Remove unused lambda captures

This fixes the lambda capture <> is not used compiler warning. PiperOrigin-RevId: 222318600
akikaaa · Nov 20, 2018 · e16716d · e16716d
1 parent 095b54b
commit e16716d
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Showing 7 changed files with 16 additions and 19 deletions.
diff --git a/tensorflow/contrib/image/kernels/adjust_hsv_in_yiq_op.cc b/tensorflow/contrib/image/kernels/adjust_hsv_in_yiq_op.cc
@@ -115,7 +115,7 @@ class AdjustHsvInYiqOp<CPUDevice> : public AdjustHsvInYiqOpBase {
         *context->device()->tensorflow_cpu_worker_threads();
     Shard(worker_threads.num_threads, worker_threads.workers, channel_count,
           kCostPerChannel,
-          [channel_count, &input_data, &output_data, &tranformation_matrix](
+          [&input_data, &output_data, &tranformation_matrix](
               int64 start_channel, int64 end_channel) {
             // Applying projection matrix to input RGB vectors.
             const float* p = input_data.data() + start_channel * kChannelSize;

diff --git a/tensorflow/core/kernels/adjust_hue_op.cc b/tensorflow/core/kernels/adjust_hue_op.cc
@@ -216,8 +216,8 @@ class AdjustHueOp<CPUDevice> : public AdjustHueOpBase {
         *context->device()->tensorflow_cpu_worker_threads();
     Shard(worker_threads.num_threads, worker_threads.workers, channel_count,
           kCostPerChannel,
-          [channel_count, &input_data, &output_data, delta_h](
-              int64 start_channel, int64 end_channel) {
+          [&input_data, &output_data, delta_h](int64 start_channel,
+                                               int64 end_channel) {
             const float* p = input_data.data() + start_channel * kChannelSize;
             float* q = output_data.data() + start_channel * kChannelSize;
             for (int i = start_channel; i < end_channel; i++) {

diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc
@@ -180,7 +180,7 @@ class Barrier : public ResourceBase {
         // SQSS is closed, nothing is left in the incomplete set,
         // the queue is not already marked as closed, and (most
         // importantly), the queue has entries in it.
-        [this, ctx, callback, component_index]() {
+        [this, ctx, callback]() {
           if (!ctx->status().ok()) {
             callback();
             return;

diff --git a/tensorflow/core/kernels/deep_conv2d.cc b/tensorflow/core/kernels/deep_conv2d.cc
@@ -434,10 +434,9 @@ struct TransformFilters {
         tile_spatial_size, base_filter_spatial_size, transform_matrix);
 
     auto shard = [&ctx, &args, &transform, &base_filter_rows, &base_filter_cols,
-                  &num_filters_transform, &in_depth, &out_depth,
-                  &filter_shards_row, &filter_shards_col, &tile_spatial_size,
-                  &filter_in, &transform_matrix,
-                  &filter_out](int64 start, int64 limit) {
+                  &num_filters_transform, &in_depth, &filter_shards_row,
+                  &filter_shards_col, &tile_spatial_size, &filter_in,
+                  &transform_matrix, &filter_out](int64 start, int64 limit) {
       // Allocate buffer for pre-processed filter:
       //   [base_filter_rows, base_filter_cols, num_filters_transform, in_depth]
       //
@@ -533,9 +532,9 @@ struct PackFilters {
     const int64 out_depth = args.out_depth;
     const int64 num_filters = filter_shards_row * filter_shards_col * out_depth;
 
-    auto shard = [&ctx, &packed_filters, &filter_transform_data,
-                  &tile_spatial_size, &in_depth, &out_depth, &filter_shards_row,
-                  &filter_shards_col, &num_filters](int64 start, int64 limit) {
+    auto shard = [&ctx, &packed_filters, &filter_transform_data, &in_depth,
+                  &out_depth, &filter_shards_row, &filter_shards_col,
+                  &num_filters](int64 start, int64 limit) {
       const int64 filter_coord_stride = num_filters * in_depth;
       for (int64 i = start; i < limit; ++i) {
         // Allocate filter buffer [out_depth, shard_rows, shard_cols, in_depth].
@@ -1004,9 +1003,9 @@ struct DeepConv2D<CPUDevice, T> {
         out_tile_spatial_size, tile_spatial_size, output_transform_matrix);
 
     auto shard = [&ctx, &args, &transform, &packed_filters, &in_depth,
-                  out_depth, tile_rows, tile_cols, out_tile_rows, out_tile_cols,
-                  filter_shards_row, filter_shards_col, tile_spatial_size,
-                  &input, &tile_transform_matrix, &output_transform_matrix,
+                  out_depth, out_tile_rows, out_tile_cols, filter_shards_row,
+                  filter_shards_col, tile_spatial_size, &input,
+                  &tile_transform_matrix, &output_transform_matrix,
                   &output](int64 batch_start, int64 batch_limit) {
       const int64 row_tiles =
           (args.out_rows + out_tile_rows - 1) / out_tile_rows +

diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc
@@ -352,9 +352,9 @@ class TensorArrayGradOp : public TensorArrayCreationOp {
     }
 
     const auto key = strings::StrCat(output_handle(0), output_handle(1));
-    auto creator = [this, key, tensor_array, array_size, marked_size,
-                    element_shape, shape_to_prepend, tensor_array_output_handle,
-                    output_handle](TensorArray** ret) -> Status {
+    auto creator = [key, tensor_array, array_size, marked_size, element_shape,
+                    shape_to_prepend,
+                    tensor_array_output_handle](TensorArray** ret) -> Status {
       *ret = new TensorArray(
           key, tensor_array->ElemType(), *tensor_array_output_handle,
           array_size, element_shape, tensor_array->HasIdenticalElementShapes(),

diff --git a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/lite/kernels/internal/optimized/optimized_ops.h
@@ -4293,7 +4293,6 @@ inline void LogSoftmax(const SoftmaxParams& params,
   using FixedPointScaledDiff =
       gemmlowp::FixedPoint<int32, kScaledDiffIntegerBits>;
   using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumulationIntegerBits>;
-  using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>;
 
   const int trailing_dim = input_shape.DimensionsCount() - 1;
   const int outer_size =

diff --git a/tensorflow/lite/kernels/internal/reference/reference_ops.h b/tensorflow/lite/kernels/internal/reference/reference_ops.h
@@ -2736,7 +2736,6 @@ inline void LogSoftmax(const SoftmaxParams& params,
   using FixedPointScaledDiff =
       gemmlowp::FixedPoint<int32, kScaledDiffIntegerBits>;
   using FixedPointAccum = gemmlowp::FixedPoint<int32, kAccumulationIntegerBits>;
-  using FixedPoint0 = gemmlowp::FixedPoint<int32, 0>;
 
   const int trailing_dim = input_shape.DimensionsCount() - 1;
   const int outer_size =