diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index eb3323e7a06..ddf1f4612f1 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -234,7 +234,7 @@ void TestEncodeDecode(int line, const std::vector<string>& data) {
// Create C++ Tensor
Tensor src(tensorflow::DT_STRING, TensorShape(dims));
for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
- src.flat<string>()(i) = data[i];
+ src.flat<tstring>()(i) = data[i];
}
TF_Tensor* dst = TF_TensorFromTensor(src, status);
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
@@ -244,7 +244,7 @@ void TestEncodeDecode(int line, const std::vector<string>& data) {
ASSERT_EQ(Status::OK(), TF_TensorToTensor(dst, &output)) << line;
ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
for (tensorflow::int64 i = 0; i < src.NumElements(); ++i) {
- ASSERT_EQ(data[i], output.flat<string>()(i)) << line;
+ ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
}
TF_DeleteTensor(dst);
@@ -1386,7 +1386,7 @@ TEST(CAPI, SavedModel) {
tensorflow::Example example;
auto* feature_map = example.mutable_features()->mutable_feature();
(*feature_map)["x"].mutable_float_list()->add_value(i);
- input.flat<string>()(i) = example.SerializeAsString();
+ input.flat<tstring>()(i) = example.SerializeAsString();
}
const tensorflow::string input_op_name(
diff --git a/tensorflow/c/tf_tensor.cc b/tensorflow/c/tf_tensor.cc
index f8d3bc220f9..44efcba99c7 100644
--- a/tensorflow/c/tf_tensor.cc
+++ b/tensorflow/c/tf_tensor.cc
@@ -354,7 +354,7 @@ TF_Tensor* TF_TensorFromTensor(const tensorflow::Tensor& src,
// Compute bytes needed for encoding.
size_t size = 0;
- const auto& srcarray = src.flat<string>();
+ const auto& srcarray = src.flat<tstring>();
for (int i = 0; i < srcarray.size(); ++i) {
const string& s = srcarray(i);
// uint64 starting_offset, TF_StringEncode-d string.
@@ -440,7 +440,7 @@ Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst) {
const char* limit = input + src_size;
*dst = Tensor(static_cast<tensorflow::DataType>(src->dtype), src->shape);
- auto dstarray = dst->flat<string>();
+ auto dstarray = dst->flat<tstring>();
for (tensorflow::int64 i = 0; i < num_elements; ++i) {
tensorflow::uint64 offset =
reinterpret_cast<const tensorflow::uint64*>(input)[i];
diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index a0353bf17a6..86f503c9e10 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -193,7 +193,7 @@ string PrintTensor(const TensorProto& tensor_proto) {
string ret;
for (int64 i = 0; i < num_elts; ++i) {
if (i > 0) strings::StrAppend(&ret, " ");
- strings::StrAppend(&ret, absl::CEscape(t.flat<string>()(i)));
+ strings::StrAppend(&ret, absl::CEscape(t.flat<tstring>()(i)));
}
return ret;
}
diff --git a/tensorflow/cc/framework/ops.cc b/tensorflow/cc/framework/ops.cc
index 920a8e79556..8516dfd7a29 100644
--- a/tensorflow/cc/framework/ops.cc
+++ b/tensorflow/cc/framework/ops.cc
@@ -97,7 +97,7 @@ Input::Initializer::Initializer(
Tensor elem = e.tensor;
if (first.tensor.dtype() == DT_STRING) {
for (int i = 0; i < elem.NumElements(); ++i) {
- t.flat<string>()(offset + i) = elem.flat<string>()(i);
+ t.flat<tstring>()(offset + i) = elem.flat<tstring>()(i);
}
offset += elem.NumElements();
} else {
diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc
index dfc7ccd9542..a3b80fbdba5 100644
--- a/tensorflow/cc/saved_model/loader.cc
+++ b/tensorflow/cc/saved_model/loader.cc
@@ -75,7 +75,7 @@ Status LoadMetaGraphIntoSession(const MetaGraphDef& meta_graph_def,
Tensor CreateStringTensor(const string& value) {
Tensor tensor(DT_STRING, TensorShape({}));
- tensor.scalar<string>()() = value;
+ tensor.scalar<tstring>()() = value;
return tensor;
}
@@ -219,7 +219,7 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir,
// Add variables to the graph.
Tensor variables_path_tensor(DT_STRING, TensorShape({}));
- variables_path_tensor.scalar<string>()() = variables_path;
+ variables_path_tensor.scalar<tstring>()() = variables_path;
std::vector<std::pair<string, Tensor>> inputs = {
{string(variable_filename_const_op_name), variables_path_tensor}};
diff --git a/tensorflow/compiler/jit/kernels/xla_ops.cc b/tensorflow/compiler/jit/kernels/xla_ops.cc
index b23980830ba..87d6548a1a7 100644
--- a/tensorflow/compiler/jit/kernels/xla_ops.cc
+++ b/tensorflow/compiler/jit/kernels/xla_ops.cc
@@ -508,7 +508,7 @@ void XlaCompileOp::Compute(OpKernelContext* ctx) {
client, executable, kernel, std::move(variables), constants_.size()));
Tensor compilation_key(cpu_allocator, DT_STRING, TensorShape({}));
- compilation_key.flat<string>()(0) = key;
+ compilation_key.flat<tstring>()(0) = key;
Tensor compilation_successful(cpu_allocator, DT_BOOL, TensorShape({}));
compilation_successful.flat<bool>()(0) = true;
@@ -523,7 +523,7 @@ XlaRunOp::XlaRunOp(OpKernelConstruction* ctx)
void XlaRunOp::Compute(OpKernelContext* ctx) {
VLOG(3) << "XlaRunOp " << def().name();
Tensor key_tensor = ctx->input(ctx->num_inputs() - 1);
- const XlaExecutableClosureStore::KeyT& key = key_tensor.flat<string>()(0);
+ const XlaExecutableClosureStore::KeyT& key = key_tensor.flat<tstring>()(0);
XlaExecutableClosure closure =
XlaExecutableClosureStore::Global()->Consume(key);
diff --git a/tensorflow/compiler/tf2tensorrt/kernels/get_calibration_data_op.cc b/tensorflow/compiler/tf2tensorrt/kernels/get_calibration_data_op.cc
index 83a16892816..374f75c0ab9 100644
--- a/tensorflow/compiler/tf2tensorrt/kernels/get_calibration_data_op.cc
+++ b/tensorflow/compiler/tf2tensorrt/kernels/get_calibration_data_op.cc
@@ -40,7 +40,7 @@ class GetCalibrationDataOp : public OpKernel {
// serialized string to that tensor, and later sess.run() will copy it back
// to host. We need to optimize this.
- const string& resource_name = context->input(0).scalar<string>()();
+ const string& resource_name = context->input(0).scalar<tstring>()();
// Get the resource.
TRTEngineCacheResource* resource = nullptr;
OP_REQUIRES_OK(context, context->resource_manager()->Lookup(
@@ -59,7 +59,7 @@ class GetCalibrationDataOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_output(0, TensorShape({}), &output));
- output->scalar<string>()() = serialized_resource;
+ output->scalar<tstring>()() = serialized_resource;
}
};
diff --git a/tensorflow/compiler/tf2tensorrt/kernels/trt_engine_resource_ops.cc b/tensorflow/compiler/tf2tensorrt/kernels/trt_engine_resource_ops.cc
index e28dcc1cbba..51f7e3aabc5 100644
--- a/tensorflow/compiler/tf2tensorrt/kernels/trt_engine_resource_ops.cc
+++ b/tensorflow/compiler/tf2tensorrt/kernels/trt_engine_resource_ops.cc
@@ -109,7 +109,7 @@ class InitializeTRTResource : public OpKernel {
resource->cache_.size(), " entries."));
// Get the file name.
