format

Author: wejoncy

onnxruntime/core/providers/coreml/builders/impl/softmax_op_builder.cc

@@ -35,7 +35,7 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   NodeAttrHelper helper(node);
   int32_t axis_default_value = (node.SinceVersion() < 13) ? 1 : -1;
   const auto axis = helper.Get("axis", axis_default_value);
-  auto axis_nonnegative  = HandleNegativeAxis(axis, data_shape.size());
+  auto axis_nonnegative = HandleNegativeAxis(axis, data_shape.size());
 
 #if defined(COREML_ENABLE_MLPROGRAM)
   // CoreML's softmax match onnx's softmax behavior since opset 13.
@@ -47,15 +47,15 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
     const int32_t elem_type = static_cast<int32_t>(input_dtype);
 
     std::string_view layer_input_name_x = node.InputDefs()[0]->Name();
-    const bool need_reshape = node.SinceVersion() < 13 && axis_nonnegative  != static_cast<int64_t>(data_shape.size()) - 1;
+    const bool need_reshape = node.SinceVersion() < 13 && axis_nonnegative != static_cast<int64_t>(data_shape.size()) - 1;
     std::vector<int64_t> target_shape;
     if (need_reshape) {
       // reshape to 2D to simulate onnx softmax behavior
       auto reshape1 = model_builder.CreateOperation(node, "reshape", "pre");
       TensorShape input_shape(data_shape);
-      target_shape.push_back(input_shape.SizeToDimension(axis_nonnegative ));
-      target_shape.push_back(input_shape.SizeFromDimension(axis_nonnegative ));
-      axis_nonnegative  = 1;
+      target_shape.push_back(input_shape.SizeToDimension(axis_nonnegative));
+      target_shape.push_back(input_shape.SizeFromDimension(axis_nonnegative));
+      axis_nonnegative = 1;
       AddOperationInput(*reshape1, "x", layer_input_name_x);
       AddOperationInput(*reshape1, "shape", model_builder.AddConstant(reshape1->type(), "shape1", target_shape));
       layer_input_name_x = model_builder.GetUniqueName(node, "ln_reshape1_");
@@ -64,7 +64,7 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
     }
     std::unique_ptr<Operation> op = model_builder.CreateOperation(node, "softmax");
     AddOperationInput(*op, "x", layer_input_name_x);
-    AddOperationInput(*op, "axis", model_builder.AddScalarConstant(op->type(), "axis", axis_nonnegative ));
+    AddOperationInput(*op, "axis", model_builder.AddScalarConstant(op->type(), "axis", axis_nonnegative));
     if (!need_reshape) {
       AddOperationOutput(*op, *node.OutputDefs()[0]);
       model_builder.AddOperation(std::move(op));
@@ -91,8 +91,8 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
       // note: if opsets < 13, onnx Softmax coerces the input shape to be 2D based on axis.
       // we need to manually reshape to 2D and apply SoftmaxND to axis -1 to achieve equivalent results for CoreML.
       TensorShape input_shape(data_shape);
-      const auto size_to_dimension = input_shape.SizeToDimension(axis_nonnegative );
-      const auto size_from_dimension = input_shape.SizeFromDimension(axis_nonnegative );
+      const auto size_to_dimension = input_shape.SizeToDimension(axis_nonnegative);
+      const auto size_from_dimension = input_shape.SizeFromDimension(axis_nonnegative);
 
       TensorShapeVector target_shape;
       target_shape.push_back(size_to_dimension);

onnxruntime/core/providers/cpu/tensor/unsqueeze.h

@@ -20,7 +20,7 @@ class UnsqueezeBase {
   };
 
   Status PrepareCompute(OpKernelContext* context, Prepare& p) const;
-static TensorShapeVector ComputeOutputShape(
+  static TensorShapeVector ComputeOutputShape(
       const TensorShape& input_shape,
       const TensorShapeVector& axes) {
     TensorShapeVector output_shape;