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-rw-r--r--tensorflow/contrib/image/kernels/bipartite_match_op.cc2
-rw-r--r--tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc8
-rw-r--r--tensorflow/core/kernels/colorspace_op.cc2
-rw-r--r--tensorflow/core/kernels/non_max_suppression_op.cc5
-rw-r--r--tensorflow/core/kernels/quantized_resize_bilinear_op.cc4
-rw-r--r--tensorflow/core/kernels/random_crop_op.cc4
-rw-r--r--tensorflow/core/kernels/resize_area_op.cc5
-rw-r--r--tensorflow/core/kernels/resize_bicubic_op.cc10
-rw-r--r--tensorflow/core/kernels/resize_bilinear_op.cc10
-rw-r--r--tensorflow/core/kernels/resize_nearest_neighbor_op.cc8
-rw-r--r--tensorflow/core/kernels/sample_distorted_bounding_box_op.cc6
-rw-r--r--tensorflow/core/kernels/substr_op.cc20
12 files changed, 39 insertions, 45 deletions
diff --git a/tensorflow/contrib/image/kernels/bipartite_match_op.cc b/tensorflow/contrib/image/kernels/bipartite_match_op.cc
index 7d207c388b..726adb0777 100644
--- a/tensorflow/contrib/image/kernels/bipartite_match_op.cc
+++ b/tensorflow/contrib/image/kernels/bipartite_match_op.cc
@@ -85,7 +85,7 @@ class BipartiteMatchOp : public OpKernel {
context->allocate_output(1, TensorShape({num_input_columns}),
&column_to_row_match_indices));
- typename TTypes<float, 2>::ConstTensor distance_mat =
+ TTypes<float, 2>::ConstTensor distance_mat =
input_distance_mat.shaped<float, 2>(
{num_input_rows, num_input_columns});
diff --git a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
index 64973ccccd..dfa12e873a 100644
--- a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
+++ b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
@@ -80,12 +80,12 @@ class GatherTreeOp : public OpKernel {
max_sequence_lengths.shape().DebugString()));
Tensor* beams;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, step_ids_shape, &beams));
- typename TTypes<T, 3>::ConstTensor step_ids_t = step_ids.tensor<T, 3>();
- typename TTypes<T, 3>::ConstTensor parent_ids_t = parent_ids.tensor<T, 3>();
+ typename TTypes<T, 3>::ConstTensor step_ids_t(step_ids.tensor<T, 3>());
+ typename TTypes<T, 3>::ConstTensor parent_ids_t(parent_ids.tensor<T, 3>());
typename TTypes<int32>::ConstVec max_seq_lens_t =
max_sequence_lengths.vec<int32>();
- typename TTypes<T>::ConstScalar end_token_t = end_token.scalar<T>();
- typename TTypes<T, 3>::Tensor beams_t = beams->tensor<T, 3>();
+ typename TTypes<T>::ConstScalar end_token_t(end_token.scalar<T>());
+ typename TTypes<T, 3>::Tensor beams_t(beams->tensor<T, 3>());
const T end_token_value = end_token_t();
functor::GatherTree<Device, T>()(ctx, device, step_ids_t, parent_ids_t,
max_seq_lens_t, end_token_value, beams_t);
diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index 9cc2e67bbe..f4402a245d 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -71,7 +71,7 @@ class RGBToHSVOp : public OpKernel {
TensorShape({input_data.dimension(0)}),
&trange));
- typename TTypes<T, 1>::Tensor range = trange.tensor<T, 1>();
+ typename TTypes<T, 1>::Tensor range(trange.tensor<T, 1>());
functor::RGBToHSV<Device, T>()(context->eigen_device<Device>(), input_data,
range, output_data);
diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 5d28b87e6b..903b898d0a 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -105,7 +105,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
}
const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
- typename TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
+ TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
std::vector<float> scores_data(num_boxes);
std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
@@ -138,8 +138,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
Tensor* output = nullptr;
TensorShape output_shape({static_cast<int>(selected.size())});
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
- typename TTypes<int, 1>::Tensor selected_indices_data =
- output->tensor<int, 1>();
+ TTypes<int, 1>::Tensor selected_indices_data = output->tensor<int, 1>();
std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
}
diff --git a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc b/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
index fb2faede2f..9a1dcd0d49 100644
--- a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
+++ b/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
@@ -697,8 +697,8 @@ class QuantizedResizeBilinearOp : public OpKernel {
// Return if the output is empty.
if (st.output->NumElements() == 0) return;
- typename TTypes<T, 4>::ConstTensor image_data = input.tensor<T, 4>();
- typename TTypes<T, 4>::Tensor output_data = st.output->tensor<T, 4>();
+ typename TTypes<T, 4>::ConstTensor image_data(input.tensor<T, 4>());
+ typename TTypes<T, 4>::Tensor output_data(st.output->tensor<T, 4>());
ResizeBilinear<T>(image_data, st.height_scale, st.width_scale, in_min,
in_max, &output_data);
diff --git a/tensorflow/core/kernels/random_crop_op.cc b/tensorflow/core/kernels/random_crop_op.cc
index 554909760a..b89bda4769 100644
--- a/tensorflow/core/kernels/random_crop_op.cc
+++ b/tensorflow/core/kernels/random_crop_op.cc
@@ -92,8 +92,8 @@ class RandomCropOp : public OpKernel {
// TODO(shlens): Do this more efficiently with memcpy once padding is
// available for smaller images.