- const string& filename = ctx->input(1).scalar<string>()();
+ const string& filename = ctx->input(1).scalar<tstring>()();
OP_REQUIRES(ctx, !filename.empty(),
errors::InvalidArgument("filename cannot be empty."));
@@ -171,8 +171,8 @@ class SerializeTRTResource : public OpKernel {
}
void Compute(OpKernelContext* ctx) override {
- const string& resource_name = ctx->input(0).scalar<string>()();
- const string& filename = ctx->input(1).scalar<string>()();
+ const string& resource_name = ctx->input(0).scalar<tstring>()();
+ const string& filename = ctx->input(1).scalar<tstring>()();
OP_REQUIRES(ctx, !filename.empty(),
errors::InvalidArgument("filename cannot be empty."));
diff --git a/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc b/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc
index b791519c097..89daa98ee18 100644
--- a/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc
+++ b/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc
@@ -151,7 +151,7 @@ void XRTCompileOp::Compute(OpKernelContext* ctx) {
xrt::XLAComputation computation_proto;
OP_REQUIRES(
ctx,
- computation_proto.ParseFromString(computation_input.scalar<string>()()),
+ computation_proto.ParseFromString(computation_input.scalar<tstring>()()),
errors::InvalidArgument(
"Unable to parse computation input to XLAComputation"));
@@ -191,7 +191,7 @@ void XRTCompileOp::Compute(OpKernelContext* ctx) {
.ComputeProgramShape()
.ToProto();
Tensor program_shape_output(DT_STRING, TensorShape({1}));
- program_shape_output.vec<string>()(0) = program_shape.SerializeAsString();
+ program_shape_output.vec<tstring>()(0) = program_shape.SerializeAsString();
ctx->set_output(1, program_shape_output);
}
diff --git a/tensorflow/compiler/xrt/kernels/xrt_execute_op.cc b/tensorflow/compiler/xrt/kernels/xrt_execute_op.cc
index 231387e314f..1c4e1f7e2c7 100644
--- a/tensorflow/compiler/xrt/kernels/xrt_execute_op.cc
+++ b/tensorflow/compiler/xrt/kernels/xrt_execute_op.cc
@@ -260,7 +260,7 @@ Status XRTExecuteOp::DoWork(OpKernelContext* context) {
TF_RET_CHECK(TensorShapeUtils::IsScalar(execution_config.shape()));
xrt::XRTExecutionConfig config_proto;
TF_RET_CHECK(
- config_proto.ParseFromString(execution_config.scalar<string>()()));
+ config_proto.ParseFromString(execution_config.scalar<tstring>()()));
int core_index_in_replica = config_proto.core_index_in_replica();
TF_RET_CHECK(core_index_in_replica == 0);
@@ -343,12 +343,12 @@ Status XRTExecuteChainedOp::DoWork(OpKernelContext* context) {
const Tensor& execution_plan = context->input(0);
TF_RET_CHECK(TensorShapeUtils::IsScalar(execution_plan.shape()));
xrt::XRTChainedExecutePlan plan;
- TF_RET_CHECK(plan.ParseFromString(execution_plan.scalar<string>()()));
+ TF_RET_CHECK(plan.ParseFromString(execution_plan.scalar<tstring>()()));
const Tensor& execution_config = context->input(1);
TF_RET_CHECK(TensorShapeUtils::IsScalar(execution_config.shape()));
xrt::XRTChainedExecuteConfig config;
- TF_RET_CHECK(config.ParseFromString(execution_config.scalar<string>()()));
+ TF_RET_CHECK(config.ParseFromString(execution_config.scalar<tstring>()()));
XRTCompilationCache* cache;
TF_RETURN_IF_ERROR(rm->Lookup<XRTCompilationCache>(
diff --git a/tensorflow/compiler/xrt/kernels/xrt_state_ops.h b/tensorflow/compiler/xrt/kernels/xrt_state_ops.h
index 2ffde52af06..8afd2051c00 100644
--- a/tensorflow/compiler/xrt/kernels/xrt_state_ops.h
+++ b/tensorflow/compiler/xrt/kernels/xrt_state_ops.h
@@ -177,7 +177,7 @@ class XRTAllocateOp : public OpKernel {
xrt::XLAAllocation allocation_proto;
OP_REQUIRES(
ctx,
- allocation_proto.ParseFromString(allocation_info.scalar<string>()()),
+ allocation_proto.ParseFromString(allocation_info.scalar<tstring>()()),
errors::InvalidArgument(
"Unable to parse allocation input to XLAAllocation"));
@@ -419,7 +419,7 @@ class XRTMakeTupleOp : public OpKernel {
errors::Internal("tuple description input should be a string scalar"));
xrt::XLATupleNode tuple_proto;
OP_REQUIRES(
- ctx, tuple_proto.ParseFromString(tuple_info.scalar<string>()()),
+ ctx, tuple_proto.ParseFromString(tuple_info.scalar<tstring>()()),
errors::InvalidArgument("Unable to parse tuple input to XLATupleNode"));
OpInputList arg_list;
@@ -627,7 +627,7 @@ class XRTWriteLiteralOp : public OpKernel {
errors::Internal("literal input should be a string scalar"));
xla::LiteralProto literal_proto;
OP_REQUIRES(ctx,
- literal_proto.ParseFromString(literal_info.scalar<string>()()),
+ literal_proto.ParseFromString(literal_info.scalar<tstring>()()),
errors::InvalidArgument(
"Unable to parse allocation input to LiteralProto"));
xla::Literal literal;
diff --git a/tensorflow/compiler/xrt/tests/raw_api_test.cc b/tensorflow/compiler/xrt/tests/raw_api_test.cc
index f0729251eeb..427a631f82d 100644
--- a/tensorflow/compiler/xrt/tests/raw_api_test.cc
+++ b/tensorflow/compiler/xrt/tests/raw_api_test.cc
@@ -127,7 +127,7 @@ xla::LiteralProto FloatMatrix(
xla::Literal ReadOutputLiteral(const std::vector<Tensor>& outputs, size_t idx) {
xla::LiteralProto response;
- CHECK(response.ParseFromString(outputs[idx].scalar<string>()()));
+ CHECK(response.ParseFromString(outputs[idx].scalar<tstring>()()));
return xla::Literal::CreateFromProto(response).ValueOrDie();
}
@@ -316,7 +316,7 @@ TEST(RawApiTest, AllocFromTensor) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(literal, response));
}
@@ -351,7 +351,7 @@ TEST(RawApiTest, AllocUninitialized) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto read_back_literal;
EXPECT_TRUE(
- read_back_literal.ParseFromString(outputs[0].scalar<string>()()));
+ read_back_literal.ParseFromString(outputs[0].scalar<tstring>()()));
Tensor read_back_tensor;
TF_ASSERT_OK(LiteralToHostTensor(
xla::Literal::CreateFromProto(read_back_literal).ValueOrDie(), DT_FLOAT,
@@ -381,7 +381,7 @@ TEST(RawApiTest, AllocUninitialized) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(response, new_literal));
}
}
@@ -413,7 +413,7 @@ TEST(RawApiTest, AllocFromTensorTuple) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(literal, response));
}
@@ -439,7 +439,7 @@ TEST(RawApiTest, AllocFromTensorTupleSingle) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(literal, response));
}
@@ -465,7 +465,7 @@ TEST(RawApiTest, AllocFromTensorRelayout) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
// We have sent literal's data (in array layout) with a attribute layout
// {0,1}, so the expected literal read from device needs to be changed
// accordingly.
@@ -493,7 +493,7 @@ TEST(RawApiTest, AllocAndRewrite) {
int64 allocation_handle = outputs[1].scalar<int64>()();
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(alloc.value(), response));
xla::LiteralProto new_literal =
@@ -512,7 +512,7 @@ TEST(RawApiTest, AllocAndRewrite) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto new_response;
- EXPECT_TRUE(new_response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(new_response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(new_literal, new_response));
Tensor release_tensor(DT_INT64, TensorShape({1}));
@@ -652,7 +652,7 @@ TEST(RawApiTest, ReadAndWriteState) {
session.Run(ClientSession::FeedType(), {read_back}, {release}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(alloc.value(), response));
}
@@ -673,7 +673,7 @@ TEST(RawApiTest, ReadAndWriteStateAutoFree) {
TF_EXPECT_OK(session.Run({read_back}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(alloc.value(), response));
}
@@ -707,13 +707,13 @@ TEST(RawApiTest, SubBuffer) {
auto base_elements = base_literal.DecomposeTuple();
auto nested_0_elements = base_elements[0].Clone().DecomposeTuple();
xla::LiteralProto response_0;
- EXPECT_TRUE(response_0.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response_0.ParseFromString(outputs[0].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(base_elements[0], response_0));
xla::LiteralProto response_1;
- EXPECT_TRUE(response_1.ParseFromString(outputs[1].scalar<string>()()));
+ EXPECT_TRUE(response_1.ParseFromString(outputs[1].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(base_elements[1], response_1));
xla::LiteralProto response_00;
- EXPECT_TRUE(response_00.ParseFromString(outputs[2].scalar<string>()()));
+ EXPECT_TRUE(response_00.ParseFromString(outputs[2].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralToLiteralProto(nested_0_elements[0], response_00));
}
@@ -779,9 +779,9 @@ TEST(RawApiTest, MakeTuple) {
std::vector<Tensor> outputs;
TF_EXPECT_OK(session.Run({res_0, res_1}, &outputs));
xla::LiteralProto response_0;
- EXPECT_TRUE(response_0.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response_0.ParseFromString(outputs[0].scalar<tstring>()()));
xla::LiteralProto response_1;
- EXPECT_TRUE(response_1.ParseFromString(outputs[1].scalar<string>()()));
+ EXPECT_TRUE(response_1.ParseFromString(outputs[1].scalar<tstring>()()));
auto expected_0 = MakeTuple0();
EXPECT_TRUE(CompareLiteralProtos(response_0, expected_0));
@@ -853,7 +853,7 @@ TEST(RawApiTest, ExecuteChainedOpByOp) {
TF_EXPECT_OK(session.Run({read_back}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR1<float>({-150.0f, -36.0f});
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
@@ -973,7 +973,7 @@ TEST(RawApiTest, ExecuteChained) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR1<float>({-150.0f, -36.0f});
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
@@ -1022,13 +1022,13 @@ TEST(RawApiTest, CompileAndExecute) {
TF_EXPECT_OK(session.Run({read_back, c_handle.program_shape}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR1<float>({27.0f, 21.0f});
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
xla::ProgramShapeProto program_shape;
- EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<string>()(0)));
+ EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<tstring>()(0)));
EXPECT_EQ(program_shape.parameters_size(), 2);
}
@@ -1077,13 +1077,13 @@ TEST(RawApiTest, CompileAndExecuteWithArgumentVector) {
TF_EXPECT_OK(session.Run({read_back, c_handle.program_shape}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR1<float>({27.0f, 21.0f});
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
xla::ProgramShapeProto program_shape;
- EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<string>()(0)));
+ EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<tstring>()(0)));
EXPECT_EQ(program_shape.parameters_size(), 2);
}
@@ -1128,7 +1128,8 @@ TEST(RawApiTest, CompileWithXlaReturnShapes) {
{release}, &outputs));
xla::ProgramShapeProto program_shape_proto;
- EXPECT_TRUE(program_shape_proto.ParseFromString(outputs[0].vec<string>()(0)));
+ EXPECT_TRUE(
+ program_shape_proto.ParseFromString(outputs[0].vec<tstring>()(0)));
xla::ProgramShape program_shape(program_shape_proto);
EXPECT_EQ(program_shape.parameters_size(), 1);
@@ -1196,7 +1197,7 @@ TEST(RawApiTest, DotGeneralWithLayoutTest) {
TF_EXPECT_OK(session.Run({read_back}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected =
xla::LiteralUtil::CreateR2WithLayout<float>({{18.0f}, {44.0f}}, layout);
@@ -1231,7 +1232,7 @@ TEST(RawApiTest, CompileAndExecuteZeroArg) {
TF_EXPECT_OK(session.Run({read_back}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR0<float>(3.0f);
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
@@ -1281,7 +1282,7 @@ TEST(RawApiTest, CompileAndExecuteReturnTuple) {
TF_EXPECT_OK(session.Run({read_back}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto sum = xla::LiteralUtil::CreateR1<float>({9.0f, 7.0f});
auto expected = xla::LiteralUtil::MakeTuple({&sum});
@@ -1343,7 +1344,7 @@ TEST(RawApiTest, CompileAndExecuteReturnExplodedTuple) {
EXPECT_EQ(voutputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(voutputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(voutputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR0<float>(kResults[i]);
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
@@ -1514,13 +1515,13 @@ TEST(RawApiTest, CompileAndExecuteWithS64Argument) {
TF_EXPECT_OK(session.Run({read_back, c_handle.program_shape}, &outputs));
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto expected = xla::LiteralUtil::CreateR0<int64>(15123899);
EXPECT_TRUE(CompareLiteralToLiteralProto(expected, response));
xla::ProgramShapeProto program_shape;
- EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<string>()(0)));
+ EXPECT_TRUE(program_shape.ParseFromString(outputs[1].vec<tstring>()(0)));
EXPECT_EQ(program_shape.parameters_size(), 2);
EXPECT_TRUE(xla::ShapeUtil::HasPrimitiveType(
xla::Shape(program_shape.result()), xla::S64));
@@ -1580,7 +1581,7 @@ TEST(RawApiTest, TestDeviceMemoryCompaction) {
// we have on record.
