lint

Signed-off-by: Ti-Tai Wang <titaiwang@microsoft.com>

Author: titaiwangms

onnx/defs/nn/defs.cc

@@ -552,7 +552,8 @@ ONNX_OPERATOR_SET_SCHEMA(
 static std::function<void(OpSchema&)> LpPoolOpSchemaGenerator(const char* name) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
  {name} consumes an input tensor X and applies Lp pooling across
  the tensor according to kernel sizes, stride sizes, and pad lengths.
  Lp pooling consisting of computing the Lp norm on all values of a subset
@@ -576,7 +577,7 @@ static std::function<void(OpSchema&)> LpPoolOpSchemaGenerator(const char* name)
  ```
  pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
  ```)DOC";
-                        ReplaceAll(doc, "{name}", name););
+        ReplaceAll(doc, "{name}", name););
     schema.SetDoc(doc);
     schema.Attr("kernel_shape", "The size of the kernel along each axis.", AttributeProto::INTS);
     schema.Attr(
@@ -678,11 +679,12 @@ static void roiPoolTypeShapeInference(InferenceContext& ctx) {
 static std::function<void(OpSchema&)> RoiPoolOpSchemaGenerator(const char* name) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
  ROI {name} pool consumes an input tensor X and region of interests (RoIs) to
  apply {name} pooling across each RoI, to produce output 4-D tensor of shape
  (num_rois, channels, pooled_shape[0], pooled_shape[1]).)DOC";
-                        ReplaceAll(doc, "{name}", name););
+        ReplaceAll(doc, "{name}", name););
     schema.SetDoc(doc);
     schema.Attr("pooled_shape", "ROI pool output shape (height, width).", AttributeProto::INTS);
     schema.Attr(
@@ -733,10 +735,11 @@ ONNX_OPERATOR_SET_SCHEMA(MaxRoiPool, 22, OpSchema().FillUsing(RoiPoolOpSchemaGen
 static std::function<void(OpSchema&)> ConvOpSchemaGenerator(const char* filter_desc) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
 The convolution operator consumes an input tensor and {filter_desc}, and
 computes the output.)DOC";
-                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+        ReplaceAll(doc, "{filter_desc}", filter_desc););
     schema.SetDoc(doc);
     schema.Input(
         0,
@@ -1248,7 +1251,8 @@ ONNX_API void convTransposeShapeInference(InferenceContext& ctx) {
 static std::function<void(OpSchema&)> ConvTransposeOpSchemaGenerator(const char* filter_desc) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
 The convolution transpose operator consumes an input tensor and {filter_desc},
 and computes the output.
 
@@ -1263,7 +1267,7 @@ output_shape can also be explicitly specified in which case pads values are auto
   Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
 
     )DOC";
-                        ReplaceAll(doc, "{filter_desc}", filter_desc););
+        ReplaceAll(doc, "{filter_desc}", filter_desc););
     schema.SetDoc(doc);
     schema.Input(
         0,
@@ -1491,12 +1495,13 @@ ONNX_API void globalPoolTypeShapeInference(InferenceContext& ctx) {
 static std::function<void(OpSchema&)> GlobalPoolingOpSchemaGenerator(const char* op_type, const char* op) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
  Global{op_type} consumes an input tensor X and applies {op} pooling across
  the values in the same channel. This is equivalent to {op_type} with kernel size
  equal to the spatial dimension of input tensor.)DOC";
-                        ReplaceAll(doc, "{op_type}", op_type);
-                        ReplaceAll(doc, "{op}", op););
+        ReplaceAll(doc, "{op_type}", op_type);
+        ReplaceAll(doc, "{op}", op););
     schema.SetDoc(doc);
     schema.Input(
         0,
@@ -1538,12 +1543,13 @@ ONNX_OPERATOR_SET_SCHEMA(GlobalMaxPool, 22, OpSchema().FillUsing(GlobalPoolingOp
 static std::function<void(OpSchema&)> GlobalLpPoolingOpSchemaGenerator(const char* op_type, const char* op) {
   return [=](OpSchema& schema) {
     std::string doc;
-    POPULATE_OP_DOC_STR(doc = R"DOC(
+    POPULATE_OP_DOC_STR(
+        doc = R"DOC(
  Global{op_type} consumes an input tensor X and applies {op} pooling across
  the values in the same channel. This is equivalent to {op_type} with kernel size
  equal to the spatial dimension of input tensor.)DOC";
-                        ReplaceAll(doc, "{op_type}", op_type);
-                        ReplaceAll(doc, "{op}", op););
+        ReplaceAll(doc, "{op_type}", op_type);
+        ReplaceAll(doc, "{op}", op););
     schema.SetDoc(doc);
     schema.Attr(
         "p", "p value of the Lp norm used to pool over the input data.", AttributeProto::INT, static_cast<int64_t>(2));