Changed some variable names from camel case to underscore for consistency.

PiperOrigin-RevId: 201587899

1 files changed, 21 insertions, 20 deletions

diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index 14b399ef96..93267f9a4f 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -179,9 +179,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   TF_LITE_ENSURE_STATUS(AllocateTemporaryTensorsIfRequired(context, node));
 
-  bool hasBias = node->inputs->size == 3;
+  bool has_bias = node->inputs->size == 3;
   // Check number of inputs/outputs
-  TF_LITE_ENSURE(context, hasBias || node->inputs->size == 2);
+  TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
   TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
   TfLiteTensor* output = &context->tensors[node->outputs->data[0]];
   TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
@@ -204,9 +204,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // TODO(ahentz): At this point the optimized versions require 'bias'. We can
   // either change that or document that convolution requires it.
-  TF_LITE_ENSURE(context, hasBias);
+  TF_LITE_ENSURE(context, has_bias);
 
-  if (hasBias) {
+  if (has_bias) {
     bias = &context->tensors[node->inputs->data[2]];
     if (data_type == kTfLiteUInt8) {
       TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteInt32);
@@ -226,29 +226,30 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   // Matching GetWindowedOutputSize in TensorFlow.
   auto padding = params->padding;
-  auto computeOutSize = [padding](int imageSize, int filterSize, int stride,
-                                  int dilationRate) -> int {
-    int effectiveFilterSize = (filterSize - 1) * dilationRate + 1;
+  auto compute_out_size = [padding](int image_size, int filter_size, int stride,
+                                    int dilation_rate) -> int {
+    int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
     return padding == kTfLitePaddingSame
-               ? (imageSize + stride - 1) / stride
+               ? (image_size + stride - 1) / stride
                : padding == kTfLitePaddingValid
-                     ? (imageSize - effectiveFilterSize + stride) / stride
+                     ? (image_size - effective_filter_size + stride) / stride
                      : 0;
   };
 
-  int outWidth = computeOutSize(width, filter_width, params->stride_width,
-                                params->dilation_width_factor);
-  int outHeight = computeOutSize(height, filter_height, params->stride_height,
-                                 params->dilation_height_factor);
+  int out_width = compute_out_size(width, filter_width, params->stride_width,
+                                   params->dilation_width_factor);
+  int out_height =
+      compute_out_size(height, filter_height, params->stride_height,
+                       params->dilation_height_factor);
 
   data->padding.height =
       ComputePadding(params->stride_height, params->dilation_height_factor,
-                     height, filter_height, outHeight);
+                     height, filter_height, out_height);
   data->padding.width =
       ComputePadding(params->stride_width, params->dilation_width_factor, width,
-                     filter_width, outWidth);
+                     filter_width, out_width);
 
-  TF_LITE_ENSURE(context, hasBias);
+  TF_LITE_ENSURE(context, has_bias);
 
   // Note that quantized inference requires that all tensors have their
   // parameters set. This is usually done during quantized training.
@@ -267,8 +268,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
   output_size->data[0] = batches;
-  output_size->data[1] = outHeight;
-  output_size->data[2] = outWidth;
+  output_size->data[1] = out_height;
+  output_size->data[2] = out_width;
   output_size->data[3] = channels_out;
   auto output_status = context->ResizeTensor(context, output, output_size);
 
@@ -458,9 +459,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* output = &context->tensors[node->outputs->data[0]];
   TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
   TfLiteTensor* filter = &context->tensors[node->inputs->data[1]];
-  bool hasBias = node->inputs->size == 3;
+  bool has_bias = node->inputs->size == 3;
   TfLiteTensor* bias =
-      hasBias ? &context->tensors[node->inputs->data[2]] : nullptr;
+      has_bias ? &context->tensors[node->inputs->data[2]] : nullptr;
   TfLiteTensor* im2col =
       data->need_im2col
           ? &context->tensors[node->temporaries->data[data->im2col_index]]