for (size_t i = 1, j = 0; i < handles.size(); i += 2, ++j) {
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[j].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[j].scalar<tstring>()()));
EXPECT_TRUE(CompareLiteralProtos(allocs[i].value(), response));
}
}
@@ -1668,7 +1669,7 @@ TEST(RawApiTest, TestDeviceMemorySwap) {
EXPECT_EQ(outputs.size(), 1);
xla::LiteralProto response;
- EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<string>()()));
+ EXPECT_TRUE(response.ParseFromString(outputs[0].scalar<tstring>()()));
auto literal = xla::Literal::CreateFromProto(response).ValueOrDie();
EXPECT_EQ(literal, zero_literal);
}
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc
index 51b27ea4212..1e6de7ee17e 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc
@@ -214,8 +214,8 @@ class ToBigtableOp : public AsyncOpKernel {
std::vector<string> columns;
columns.reserve(column_families_tensor->NumElements());
for (uint64 i = 0; i < column_families_tensor->NumElements(); ++i) {
- column_families.push_back(column_families_tensor->flat<string>()(i));
- columns.push_back(columns_tensor->flat<string>()(i));
+ column_families.push_back(column_families_tensor->flat<tstring>()(i));
+ columns.push_back(columns_tensor->flat<tstring>()(i));
}
DatasetBase* dataset;
@@ -317,7 +317,7 @@ class ToBigtableOp : public AsyncOpKernel {
"Iterator produced a set of Tensors shorter than expected");
}
::google::cloud::bigtable::SingleRowMutation mutation(
- std::move(tensors[0].scalar<string>()()));
+ std::move(tensors[0].scalar<tstring>()()));
std::chrono::milliseconds timestamp(timestamp_int);
for (size_t i = 1; i < tensors.size(); ++i) {
if (!TensorShapeUtils::IsScalar(tensors[i].shape())) {
@@ -326,11 +326,11 @@ class ToBigtableOp : public AsyncOpKernel {
if (timestamp_int == -1) {
mutation.emplace_back(::google::cloud::bigtable::SetCell(
column_families[i - 1], columns[i - 1],
- std::move(tensors[i].scalar<string>()())));
+ std::move(tensors[i].scalar<tstring>()())));
} else {
mutation.emplace_back(::google::cloud::bigtable::SetCell(
column_families[i - 1], columns[i - 1], timestamp,
- std::move(tensors[i].scalar<string>()())));
+ std::move(tensors[i].scalar<tstring>()())));
}
}
bulk_mutation->emplace_back(std::move(mutation));
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc
index f1973e370a8..b341b0cae26 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc
@@ -156,13 +156,13 @@ class BigtableLookupDatasetOp : public UnaryDatasetOpKernel {
::google::cloud::StatusOr<
std::pair<bool, ::google::cloud::bigtable::Row>>
row = dataset()->table_->table().ReadRow(
- input_tensors[0].scalar<string>()(), dataset()->filter_);
+ input_tensors[0].scalar<tstring>()(), dataset()->filter_);
if (!row.ok()) {
return GcpStatusToTfStatus(row.status());
}
if (!row->first) {
return errors::DataLoss("Row key '",
- input_tensors[0].scalar<string>()(),
+ input_tensors[0].scalar<tstring>()(),
"' not found.");
}
TF_RETURN_IF_ERROR(ParseRow(ctx, row->second, out_tensors));
@@ -180,7 +180,7 @@ class BigtableLookupDatasetOp : public UnaryDatasetOpKernel {
std::vector<Tensor>* out_tensors) {
out_tensors->reserve(dataset()->columns_.size() + 1);
Tensor row_key_tensor(ctx->allocator({}), DT_STRING, {});
- row_key_tensor.scalar<string>()() = string(row.row_key());
+ row_key_tensor.scalar<tstring>()() = tstring(row.row_key());
out_tensors->emplace_back(std::move(row_key_tensor));
if (row.cells().size() > 2 * dataset()->columns_.size()) {
@@ -198,7 +198,7 @@ class BigtableLookupDatasetOp : public UnaryDatasetOpKernel {
if (cell_itr->family_name() == dataset()->column_families_[i] &&
string(cell_itr->column_qualifier()) ==
dataset()->columns_[i]) {
- col_tensor.scalar<string>()() = string(cell_itr->value());
+ col_tensor.scalar<tstring>()() = tstring(cell_itr->value());
found_column = true;
}
}
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc
index 352e7af7de9..3908d40908d 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc
@@ -99,7 +99,7 @@ class BigtablePrefixKeyDatasetOp : public DatasetOpKernel {
const ::google::cloud::bigtable::Row& row,
std::vector<Tensor>* out_tensors) override {
Tensor output_tensor(ctx->allocator({}), DT_STRING, {});
- output_tensor.scalar<string>()() = string(row.row_key());
+ output_tensor.scalar<tstring>()() = tstring(row.row_key());
out_tensors->emplace_back(std::move(output_tensor));
return Status::OK();
}
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc
index 591bc786bd8..e3e6acba351 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc
@@ -105,7 +105,7 @@ class BigtableRangeKeyDatasetOp : public DatasetOpKernel {
const ::google::cloud::bigtable::Row& row,
std::vector<Tensor>* out_tensors) override {
Tensor output_tensor(ctx->allocator({}), DT_STRING, {});
- output_tensor.scalar<string>()() = string(row.row_key());
+ output_tensor.scalar<tstring>()() = string(row.row_key());
out_tensors->emplace_back(std::move(output_tensor));
return Status::OK();
}
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc
index d3780f56ed8..0ca39f18670 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc
@@ -177,11 +177,11 @@ class BigtableSampleKeyPairsDatasetOp : public DatasetOpKernel {
*end_of_sequence = false;
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
- out_tensors->back().scalar<string>()() = keys_[index_];
+ out_tensors->back().scalar<tstring>()() = keys_[index_];
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
- out_tensors->back().scalar<string>()() = keys_[index_ + 1];
+ out_tensors->back().scalar<tstring>()() = keys_[index_ + 1];
++index_;
return Status::OK();
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc
index 967920ef3dc..513514f63c1 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc
@@ -99,7 +99,7 @@ class BigtableSampleKeysDatasetOp : public DatasetOpKernel {
if (index_ < row_keys_.size()) {
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
- out_tensors->back().scalar<string>()() =
+ out_tensors->back().scalar<tstring>()() =
string(row_keys_[index_].row_key);
*end_of_sequence = false;
index_++;
diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc
index c94e66057c9..d5071537b9b 100644
--- a/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc
+++ b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc
@@ -177,7 +177,7 @@ class BigtableScanDatasetOp : public DatasetOpKernel {
std::vector<Tensor>* out_tensors) override {
out_tensors->reserve(dataset()->columns_.size() + 1);
Tensor row_key_tensor(ctx->allocator({}), DT_STRING, {});
- row_key_tensor.scalar<string>()() = string(row.row_key());
+ row_key_tensor.scalar<tstring>()() = string(row.row_key());
out_tensors->emplace_back(std::move(row_key_tensor));
if (row.cells().size() > 2 * dataset()->columns_.size()) {
diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
index 9655e49d91b..5f9976a491c 100644
--- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
@@ -46,7 +46,7 @@ class CreateTreeEnsembleVariableOp : public OpKernel {
OP_REQUIRES_OK(context, context->input("tree_ensemble_config",
&tree_ensemble_config_t));
auto* result = new DecisionTreeEnsembleResource();
- if (!result->InitFromSerialized(tree_ensemble_config_t->scalar<string>()(),
+ if (!result->InitFromSerialized(tree_ensemble_config_t->scalar<tstring>()(),
stamp_token)) {
result->Unref();
OP_REQUIRES(
@@ -99,7 +99,7 @@ class TreeEnsembleSerializeOp : public OpKernel {
Tensor* output_config_t = nullptr;
OP_REQUIRES_OK(
context, context->allocate_output(1, TensorShape(), &output_config_t));
- output_config_t->scalar<string>()() =
+ output_config_t->scalar<tstring>()() =
ensemble_resource->SerializeAsString();
}
};
@@ -130,7 +130,7 @@ class TreeEnsembleDeserializeOp : public OpKernel {
OP_REQUIRES(
context,
ensemble_resource->InitFromSerialized(
- tree_ensemble_config_t->scalar<string>()(), stamp_token),
+ tree_ensemble_config_t->scalar<tstring>()(), stamp_token),
errors::InvalidArgument("Unable to parse tree ensemble config."));
}
};
diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
index 431dc68836b..bea5c2a839a 100644
--- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc
@@ -324,7 +324,7 @@ class QuantileAccumulatorAddSummariesOp : public OpKernel {
context,
ParseProtoUnlimited(
summary_proto,
- summary_list[resource_handle_idx].scalar<string>()()),
+ summary_list[resource_handle_idx].scalar<tstring>()()),
errors::InvalidArgument("Unable to parse quantile summary."));
std::vector<QuantileSummaryEntry> entries;
entries.reserve(summary_proto->entries_size());
@@ -543,7 +543,7 @@ class QuantileAccumulatorDeserializeOp : public OpKernel {
::boosted_trees::QuantileStreamState state_proto;
OP_REQUIRES(
context,
- ParseProtoUnlimited(&state_proto, stream_state_t->scalar<string>()()),
+ ParseProtoUnlimited(&state_proto, stream_state_t->scalar<tstring>()()),
errors::InvalidArgument("Unabnle to parse quantile stream state."));
std::vector<QuantileSummary> summaries;
summaries.reserve(state_proto.summaries_size());
diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
index 65276242aba..4e96957130e 100644
--- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc
@@ -213,8 +213,8 @@ class BuildDenseInequalitySplitsOp : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output("split_infos",
TensorShape({size_output}),
&output_splits_t));
- tensorflow::TTypes<string>::Vec output_splits =
- output_splits_t->vec<string>();
+ tensorflow::TTypes<tstring>::Vec output_splits =
+ output_splits_t->vec<tstring>();
if (num_elements == 0) {
return;
@@ -529,8 +529,8 @@ class BuildSparseInequalitySplitsOp : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output(
"split_infos", TensorShape({num_elements}),
&output_splits_t));
- tensorflow::TTypes<string>::Vec output_splits =
- output_splits_t->vec<string>();
+ tensorflow::TTypes<tstring>::Vec output_splits =
+ output_splits_t->vec<tstring>();
SplitBuilderState state(context);
// For each tree node that needs to be split.
