore/lite/src/ops/broadcast_to.cc

@@ -80,7 +80,7 @@ int BroadcastTo::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *>
   outputs[0]->set_format(input->format());
   outputs[0]->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   std::vector<int32_t> dst_shape(GetDstShape());
   for (size_t i = 0; i < dst_shape.size(); ++i) {

mindspore/lite/src/ops/cast.cc

@@ -93,7 +93,7 @@ int Cast::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> output
 
   output->set_data_type(static_cast<TypeId>(GetDstT()));
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
 
   if (GetSrcT() != 0 && input->data_type() != GetSrcT()) {

mindspore/lite/src/ops/concat.cc

@@ -98,7 +98,7 @@ int Concat::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outp
   output->set_data_type(input0->data_type());
   output->set_format(input0->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
 
   auto input0_shape = inputs_.at(0)->shape();

mindspore/lite/src/ops/constant_of_shape.cc

@@ -83,7 +83,7 @@ int ConstantOfShape::InferShape(std::vector<Tensor *> inputs_, std::vector<Tenso
   out_tensor->set_data_type(static_cast<TypeId>(GetDataType()));
   out_tensor->set_format(in_tensor->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto in_data = reinterpret_cast<int *>(in_tensor->data_c());
   if (in_data == nullptr) {

mindspore/lite/src/ops/conv2d.cc

@@ -391,7 +391,7 @@ int Conv2D::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outp
   pad_r_ = GetPadRight();
 
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto in_shape = input_tensor->shape();
   int input_h = in_shape.at(1);

mindspore/lite/src/ops/crop.cc

@@ -71,7 +71,7 @@ int Crop::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outputs
   outputs[0]->set_format(inputs[0]->format());
   outputs[0]->set_data_type(inputs[0]->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   outputs[0]->set_shape(inputs[1]->shape());
   return RET_OK;

mindspore/lite/src/ops/deconv2d.cc

@@ -317,7 +317,7 @@ int DeConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vector<lite::
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   int32_t input_h = input->Height();
   int32_t input_w = input->Width();

mindspore/lite/src/ops/dedepthwise_conv2d.cc

@@ -138,7 +138,7 @@ int DeDepthwiseConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vect
   output->set_format(input->format());
   output->set_data_type(input->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto in_shape = input->shape();
   int input_h = in_shape.at(1);

mindspore/lite/src/ops/depth_to_space.cc

@@ -73,7 +73,7 @@ int DepthToSpace::InferShape(std::vector<lite::Tensor *> inputs, std::vector<lit
   outputs[0]->set_data_type(input->data_type());
   outputs[0]->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape = input->shape();
   if (input_shape.size() != kDimension_4d) {

mindspore/lite/src/ops/depthwise_conv2d.cc

@@ -219,7 +219,7 @@ int DepthwiseConv2D::InferShape(std::vector<lite::Tensor *> inputs_, std::vector
   pad_r_ = GetPadRight();
 
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto in_shape = input->shape();
   int input_h = in_shape.at(1);

mindspore/lite/src/ops/detection_post_process.cc

@@ -190,7 +190,7 @@ int DetectionPostProcess::InferShape(std::vector<lite::Tensor *> inputs_, std::v
   num_det->set_format(boxes->format());
   num_det->set_data_type(kNumberTypeFloat32);
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   const auto max_detections = GetMaxDetections();
   const auto max_classes_per_detection = GetMaxClassesPerDetection();

mindspore/lite/src/ops/dropout.cc

@@ -84,7 +84,7 @@ int Dropout::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> out
   auto output0 = outputs_.front();
   MS_ASSERT(output0 != nullptr);
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   output0->set_shape(input->shape());
   output0->set_data_type(input->data_type());

mindspore/lite/src/ops/dropout_grad.cc

@@ -87,7 +87,7 @@ int DropoutGrad::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *>
   auto output = outputs_.front();
   MS_ASSERT(output != nullptr);
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   output->set_shape(input->shape());
   output->set_data_type(input->data_type());

mindspore/lite/src/ops/embedding_lookup.cc

@@ -70,7 +70,7 @@ int EmbeddingLookup::InferShape(std::vector<Tensor *> inputs_, std::vector<Tenso
   output->set_format(params_->format());
   output->set_data_type(params_->data_type());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
 
   auto embedding_shape = params_->shape();

mindspore/lite/src/ops/expand_dims.cc

@@ -103,7 +103,7 @@ int ExpandDims::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *>
   output->set_data_type(input->data_type());
   output->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   int dim = GetDim();
   if (dim < 0) {

mindspore/lite/src/ops/fft_imag.cc

@@ -43,7 +43,7 @@ int FftImag::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> out
   output->set_data_type(TypeId::kNumberTypeFloat32);
   output->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape = input->shape();
   input_shape.pop_back();

mindspore/lite/src/ops/fft_real.cc

@@ -43,7 +43,7 @@ int FftReal::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> out
   output->set_data_type(TypeId::kNumberTypeFloat32);
   output->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
   auto input_shape = input->shape();
   input_shape.pop_back();

mindspore/lite/src/ops/fill.cc

@@ -71,7 +71,7 @@ int Fill::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> output
   output->set_data_type(input->data_type());
   output->set_format(input->format());
   if (!infer_flag()) {
-    return RET_OK;
+    return RET_INFER_INVALID;
   }
 
   std::vector<int> output_shape;