- typename TTypes<T, 3>::ConstTensor input_data = input.tensor<T, 3>();
- typename TTypes<T, 3>::Tensor output_data = output->tensor<T, 3>();
+ typename TTypes<T, 3>::ConstTensor input_data(input.tensor<T, 3>());
+ typename TTypes<T, 3>::Tensor output_data(output->tensor<T, 3>());
for (int y = 0; y < target_height; ++y) {
for (int x = 0; x < target_width; ++x) {
diff --git a/tensorflow/core/kernels/resize_area_op.cc b/tensorflow/core/kernels/resize_area_op.cc
index ada50dfb70..98b8a0df28 100644
--- a/tensorflow/core/kernels/resize_area_op.cc
+++ b/tensorflow/core/kernels/resize_area_op.cc
@@ -149,7 +149,7 @@ class ResizeAreaOp : public OpKernel {
if (!context->status().ok()) return;
- typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
+ typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
// Precompute values used when iterating over x coordinates within a row.
// Note that it may be useful to cache x_interps for a given
@@ -190,8 +190,7 @@ class ResizeAreaOp : public OpKernel {
void ComputeLoop(const ImageResizerState& st,
const std::vector<CachedInterpolation>& x_interps,
typename TTypes<T, 4>::ConstTensor input_data) {
- typename TTypes<float, 4>::Tensor output_data =
- st.output->tensor<float, 4>();
+ TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
// When using this algorithm for downsizing, the target pixel value is the
// weighted average of all the source pixels. The weight is determined by
diff --git a/tensorflow/core/kernels/resize_bicubic_op.cc b/tensorflow/core/kernels/resize_bicubic_op.cc
index 86e61bbcef..65014b6c44 100644
--- a/tensorflow/core/kernels/resize_bicubic_op.cc
+++ b/tensorflow/core/kernels/resize_bicubic_op.cc
@@ -480,9 +480,8 @@ class ResizeBicubicOp : public OpKernel {
if (!context->status().ok()) return;
- typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
- typename TTypes<float, 4>::Tensor output_data =
- st.output->tensor<float, 4>();
+ typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+ TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
interpolate_with_caching<T>(input_data, st, output_data);
}
@@ -510,9 +509,8 @@ class ResizeBicubicOpGrad : public OpKernel {
if (!context->status().ok()) return;
- typename TTypes<float, 4>::ConstTensor input_grad =
- input.tensor<float, 4>();
- typename TTypes<T, 4>::Tensor output_grad = st.output->tensor<T, 4>();
+ TTypes<float, 4>::ConstTensor input_grad = input.tensor<float, 4>();
+ typename TTypes<T, 4>::Tensor output_grad(st.output->tensor<T, 4>());
ResizeBicubicGrad<T>(input_grad, st, output_grad);
}
diff --git a/tensorflow/core/kernels/resize_bilinear_op.cc b/tensorflow/core/kernels/resize_bilinear_op.cc
index d9cb993a4b..dde59e8e74 100644
--- a/tensorflow/core/kernels/resize_bilinear_op.cc
+++ b/tensorflow/core/kernels/resize_bilinear_op.cc
@@ -51,9 +51,8 @@ class ResizeBilinearOp : public OpKernel {
// Return if the output is empty.
if (st.output->NumElements() == 0) return;
- typename TTypes<T, 4>::ConstTensor image_data = input.tensor<T, 4>();
- typename TTypes<float, 4>::Tensor output_data =
- st.output->tensor<float, 4>();
+ typename TTypes<T, 4>::ConstTensor image_data(input.tensor<T, 4>());
+ TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
functor::ResizeBilinear<Device, T>()(context->eigen_device<Device>(),
image_data, st.height_scale,
@@ -258,9 +257,8 @@ class ResizeBilinearOpGrad : public OpKernel {
if (!context->status().ok()) return;
- typename TTypes<float, 4>::ConstTensor input_grad =
- input.tensor<float, 4>();
- typename TTypes<T, 4>::Tensor output_grad = st.output->tensor<T, 4>();
+ TTypes<float, 4>::ConstTensor input_grad = input.tensor<float, 4>();
+ typename TTypes<T, 4>::Tensor output_grad(st.output->tensor<T, 4>());
functor::ResizeBilinearGrad<Device, T>()(context->eigen_device<Device>(),
input_grad, st.height_scale,
diff --git a/tensorflow/core/kernels/resize_nearest_neighbor_op.cc b/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
index bfd29b7ec8..8ec526c2b2 100644
--- a/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
+++ b/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
@@ -56,8 +56,8 @@ class ResizeNearestNeighborOp : public OpKernel {
// Return if the output is empty.