for (int root_idx = 0; root_idx < num_elements; ++root_idx) {
@@ -780,8 +780,8 @@ class BuildCategoricalEqualitySplitsOp : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output("split_infos",
TensorShape({size_output}),
&output_splits_t));
- tensorflow::TTypes<string>::Vec output_splits =
- output_splits_t->vec<string>();
+ tensorflow::TTypes<tstring>::Vec output_splits =
+ output_splits_t->vec<tstring>();
if (num_elements == 0) {
return;
}
diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
index 8cb5cfbd3dd..bf5f5d34457 100644
--- a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
@@ -468,7 +468,7 @@ class GrowTreeEnsembleOp : public OpKernel {
for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) {
const auto& partition_ids = partition_ids_list[handler_id].vec<int32>();
const auto& gains = gains_list[handler_id].vec<float>();
- const auto& splits = splits_list[handler_id].vec<string>();
+ const auto& splits = splits_list[handler_id].vec<tstring>();
OP_REQUIRES(context, partition_ids.size() == gains.size(),
errors::InvalidArgument(
"Inconsistent partition Ids and gains tensors: ",
@@ -502,7 +502,7 @@ class GrowTreeEnsembleOp : public OpKernel {
// Find best split per partition going through every feature candidate.
for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) {
const auto& gains = gains_list[handler_id].vec<float>();
- const auto& splits = splits_list[handler_id].vec<string>();
+ const auto& splits = splits_list[handler_id].vec<tstring>();
OP_REQUIRES(context, gains.size() == 1,
errors::InvalidArgument(
"Gains size must be one for oblivious weak learner: ",
diff --git a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
index b0f9237ea27..7a19a1c9231 100644
--- a/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
+++ b/tensorflow/contrib/cloud/kernels/bigquery_reader_ops.cc
@@ -153,7 +153,7 @@ class GenerateBigQueryReaderPartitionsOp : public OpKernel {
context->allocate_output(0, TensorShape({num_partitions_}),
&output_tensor));
- auto output = output_tensor->template flat<string>();
+ auto output = output_tensor->template flat<tstring>();
for (int64 i = 0; i < num_partitions_; ++i) {
BigQueryTablePartition partition;
partition.set_start_index(i * partition_size);
diff --git a/tensorflow/contrib/ffmpeg/decode_audio_op.cc b/tensorflow/contrib/ffmpeg/decode_audio_op.cc
index ca65ad45326..32e62a6725f 100644
--- a/tensorflow/contrib/ffmpeg/decode_audio_op.cc
+++ b/tensorflow/contrib/ffmpeg/decode_audio_op.cc
@@ -135,9 +135,10 @@ class DecodeAudioOpV2 : public OpKernel {
"channel_count must be a rank-0 tensor but got shape ",
channel_count_tensor.shape().DebugString()));
- const tensorflow::StringPiece contents = contents_tensor.scalar<string>()();
+ const tensorflow::StringPiece contents =
+ contents_tensor.scalar<tstring>()();
const string file_format =
- absl::AsciiStrToLower(file_format_tensor.scalar<string>()());
+ absl::AsciiStrToLower(file_format_tensor.scalar<tstring>()());
const int32 samples_per_second =
samples_per_second_tensor.scalar<int32>()();
const int32 channel_count = channel_count_tensor.scalar<int32>()();
@@ -243,7 +244,7 @@ class DecodeAudioOp : public OpKernel {
errors::InvalidArgument("contents must be scalar but got shape ",
contents.shape().DebugString()));
- const tensorflow::StringPiece file_contents = contents.scalar<string>()();
+ const tensorflow::StringPiece file_contents = contents.scalar<tstring>()();
Decode(context, file_contents, file_format_, samples_per_second_,
channel_count_, "");
}
diff --git a/tensorflow/contrib/ffmpeg/decode_video_op.cc b/tensorflow/contrib/ffmpeg/decode_video_op.cc
index 6f8ad486d10..0bfdc2781aa 100644
--- a/tensorflow/contrib/ffmpeg/decode_video_op.cc
+++ b/tensorflow/contrib/ffmpeg/decode_video_op.cc
@@ -45,7 +45,8 @@ class DecodeVideoOp : public OpKernel {
errors::InvalidArgument(
"contents must be a rank-0 tensor but got shape ",
contents_tensor.shape().DebugString()));
- const tensorflow::StringPiece contents = contents_tensor.scalar<string>()();
+ const tensorflow::StringPiece contents =
+ contents_tensor.scalar<tstring>()();
// Write the input data to a temp file.
string extension;
diff --git a/tensorflow/contrib/ffmpeg/encode_audio_op.cc b/tensorflow/contrib/ffmpeg/encode_audio_op.cc
index 7de09e062ec..ee418fb9020 100644
--- a/tensorflow/contrib/ffmpeg/encode_audio_op.cc
+++ b/tensorflow/contrib/ffmpeg/encode_audio_op.cc
@@ -45,7 +45,7 @@ void Encode(OpKernelContext* context, const Tensor& contents,
// Copy the encoded audio file to the output tensor.
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape(), &output));
- output->scalar<string>()() = encoded_audio;
+ output->scalar<tstring>()() = encoded_audio;
}
} // namespace
@@ -95,7 +95,7 @@ class EncodeAudioOpV2 : public OpKernel {
bits_per_second_tensor.shape().DebugString()));
const string file_format =
- absl::AsciiStrToLower(file_format_tensor.scalar<string>()());
+ absl::AsciiStrToLower(file_format_tensor.scalar<tstring>()());
const int32 samples_per_second =
samples_per_second_tensor.scalar<int32>()();
const int32 bits_per_second = bits_per_second_tensor.scalar<int32>()();
diff --git a/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc
index a4084e79753..48491b9f051 100644
--- a/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc
+++ b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc
@@ -198,7 +198,7 @@ class SequenceFileDatasetOp : public DatasetOpKernel {
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
*output = new Dataset(ctx, filenames, output_types_);
@@ -264,11 +264,11 @@ class SequenceFileDatasetOp : public DatasetOpKernel {
TF_RETURN_IF_ERROR(status);
Tensor key_tensor(ctx->allocator({}), DT_STRING, {});
- key_tensor.scalar<string>()() = key;
+ key_tensor.scalar<tstring>()() = key;
out_tensors->emplace_back(std::move(key_tensor));
Tensor value_tensor(ctx->allocator({}), DT_STRING, {});
- value_tensor.scalar<string>()() = value;
+ value_tensor.scalar<tstring>()() = value;
out_tensors->emplace_back(std::move(value_tensor));
*end_of_sequence = false;
diff --git a/tensorflow/contrib/ignite/kernels/dataset/ignite_binary_object_parser.cc b/tensorflow/contrib/ignite/kernels/dataset/ignite_binary_object_parser.cc
index 4218ec05f2c..41c9a8b1f49 100644
--- a/tensorflow/contrib/ignite/kernels/dataset/ignite_binary_object_parser.cc
+++ b/tensorflow/contrib/ignite/kernels/dataset/ignite_binary_object_parser.cc
@@ -73,7 +73,7 @@ Status BinaryObjectParser::Parse(uint8_t** ptr,
}
case STRING: {
out_tensors->emplace_back(cpu_allocator(), DT_STRING, TensorShape({}));
- out_tensors->back().scalar<string>()() = ParseString(ptr);
+ out_tensors->back().scalar<tstring>()() = ParseString(ptr);
break;
}
case DATE: {
@@ -150,7 +150,7 @@ Status BinaryObjectParser::Parse(uint8_t** ptr,
out_tensors->emplace_back(cpu_allocator(), DT_STRING,
TensorShape({length}));
for (int32_t i = 0; i < length; i++)
- out_tensors->back().vec<string>()(i) = ParseString(ptr);
+ out_tensors->back().vec<tstring>()(i) = ParseString(ptr);
break;
}
case DATE_ARR: {
diff --git a/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc b/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc
index 886f6798150..d5da76a753f 100644
--- a/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc
+++ b/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc
@@ -30,7 +30,7 @@ class ObtainNextOp : public OpKernel {
const Tensor* list;
OP_REQUIRES_OK(ctx, ctx->input("list", &list));
int64 num_elements = list->NumElements();
- auto list_flat = list->flat<string>();
+ auto list_flat = list->flat<tstring>();
// Allocate output.
Tensor* output_tensor = nullptr;
@@ -48,7 +48,7 @@ class ObtainNextOp : public OpKernel {
*pos = (*pos + 1) % num_elements;
// Assign value to output.
- output_tensor->scalar<string>()() = list_flat(*pos);
+ output_tensor->scalar<tstring>()() = list_flat(*pos);
}
};
diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc
index bb0d4c178dc..8e0e7133686 100644
--- a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc
+++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc
@@ -33,7 +33,7 @@ class KafkaDatasetOp : public DatasetOpKernel {
std::vector<string> topics;
topics.reserve(topics_tensor->NumElements());
for (int i = 0; i < topics_tensor->NumElements(); ++i) {
- topics.push_back(topics_tensor->flat<string>()(i));
+ topics.push_back(topics_tensor->flat<tstring>()(i));
}
std::string servers = "";
@@ -128,7 +128,7 @@ class KafkaDatasetOp : public DatasetOpKernel {
if (message->err() == RdKafka::ERR_NO_ERROR) {
// Produce the line as output.
Tensor line_tensor(cpu_allocator(), DT_STRING, {});
- line_tensor.scalar<string>()() =
+ line_tensor.scalar<tstring>()() =
std::string(static_cast<const char*>(message->payload()),
message->len());
out_tensors->emplace_back(std::move(line_tensor));
diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
index ee4b0373ef7..0923bdd32bb 100644
--- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
+++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
@@ -78,7 +78,7 @@ template <>
int64 SparseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
const int64 start = feature_start_indices_[batch];
if (DT_STRING == values_.dtype())
- return Fingerprint64(values_.vec<string>().data()[start + n]);
+ return Fingerprint64(values_.vec<tstring>().data()[start + n]);
return values_.vec<int64>().data()[start + n];
}
@@ -87,7 +87,7 @@ template <>
string SparseTensorColumn<string>::Feature(int64 batch, int64 n) const {
const int64 start = feature_start_indices_[batch];
if (DT_STRING == values_.dtype())
- return values_.vec<string>().data()[start + n];
+ return values_.vec<tstring>().data()[start + n];
return std::to_string(values_.vec<int64>().data()[start + n]);
}
@@ -95,7 +95,7 @@ template <>
StringPiece SparseTensorColumn<StringPiece>::Feature(int64 batch,
int64 n) const {
const int64 start = feature_start_indices_[batch];
- return values_.vec<string>().data()[start + n];
+ return values_.vec<tstring>().data()[start + n];
}
// A column that is backed by a dense tensor.
@@ -118,21 +118,21 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
template <>
int64 DenseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
if (DT_STRING == tensor_.dtype())
- return Fingerprint64(tensor_.matrix<string>()(batch, n));
+ return Fingerprint64(tensor_.matrix<tstring>()(batch, n));
return tensor_.matrix<int64>()(batch, n);
}
// Internal type is string or StringPiece when using StringCrosser.
template <>
string DenseTensorColumn<string>::Feature(int64 batch, int64 n) const {
- if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
+ if (DT_STRING == tensor_.dtype()) return tensor_.matrix<tstring>()(batch, n);
return std::to_string(tensor_.matrix<int64>()(batch, n));
}
template <>
StringPiece DenseTensorColumn<StringPiece>::Feature(int64 batch,
int64 n) const {
- return tensor_.matrix<string>()(batch, n);
+ return tensor_.matrix<tstring>()(batch, n);
}
// Updates Output tensors with sparse crosses.
diff --git a/tensorflow/contrib/libsvm/kernels/decode_libsvm_op.cc b/tensorflow/contrib/libsvm/kernels/decode_libsvm_op.cc
index 720c74e3de5..f35453f267e 100644
--- a/tensorflow/contrib/libsvm/kernels/decode_libsvm_op.cc
+++ b/tensorflow/contrib/libsvm/kernels/decode_libsvm_op.cc
@@ -36,7 +36,7 @@ class DecodeLibsvmOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* label_tensor;
OP_REQUIRES_OK(
diff --git a/tensorflow/contrib/session_bundle/session_bundle.cc b/tensorflow/contrib/session_bundle/session_bundle.cc
index a690d9b129a..996e4ce0b80 100644
--- a/tensorflow/contrib/session_bundle/session_bundle.cc
+++ b/tensorflow/contrib/session_bundle/session_bundle.cc
@@ -72,7 +72,7 @@ Status GetMetaGraphDefFromExport(const StringPiece export_dir,
// Creates a string tensor.
Tensor CreateStringTensor(const string& value) {
Tensor tensor(DT_STRING, TensorShape({}));
- tensor.scalar<string>()() = value;
+ tensor.scalar<tstring>()() = value;
return tensor;
}
diff --git a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
index 94650fe108b..5f997c2fba0 100644
--- a/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/model_ops.cc
@@ -52,7 +52,7 @@ class CreateTreeVariableOp : public OpKernel {
auto* result = new DecisionTreeResource(param_proto_);
if (!ParseProtoUnlimited(result->mutable_decision_tree(),
- tree_config_t->scalar<string>()())) {
+ tree_config_t->scalar<tstring>()())) {
result->Unref();
OP_REQUIRES(context, false,
errors::InvalidArgument("Unable to parse tree config."));
@@ -85,7 +85,7 @@ class TreeSerializeOp : public OpKernel {
Tensor* output_config_t = nullptr;
OP_REQUIRES_OK(
context, context->allocate_output(0, TensorShape(), &output_config_t));
- output_config_t->scalar<string>()() =
+ output_config_t->scalar<tstring>()() =
decision_tree_resource->decision_tree().SerializeAsString();
}
};
@@ -116,7 +116,7 @@ class TreeDeserializeOp : public OpKernel {
decision_trees::Model* config =
decision_tree_resource->mutable_decision_tree();
OP_REQUIRES(context,
- ParseProtoUnlimited(config, tree_config_t->scalar<string>()()),
+ ParseProtoUnlimited(config, tree_config_t->scalar<tstring>()()),
errors::InvalidArgument("Unable to parse tree config."));
decision_tree_resource->MaybeInitialize();
}
@@ -224,7 +224,7 @@ class TreePredictionsV4Op : public OpKernel {
: 0);
OP_REQUIRES_OK(context, context->allocate_output(1, output_paths_shape,
&output_tree_paths));
- auto out_paths = output_tree_paths->unaligned_flat<string>();
+ auto out_paths = output_tree_paths->unaligned_flat<tstring>();
// TODO(gilberth): If this slows down inference too much, consider having
// a filter that only serializes paths for the predicted label that we're
diff --git a/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc b/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc
index b21a9179777..fcea240dee9 100644
--- a/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc
@@ -38,7 +38,7 @@ float Convert(const string& in) {
void Evaluate(const Tensor& input_data, Tensor output_data, int32 start,
int32 end) {
auto out_data = output_data.unaligned_flat<float>();
- const auto in_data = input_data.unaligned_flat<string>();
+ const auto in_data = input_data.unaligned_flat<tstring>();
for (int32 i = start; i < end; ++i) {
out_data(i) = Convert(in_data(i));
diff --git a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
index ede6e1abc9f..e4693cf68dc 100644
--- a/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/stats_ops.cc
@@ -56,7 +56,7 @@ class CreateFertileStatsVariableOp : public OpKernel {
errors::InvalidArgument("Stats config must be a scalar."));
auto* result = new FertileStatsResource(param_proto_);
FertileStats stats;
- if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()())) {
+ if (!ParseProtoUnlimited(&stats, stats_config_t->scalar<tstring>()())) {
result->Unref();
OP_REQUIRES(context, false,
errors::InvalidArgument("Unable to parse stats config."));
@@ -98,7 +98,7 @@ class FertileStatsSerializeOp : public OpKernel {
FertileStats stats;
fertile_stats_resource->PackToProto(&stats);
- output_config_t->scalar<string>()() = stats.SerializeAsString();
+ output_config_t->scalar<tstring>()() = stats.SerializeAsString();
}
private:
@@ -128,9 +128,10 @@ class FertileStatsDeserializeOp : public OpKernel {
// Deallocate all the previous objects on the resource.
fertile_stats_resource->Reset();
FertileStats stats;
- OP_REQUIRES(context,
- ParseProtoUnlimited(&stats, stats_config_t->scalar<string>()()),
- errors::InvalidArgument("Unable to parse stats config."));
+ OP_REQUIRES(
+ context,
+ ParseProtoUnlimited(&stats, stats_config_t->scalar<tstring>()()),
+ errors::InvalidArgument("Unable to parse stats config."));
fertile_stats_resource->ExtractFromProto(stats);
fertile_stats_resource->MaybeInitialize();
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
index f4a7058ddb8..417cb6f7420 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc
@@ -103,7 +103,7 @@ float CandidateGraphRunner::SplitScore() {
void CandidateGraphRunner::GetSplit(decision_trees::BinaryNode* node) {
std::vector<Tensor> outputs;
RunOp(kNoOp, TensorNameValueList(), {kGetSplitName}, &outputs);
- ParseProtoUnlimited(node, outputs[0].unaligned_flat<string>()(0));
+ ParseProtoUnlimited(node, outputs[0].unaligned_flat<tstring>()(0));
const auto& oblique = split_.inequality_left_child_test().oblique();
auto* new_split =
node->mutable_inequality_left_child_test()->mutable_oblique();
diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc
index 8da13aaca22..b073d1ae568 100644
--- a/tensorflow/core/common_runtime/direct_session_test.cc
+++ b/tensorflow/core/common_runtime/direct_session_test.cc
@@ -1055,9 +1055,9 @@ class SessionMetadataReaderOp : public OpKernel {
OP_REQUIRES_OK(ctx,
ctx->allocate_output("y", TensorShape({}), &out_tensor));
if (ctx->session_metadata() != nullptr) {
- out_tensor->scalar<string>()() = ctx->session_metadata()->DebugString();
+ out_tensor->scalar<tstring>()() = ctx->session_metadata()->DebugString();
} else {
- out_tensor->scalar<string>()() = "";
+ out_tensor->scalar<tstring>()() = "";
}
}
};
@@ -1079,7 +1079,7 @@ TEST(DirectSessionTest, SessionMetadataAbsent) {
run_opts.set_inter_op_thread_pool(-1);
auto s = sess->Run(run_opts, {}, {y->name() + ":0"}, {}, &outputs, nullptr);
- EXPECT_EQ("", outputs[0].scalar<string>()());
+ EXPECT_EQ("", outputs[0].scalar<tstring>()());
}
TEST(DirectSessionTest, SessionMetadataPresent) {
@@ -1104,7 +1104,7 @@ TEST(DirectSessionTest, SessionMetadataPresent) {
SessionMetadata read_metadata;
ASSERT_TRUE(protobuf::TextFormat::ParseFromString(
- outputs[0].scalar<string>()(), &read_metadata));
+ outputs[0].scalar<tstring>()(), &read_metadata));
EXPECT_EQ("name", read_metadata.name());
EXPECT_EQ(1, read_metadata.version());
}
@@ -1468,7 +1468,7 @@ TEST(DirectSessionTest, RunHandleTest) {
const ResourceHandle& resource_handle = outputs[0].scalar<ResourceHandle>()();
Tensor string_handle(DT_STRING, {});
- string_handle.flat<string>().setConstant(resource_handle.name());
+ string_handle.flat<tstring>().setConstant(resource_handle.name());
// Second run call: Use a handle.