if (st.output->NumElements() == 0) return;
- typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
- typename TTypes<T, 4>::Tensor output_data = st.output->tensor<T, 4>();
+ typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+ typename TTypes<T, 4>::Tensor output_data(st.output->tensor<T, 4>());
bool status;
if (align_corners_) {
@@ -162,8 +162,8 @@ class ResizeNearestNeighborOpGrad : public OpKernel {
// Return if the output is empty.
if (output->NumElements() == 0) return;
- typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
- typename TTypes<T, 4>::Tensor output_data = output->tensor<T, 4>();
+ typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+ typename TTypes<T, 4>::Tensor output_data(output->tensor<T, 4>());
const float height_scale =
CalculateResizeScale(out_height, in_height, align_corners_);
diff --git a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
index 44a817a5c7..c0fde8042e 100644
--- a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
+++ b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
@@ -387,9 +387,9 @@ class SampleDistortedBoundingBoxV2Op : public OpKernel {
OP_REQUIRES_OK(
context, context->allocate_output(2, TensorShape({1, 1, 4}), &bboxes));
- typename TTypes<T, 1>::Tensor begin_data = begin->tensor<T, 1>();
- typename TTypes<T, 1>::Tensor size_data = size->tensor<T, 1>();
- typename TTypes<float, 3>::Tensor bboxes_data = bboxes->tensor<float, 3>();
+ typename TTypes<T, 1>::Tensor begin_data(begin->tensor<T, 1>());
+ typename TTypes<T, 1>::Tensor size_data(size->tensor<T, 1>());
+ TTypes<float, 3>::Tensor bboxes_data = bboxes->tensor<float, 3>();
begin_data(0) = T(offset_height);
size_data(0) = T(target_height);
diff --git a/tensorflow/core/kernels/substr_op.cc b/tensorflow/core/kernels/substr_op.cc
index e29f67297f..22e45918a0 100644
--- a/tensorflow/core/kernels/substr_op.cc
+++ b/tensorflow/core/kernels/substr_op.cc
@@ -115,7 +115,7 @@ class SubstrOp : public OpKernel {
Tensor input_buffer;
OP_REQUIRES_OK(context, context->allocate_temp(
DT_STRING, output_shape, &input_buffer));
- typename TTypes<string, 1>::Tensor input_bcast =
+ TTypes<string, 1>::Tensor input_bcast =
input_buffer.shaped<string, 1>(bcast.result_shape());
input_bcast =
input.broadcast(BCast::ToIndexArray<1>(bcast.x_bcast()));
@@ -125,8 +125,8 @@ class SubstrOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_temp(DataTypeToEnum<T>::v(),
output_shape, &pos_buffer));
- typename TTypes<T, 1>::Tensor pos_bcast =
- pos_buffer.shaped<T, 1>(bcast.result_shape());
+ typename TTypes<T, 1>::Tensor pos_bcast(
+ pos_buffer.shaped<T, 1>(bcast.result_shape()));
pos_bcast =
pos_shaped.broadcast(BCast::ToIndexArray<1>(bcast.y_bcast()));
@@ -135,8 +135,8 @@ class SubstrOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_temp(DataTypeToEnum<T>::v(),
output_shape, &len_buffer));
- typename TTypes<T, 1>::Tensor len_bcast =
- len_buffer.shaped<T, 1>(bcast.result_shape());
+ typename TTypes<T, 1>::Tensor len_bcast(
+ len_buffer.shaped<T, 1>(bcast.result_shape()));
len_bcast =
len_shaped.broadcast(BCast::ToIndexArray<1>(bcast.y_bcast()));
@@ -164,7 +164,7 @@ class SubstrOp : public OpKernel {
Tensor input_buffer;
OP_REQUIRES_OK(context, context->allocate_temp(
DT_STRING, output_shape, &input_buffer));
- typename TTypes<string, 2>::Tensor input_bcast =
+ TTypes<string, 2>::Tensor input_bcast =
input_buffer.shaped<string, 2>(bcast.result_shape());
input_bcast =
input.broadcast(BCast::ToIndexArray<2>(bcast.x_bcast()));
@@ -174,8 +174,8 @@ class SubstrOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_temp(DataTypeToEnum<T>::v(),
output_shape, &pos_buffer));
- typename TTypes<T, 2>::Tensor pos_bcast =
- pos_buffer.shaped<T, 2>(bcast.result_shape());
+ typename TTypes<T, 2>::Tensor pos_bcast(
+ pos_buffer.shaped<T, 2>(bcast.result_shape()));
pos_bcast =
pos_shaped.broadcast(BCast::ToIndexArray<2>(bcast.y_bcast()));
@@ -184,8 +184,8 @@ class SubstrOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_temp(DataTypeToEnum<T>::v(),
output_shape, &len_buffer));
- typename TTypes<T, 2>::Tensor len_bcast =
- len_buffer.shaped<T, 2>(bcast.result_shape());
+ typename TTypes<T, 2>::Tensor len_bcast(
+ len_buffer.shaped<T, 2>(bcast.result_shape()));
len_bcast =
len_shaped.broadcast(BCast::ToIndexArray<2>(bcast.y_bcast()));
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-30 05:27:42 +0000