std::vector<Tensor> outputs1;
@@ -1521,7 +1521,7 @@ TEST(DirectSessionTest, RunHandleTest_Callable) {
const ResourceHandle& resource_handle = outputs[0].scalar<ResourceHandle>()();
Tensor string_handle(DT_STRING, {});
- string_handle.flat<string>().setConstant(resource_handle.name());
+ string_handle.flat<tstring>().setConstant(resource_handle.name());
// Second run call: Use a handle.
std::vector<Tensor> outputs1;
diff --git a/tensorflow/core/common_runtime/function_testlib.cc b/tensorflow/core/common_runtime/function_testlib.cc
index 1720ee64c07..bbaa94d6143 100644
--- a/tensorflow/core/common_runtime/function_testlib.cc
+++ b/tensorflow/core/common_runtime/function_testlib.cc
@@ -33,7 +33,7 @@ class FindDeviceOpKernel : public OpKernel {
Tensor* device_tensor = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output("device_name", TensorShape{},
&device_tensor));
- device_tensor->scalar<string>()() =
+ device_tensor->scalar<tstring>()() =
ctx->function_library()->device()->name();
}
};
diff --git a/tensorflow/core/common_runtime/rendezvous_util_test.cc b/tensorflow/core/common_runtime/rendezvous_util_test.cc
index 093fa7921f5..cb3fc45499d 100644
--- a/tensorflow/core/common_runtime/rendezvous_util_test.cc
+++ b/tensorflow/core/common_runtime/rendezvous_util_test.cc
@@ -33,7 +33,7 @@ class RendezvousUtilTest : public ::testing::Test {
// string -> Tensor<string>
Tensor V(const string& content) {
Tensor tensor(DT_STRING, TensorShape({}));
- tensor.scalar<string>()() = content;
+ tensor.scalar<tstring>()() = content;
return tensor;
}
@@ -41,7 +41,7 @@ Tensor V(const string& content) {
string V(const Tensor& tensor) {
CHECK_EQ(tensor.dtype(), DT_STRING);
CHECK(TensorShapeUtils::IsScalar(tensor.shape()));
- return tensor.scalar<string>()();
+ return tensor.scalar<tstring>()();
}
string MakeStringKey(const string& name) {
diff --git a/tensorflow/core/debug/debug_grpc_io_utils_test.cc b/tensorflow/core/debug/debug_grpc_io_utils_test.cc
index c857f12e755..26fd376cc6a 100644
--- a/tensorflow/core/debug/debug_grpc_io_utils_test.cc
+++ b/tensorflow/core/debug/debug_grpc_io_utils_test.cc
@@ -147,7 +147,7 @@ TEST_F(GrpcDebugTest, SendSingleDebugTensorViaGrpcTest) {
TEST_F(GrpcDebugTest, SendDebugTensorWithLargeStringAtIndex0ViaGrpcTest) {
Tensor tensor(DT_STRING, TensorShape({1, 1}));
- tensor.flat<string>()(0) = string(5000 * 1024, 'A');
+ tensor.flat<tstring>()(0) = string(5000 * 1024, 'A');
const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
"foo_tensor", 0, "DebugIdentity");
const Status status = DebugIO::PublishDebugTensor(
@@ -162,8 +162,8 @@ TEST_F(GrpcDebugTest, SendDebugTensorWithLargeStringAtIndex0ViaGrpcTest) {
TEST_F(GrpcDebugTest, SendDebugTensorWithLargeStringAtIndex1ViaGrpcTest) {
Tensor tensor(DT_STRING, TensorShape({1, 2}));
- tensor.flat<string>()(0) = "A";
- tensor.flat<string>()(1) = string(5000 * 1024, 'A');
+ tensor.flat<tstring>()(0) = "A";
+ tensor.flat<tstring>()(1) = string(5000 * 1024, 'A');
const DebugNodeKey kDebugNodeKey("/job:localhost/replica:0/task:0/cpu:0",
"foo_tensor", 0, "DebugIdentity");
const Status status = DebugIO::PublishDebugTensor(
diff --git a/tensorflow/core/debug/debug_io_utils_test.cc b/tensorflow/core/debug/debug_io_utils_test.cc
index 928a82b0611..3eebcb3f138 100644
--- a/tensorflow/core/debug/debug_io_utils_test.cc
+++ b/tensorflow/core/debug/debug_io_utils_test.cc
@@ -47,8 +47,8 @@ class DebugIOUtilsTest : public ::testing::Test {
tensor_a_->flat<float>()(3) = 0.0;
tensor_b_.reset(new Tensor(DT_STRING, TensorShape{2}));
- tensor_b_->flat<string>()(0) = "corge";
- tensor_b_->flat<string>()(1) = "garply";
+ tensor_b_->flat<tstring>()(0) = "corge";
+ tensor_b_->flat<tstring>()(1) = "garply";
}
Env* env_;
@@ -182,8 +182,8 @@ TEST_F(DebugIOUtilsTest, DumpStringTensorToFileSunnyDay) {
// Verify tensor shape and value.
ASSERT_EQ(tensor_b_->shape(), b_prime.shape());
- for (int i = 0; i < b_prime.flat<string>().size(); ++i) {
- ASSERT_EQ(tensor_b_->flat<string>()(i), b_prime.flat<string>()(i));
+ for (int i = 0; i < b_prime.flat<tstring>().size(); ++i) {
+ ASSERT_EQ(tensor_b_->flat<tstring>()(i), b_prime.flat<tstring>()(i));
}
// Tear down temporary file and directories.
diff --git a/tensorflow/core/debug/grpc_session_debug_test.cc b/tensorflow/core/debug/grpc_session_debug_test.cc
index 642a2a4c07d..65ec1ef8a6d 100644
--- a/tensorflow/core/debug/grpc_session_debug_test.cc
+++ b/tensorflow/core/debug/grpc_session_debug_test.cc
@@ -231,7 +231,7 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
Graph graph(OpRegistry::Global());
Tensor a_tensor(DT_STRING, TensorShape({2, 2}));
for (size_t i = 0; i < 4; ++i) {
- a_tensor.flat<string>()(i) = "hello, world";
+ a_tensor.flat<tstring>()(i) = "hello, world";
}
Node* a = test::graph::Constant(&graph, a_tensor);
Node* b = test::graph::Identity(&graph, a);
@@ -266,7 +266,7 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
ASSERT_EQ(outputs[0].dtype(), DT_STRING);
ASSERT_EQ(outputs[0].NumElements(), 4);
for (size_t i = 0; i < outputs[0].NumElements(); ++i) {
- EXPECT_EQ(outputs[0].flat<string>()(i), "hello, world");
+ EXPECT_EQ(outputs[0].flat<tstring>()(i), "hello, world");
}
TF_CHECK_OK(session->Close());
@@ -278,7 +278,7 @@ TEST_F(GrpcSessionDebugTest, MultiDevices_String) {
ASSERT_EQ(1, dumped_tensors.size());
ASSERT_EQ(TensorShape({2, 2}), dumped_tensors[0].shape());
for (size_t i = 0; i < 4; ++i) {
- ASSERT_EQ("hello, world", dumped_tensors[0].flat<string>()(i));
+ ASSERT_EQ("hello, world", dumped_tensors[0].flat<tstring>()(i));
}
DeleteDumpDir();
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_rpc_factory.cc b/tensorflow/core/distributed_runtime/rpc/grpc_rpc_factory.cc
index 3635caf3d10..8be6f1d6994 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_rpc_factory.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_rpc_factory.cc
@@ -187,8 +187,8 @@ void GrpcRPCFactory::CreateCall(const Tensor& request_t, const bool try_rpc,
void GrpcRPCFactory::StartCall(const Tensor& address_t, const Tensor& method_t,
GrpcCall* call) {
- auto address = address_t.flat<string>();
- auto method = method_t.flat<string>();
+ auto address = address_t.flat<tstring>();
+ auto method = method_t.flat<tstring>();
// Stubs are maintained by the GrpcRPCFactory class and will be
// deleted when the class is destroyed.
::grpc::GenericStub* singleton_stub = nullptr;
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc b/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
index c38b89b9c6f..7f2906efca6 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_session_test.cc
@@ -501,7 +501,7 @@ TEST(GrpcSessionTest, MultiDevices_String) {
Graph graph(OpRegistry::Global());
Tensor a_tensor(DT_STRING, TensorShape({2, 2}));
for (int i = 0; i < 4; ++i) {
- a_tensor.flat<string>()(i) = "hello, world";
+ a_tensor.flat<tstring>()(i) = "hello, world";
}
Node* a = test::graph::Constant(&graph, a_tensor);
Node* b = test::graph::Identity(&graph, a);
@@ -525,7 +525,7 @@ TEST(GrpcSessionTest, MultiDevices_String) {
ASSERT_EQ(outputs[0].dtype(), DT_STRING);
ASSERT_EQ(outputs[0].NumElements(), 4);
for (int i = 0; i < outputs[0].NumElements(); ++i) {
- EXPECT_EQ(outputs[0].flat<string>()(i), "hello, world");
+ EXPECT_EQ(outputs[0].flat<tstring>()(i), "hello, world");
}
TF_CHECK_OK(session->Close());
} else {
diff --git a/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr_test.cc b/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr_test.cc
index 1483a65e3d9..5021853ce23 100644
--- a/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr_test.cc
+++ b/tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr_test.cc
@@ -30,7 +30,7 @@ namespace tensorflow {
// string -> Tensor<string>
Tensor V(const string& content) {
Tensor tensor(DT_STRING, TensorShape({}));
- tensor.scalar<string>()() = content;
+ tensor.scalar<tstring>()() = content;
return tensor;
}
@@ -38,7 +38,7 @@ Tensor V(const string& content) {
string V(const Tensor& tensor) {
CHECK_EQ(tensor.dtype(), DT_STRING);
CHECK(TensorShapeUtils::IsScalar(tensor.shape()));
- return tensor.scalar<string>()();
+ return tensor.scalar<tstring>()();
}
Rendezvous::ParsedKey MakeKey(const string& s) {
diff --git a/tensorflow/core/example/example_parser_configuration.cc b/tensorflow/core/example/example_parser_configuration.cc
index 5660465c51a..af06c07eac9 100644
--- a/tensorflow/core/example/example_parser_configuration.cc
+++ b/tensorflow/core/example/example_parser_configuration.cc
@@ -114,13 +114,14 @@ Status ExtractExampleParserConfiguration(
for (int i = 0; i < num_sparse; ++i) {
int input_idx = sparse_keys_start + i;
- (*var_len_features)[i].key = op_input_tensors[input_idx].scalar<string>()();
+ (*var_len_features)[i].key =
+ op_input_tensors[input_idx].scalar<tstring>()();
}
for (int i = 0; i < num_dense; ++i) {
FixedLenFeature& config = (*fixed_len_features)[i];
int dense_keys_offset = dense_keys_start + i;
- config.key = op_input_tensors[dense_keys_offset].scalar<string>()();
+ config.key = op_input_tensors[dense_keys_offset].scalar<tstring>()();
int defaults_offset = dense_defaults_start + i;
config.default_value = op_input_tensors[defaults_offset];
diff --git a/tensorflow/core/framework/op_compatibility_test.cc b/tensorflow/core/framework/op_compatibility_test.cc
index dc931c38cd5..4edb60786d7 100644
--- a/tensorflow/core/framework/op_compatibility_test.cc
+++ b/tensorflow/core/framework/op_compatibility_test.cc
@@ -35,7 +35,7 @@ class TestKernel : public OpKernel {
Tensor* out_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("ndef", TensorShape({}),
&out_tensor));
- out_tensor->scalar<string>()() = SummarizeNodeDef(def());
+ out_tensor->scalar<tstring>()() = SummarizeNodeDef(def());
}
};
@@ -87,7 +87,7 @@ class OpCompatibilityTest : public OpsTestBase {
TF_ASSERT_OK(RunOpKernel());
}
- string Result() { return GetOutput(0)->scalar<string>()(); }
+ string Result() { return GetOutput(0)->scalar<tstring>()(); }
void ExpectIncompatible(const OpDef& old_op_def, const OpDef& new_op_def,
const string& error) {
diff --git a/tensorflow/core/framework/reader_base.cc b/tensorflow/core/framework/reader_base.cc
index 39d83d9633b..ec27b8b89cb 100644
--- a/tensorflow/core/framework/reader_base.cc
+++ b/tensorflow/core/framework/reader_base.cc
@@ -214,7 +214,7 @@ string ReaderBase::GetNextWorkLocked(QueueInterface* queue,
context->SetStatus(errors::InvalidArgument(
"Expected to dequeue a one-element string tensor"));
} else {
- work = tuple[0].flat<string>()(0);
+ work = tuple[0].flat<tstring>()(0);
}
}
n.Notify();
diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc
index 1c392fc8323..da9a1fbbe89 100644
--- a/tensorflow/core/framework/rendezvous_test.cc
+++ b/tensorflow/core/framework/rendezvous_test.cc
@@ -86,7 +86,7 @@ class LocalRendezvousTest : public ::testing::Test {
// string -> Tensor<string>
Tensor V(const string& content) {
Tensor tensor(DT_STRING, TensorShape({}));
- tensor.scalar<string>()() = content;
+ tensor.scalar<tstring>()() = content;
return tensor;
}
@@ -94,7 +94,7 @@ Tensor V(const string& content) {
string V(const Tensor& tensor) {
CHECK_EQ(tensor.dtype(), DT_STRING);
CHECK(TensorShapeUtils::IsScalar(tensor.shape()));
- return tensor.scalar<string>()();
+ return tensor.scalar<tstring>()();
}
Rendezvous::ParsedKey MakeKey(const string& name) {
diff --git a/tensorflow/core/framework/resource_mgr.h b/tensorflow/core/framework/resource_mgr.h
index 301fe686df1..67ea803511c 100644
--- a/tensorflow/core/framework/resource_mgr.h
+++ b/tensorflow/core/framework/resource_mgr.h
@@ -639,8 +639,8 @@ Status GetResourceFromContext(OpKernelContext* ctx, const string& input_name,
"Resource handle must have 2 elements, but had shape: ",
tensor.shape().DebugString());
}
- container = tensor.flat<string>()(0);
- shared_name = tensor.flat<string>()(1);
+ container = tensor.flat<tstring>()(0);
+ shared_name = tensor.flat<tstring>()(1);
}
return ctx->resource_manager()->Lookup(container, shared_name, resource);
}
diff --git a/tensorflow/core/framework/resource_op_kernel.h b/tensorflow/core/framework/resource_op_kernel.h
index fbcd439dea3..60e9703190c 100644
--- a/tensorflow/core/framework/resource_op_kernel.h
+++ b/tensorflow/core/framework/resource_op_kernel.h
@@ -96,7 +96,7 @@ class ResourceOpKernel : public OpKernel {
}
if (!has_resource_type_) {
- auto h = handle_.AccessTensor(context)->template flat<string>();
+ auto h = handle_.AccessTensor(context)->template flat<tstring>();
h(0) = cinfo_.container();
h(1) = cinfo_.name();
}
diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc
index d4aed387610..dd4ca706f28 100644
--- a/tensorflow/core/framework/tensor_test.cc
+++ b/tensorflow/core/framework/tensor_test.cc
@@ -480,7 +480,7 @@ TEST_F(TensorReshapeTest, ReshapeError) {
Tensor string_tensor{DT_STRING, {10}};
// Note that the error message compare # of elements, not # of bytes.
- EXPECT_DEATH((string_tensor.bit_casted_shaped<string, 1>({9})), "9 vs. 10");
+ EXPECT_DEATH((string_tensor.bit_casted_shaped<tstring, 1>({9})), "9 vs. 10");
}
TEST_F(TensorReshapeTest, Flat) {
@@ -795,27 +795,27 @@ TEST(Tensor_Scalar, Basics) {
{
Tensor t(DT_STRING, TensorShape({}));
EXPECT_EQ(1, t.NumElements());
- auto Tt = t.scalar<string>();
+ auto Tt = t.scalar<tstring>();
EXPECT_EQ(1, Tt.size());
EXPECT_EQ(0, Tt.rank());
- t.scalar<string>()() = "foo";
+ t.scalar<tstring>()() = "foo";
EXPECT_EQ("foo", Tt());
}
{
Tensor t(DT_STRING, TensorShape({1}));
EXPECT_EQ(1, t.NumElements());
- auto Tt = t.vec<string>();
+ auto Tt = t.vec<tstring>();
EXPECT_EQ(1, Tt.size());
- t.flat<string>()(0) = "foo";
+ t.flat<tstring>()(0) = "foo";
EXPECT_EQ("foo", Tt(0));
}
{
Tensor t(DT_STRING, TensorShape({1, 1, 1}));
EXPECT_EQ(1, t.NumElements());
- auto Tt = t.scalar<string>();
+ auto Tt = t.scalar<tstring>();
EXPECT_EQ(1, Tt.size());
EXPECT_EQ(0, Tt.rank());
- t.flat<string>()(0) = "bar";
+ t.flat<tstring>()(0) = "bar";
EXPECT_EQ("bar", Tt());
}
{
@@ -860,7 +860,7 @@ TEST(Tensor_HostScalar, Basics) {
Tensor t("fooooooooooooooooooooooooooooooooooooo");
EXPECT_EQ(DT_STRING, t.dtype());
EXPECT_EQ(1, t.NumElements());
- auto Tt = t.scalar<string>();
+ auto Tt = t.scalar<tstring>();
EXPECT_EQ(1, Tt.size());
EXPECT_EQ(0, Tt.rank());
EXPECT_EQ("fooooooooooooooooooooooooooooooooooooo", Tt());
@@ -980,7 +980,7 @@ TEST(Tensor_String, SimpleWithHelper) {
Tensor t2(DT_STRING, {2, 3});
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
- t2.matrix<string>()(i, j) = strings::StrCat(i * 3 + j);
+ t2.matrix<tstring>()(i, j) = strings::StrCat(i * 3 + j);
}
}
@@ -1163,7 +1163,7 @@ TEST(Tensor, FailureToAllocate) {
// String
{
Tensor t(DT_STRING, TensorShape({1}));
- t.vec<string>()(0) = "foo";
+ t.vec<tstring>()(0) = "foo";
TensorProto proto;
t.AsProtoField(&proto);
diff --git a/tensorflow/core/framework/tensor_util.cc b/tensorflow/core/framework/tensor_util.cc
index c87cc9548da..2e99626cb94 100644
--- a/tensorflow/core/framework/tensor_util.cc
+++ b/tensorflow/core/framework/tensor_util.cc
@@ -48,7 +48,7 @@ void DeepCopy(const Tensor& input, Tensor* output) {
input_data.size());
}
} else if (input.dtype() == DT_STRING) {
- output->unaligned_flat<string>() = input.unaligned_flat<string>();
+ output->unaligned_flat<tstring>() = input.unaligned_flat<tstring>();
} else {
CHECK_EQ(DT_VARIANT, input.dtype());
output->unaligned_flat<Variant>() = input.unaligned_flat<Variant>();
@@ -103,7 +103,7 @@ Status Concat(const gtl::ArraySlice<Tensor>& tensors, Tensor* result) {
int64 offset = 0;
for (const Tensor& tensor : tensors) {
- auto from_strings = tensor.flat<string>();
+ auto from_strings = tensor.flat<tstring>();
CHECK_LE(offset + tensor.NumElements(), result->NumElements());
for (int i = 0; i < tensor.NumElements(); ++i) {
to_strings[offset + i] = from_strings(i);
@@ -155,7 +155,7 @@ Status Split(const Tensor& tensor, const gtl::ArraySlice<int64>& sizes,
if (tensor.dtype() != DT_STRING) {
return errors::Internal("Unexpected data type");
}
- auto from_strings = tensor.flat<string>();
+ auto from_strings = tensor.flat<tstring>();
int64 offset = 0;
for (int64 size : sizes) {
diff --git a/tensorflow/core/framework/tensor_util_test.cc b/tensorflow/core/framework/tensor_util_test.cc
index 44708765bbf..fe988015e27 100644
--- a/tensorflow/core/framework/tensor_util_test.cc
+++ b/tensorflow/core/framework/tensor_util_test.cc
@@ -111,12 +111,12 @@ TEST(TensorUtil, DeepCopy) {
// Test string deep copy
Tensor str1(DT_STRING, TensorShape({2}));
- str1.flat<string>()(0) = "foo1";
- str1.flat<string>()(1) = "foo2";
+ str1.flat<tstring>()(0) = "foo1";
+ str1.flat<tstring>()(1) = "foo2";
Tensor str2 = tensor::DeepCopy(str1);
- str2.flat<string>()(0) = "bar1";
- str2.flat<string>()(1) = "bar2";
- EXPECT_NE(str2.flat<string>()(0), str1.flat<string>()(0));
+ str2.flat<tstring>()(0) = "bar1";
+ str2.flat<tstring>()(1) = "bar2";
+ EXPECT_NE(str2.flat<tstring>()(0), str1.flat<tstring>()(0));
}
TEST(TensorUtil, DeepCopySlice) {
@@ -151,7 +151,7 @@ TEST(TensorUtil, DeepCopySlice) {
TEST(TensorUtil, DeepCopySliceString) {
Tensor x(DT_STRING, TensorShape({10}));
- x.flat<string>().setConstant("hello");
+ x.flat<tstring>().setConstant("hello");
// Slice 'x' -- y still refers to the same buffer.
Tensor y = x.Slice(3, 7);
@@ -160,7 +160,7 @@ TEST(TensorUtil, DeepCopySliceString) {
Tensor z = tensor::DeepCopy(y);
// Set x to be different.
- x.flat<string>().setConstant("goodbye");
+ x.flat<tstring>().setConstant("goodbye");
EXPECT_EQ(TensorShape({10}), x.shape());
EXPECT_EQ(TensorShape({4}), y.shape());
@@ -171,11 +171,11 @@ TEST(TensorUtil, DeepCopySliceString) {
// x and y should now all be 'goodbye', but z should be 'hello'.
for (int i = 0; i < 10; ++i) {
- EXPECT_EQ("goodbye", x.flat<string>()(i));
+ EXPECT_EQ("goodbye", x.flat<tstring>()(i));
}
for (int i = 0; i < 4; ++i) {
- EXPECT_EQ("goodbye", y.unaligned_flat<string>()(i));
- EXPECT_EQ("hello", z.flat<string>()(i));
+ EXPECT_EQ("goodbye", y.unaligned_flat<tstring>()(i));
+ EXPECT_EQ("hello", z.flat<tstring>()(i));
}
}
@@ -202,11 +202,12 @@ TEST(TensorUtil, DeepCopySliceVariant) {
// Each element of x and y should now be a DT_STRING Tensor containing "foo",
// but each element of z should be a DT_FLOAT tensor containing 42.0.
for (int i = 0; i < 10; ++i) {
- EXPECT_EQ("foo", x.flat<Variant>()(i).get<Tensor>()->scalar<string>()());
+ EXPECT_EQ("foo", x.flat<Variant>()(i).get<Tensor>()->scalar<tstring>()());
}
for (int i = 0; i < 4; ++i) {
- EXPECT_EQ("foo",
- y.unaligned_flat<Variant>()(i).get<Tensor>()->scalar<string>()());
+ EXPECT_EQ(
+ "foo",
+ y.unaligned_flat<Variant>()(i).get<Tensor>()->scalar<tstring>()());
EXPECT_EQ(42.0, z.flat<Variant>()(i).get<Tensor>()->scalar<float>()());
}
}
@@ -271,7 +272,7 @@ TEST(TensorUtil, Split) {
TEST(TensorUtil, ConcatSplitStrings) {
Tensor x(DT_STRING, TensorShape({4, 3}));
for (int i = 0; i < 4 * 3; ++i) {
- x.flat<string>()(i) = strings::StrCat("foo_", i);
+ x.flat<tstring>()(i) = strings::StrCat("foo_", i);
}
std::vector<Tensor> split;
@@ -280,15 +281,15 @@ TEST(TensorUtil, ConcatSplitStrings) {
TF_ASSERT_OK(tensor::Concat(split, &x_round_tripped));
ASSERT_EQ(x.shape(), x_round_tripped.shape());
for (int i = 0; i < 4 * 3; ++i) {
- EXPECT_EQ(x.flat<string>()(i), x_round_tripped.flat<string>()(i));
+ EXPECT_EQ(x.flat<tstring>()(i), x_round_tripped.flat<tstring>()(i));
}
// Ensure that no memory is being shared between 'x' and 'x_round_tripped'.
for (int i = 0; i < 4 * 3; ++i) {
- x_round_tripped.flat<string>()(i) = strings::StrCat("bar_", i);
+ x_round_tripped.flat<tstring>()(i) = strings::StrCat("bar_", i);
}
for (int i = 0; i < 4 * 3; ++i) {
- EXPECT_NE(x.flat<string>()(i), x_round_tripped.flat<string>()(i));
+ EXPECT_NE(x.flat<tstring>()(i), x_round_tripped.flat<tstring>()(i));
}
}
diff --git a/tensorflow/core/framework/variant_op_copy_test.cc b/tensorflow/core/framework/variant_op_copy_test.cc
index 25cddc00a3a..19226d232ae 100644
--- a/tensorflow/core/framework/variant_op_copy_test.cc
+++ b/tensorflow/core/framework/variant_op_copy_test.cc
@@ -244,7 +244,7 @@ TEST(VariantOpCopyTest, CreateConstOnGPUFailsGracefully) {
// Create the input StoredTensorValue and serialize it.
StoredTensorValue from;
from.stored = Tensor(DT_STRING, TensorShape({}));
- from.stored.scalar<string>()() = "hi";
+ from.stored.scalar<tstring>()() = "hi";
VariantTensorData data;
data.set_type_name(from.TypeName());
from.Encode(&data);
@@ -292,7 +292,7 @@ TEST(VariantOpCopyTest, CreateCopyCPUToCPU) {
TEST(VariantOpCopyTest, CreateCopyCPUToCPUString) {
Scope root = Scope::NewRootScope().WithDevice("/cpu:0");
Tensor t_str(DT_STRING, TensorShape({}));
- t_str.scalar<string>()() = "hi";
+ t_str.scalar<tstring>()() = "hi";
Output create_op = CreateTestVariant(root, t_str);
Output identity = ops::Identity(root, create_op);
@@ -309,7 +309,7 @@ TEST(VariantOpCopyTest, CreateCopyCPUToCPUString) {
EXPECT_EQ("StoredTensorValue", r1.TypeName());
const StoredTensorValue* v1 = r1.get<StoredTensorValue>();
EXPECT_NE(v1, nullptr);
- EXPECT_EQ("hi", v1->stored.scalar<string>()());
+ EXPECT_EQ("hi", v1->stored.scalar<tstring>()());
}
}
@@ -356,7 +356,7 @@ TEST(VariantOpCopyTest, CreateCopyCPUToGPUStringFailsSafely) {
Scope root = Scope::NewRootScope().WithDevice("/cpu:0");
Scope with_gpu = root.WithDevice("/gpu:0");
Tensor t_str(DT_STRING, TensorShape({}));
- t_str.scalar<string>()() = "hi";
+ t_str.scalar<tstring>()() = "hi";
Output create_op = CreateTestVariant(root, t_str);
Output identity = ops::Identity(with_gpu, create_op);
diff --git a/tensorflow/core/graph/quantize_training.cc b/tensorflow/core/graph/quantize_training.cc
index 26bb6543569..4670e7a543c 100644
--- a/tensorflow/core/graph/quantize_training.cc
+++ b/tensorflow/core/graph/quantize_training.cc
@@ -172,8 +172,8 @@ StringPiece GetNodeNamePrefix(const Node* node) {
}
void FillStringTensor(Tensor* dst, const Tensor& src) {
- auto dst_flat = dst->flat<string>();
- auto src_flat = src.flat<string>();
+ auto dst_flat = dst->flat<tstring>();
+ auto src_flat = src.flat<tstring>();
for (int i = 0; i < src.NumElements(); i++) {
dst_flat(i) = src_flat(i);
}
@@ -220,8 +220,8 @@ Status ConnectVariablesToSaveOp(Graph* graph, Node* save_op,
FillStringTensor(&new_shape_and_slices, shape_and_slices);
for (int i = 0; i < var_size; i++) {
Node* var = added_variables[i];
- new_tensor_names.flat<string>()(tn_size + i) = var->name();
- new_shape_and_slices.flat<string>()(tn_size + i) = "";
+ new_tensor_names.flat<tstring>()(tn_size + i) = var->name();
+ new_shape_and_slices.flat<tstring>()(tn_size + i) = "";
var_nodeouts.emplace_back(var);
}
save_op_builder = save_op_builder.Input(var_nodeouts);
@@ -275,7 +275,7 @@ Status AddRestoreVariableSubgraphs(Graph* graph, Node* save_op,
// Construct the tensor_names input with the variable name.
Node* tensor_names;
Tensor tensor_names_val(DT_STRING, TensorShape({1}));
- tensor_names_val.flat<string>()(0) = var->name();
+ tensor_names_val.flat<tstring>()(0) = var->name();
TF_RETURN_IF_ERROR(NodeBuilder(tensor_names_op_name, "Const")
.Attr("dtype", DT_STRING)
.Attr("value", tensor_names_val)
@@ -284,7 +284,7 @@ Status AddRestoreVariableSubgraphs(Graph* graph, Node* save_op,
// Construct the shape_and_slices input with empty string.
Node* shape_and_slices;
Tensor shape_and_slices_val(DT_STRING, TensorShape({1}));
- shape_and_slices_val.flat<string>()(0) = "";
+ shape_and_slices_val.flat<tstring>()(0) = "";
TF_RETURN_IF_ERROR(NodeBuilder(shape_and_slices_op_name, "Const")
.Attr("dtype", DT_STRING)
.Attr("value", shape_and_slices_val)
diff --git a/tensorflow/core/grappler/costs/utils.cc b/tensorflow/core/grappler/costs/utils.cc
index d45bb14e070..198b6039b66 100644
--- a/tensorflow/core/grappler/costs/utils.cc
+++ b/tensorflow/core/grappler/costs/utils.cc
@@ -130,7 +130,7 @@ static void ExtractExtraProperties(
if (tensor.NumElements() != 1) {
continue;
}
- const string filename = tensor.scalar<string>()();
+ const string filename = tensor.scalar<tstring>()();
Env* env = Env::Default();
FileStatistics stat;
diff --git a/tensorflow/core/grappler/graph_view_test.cc b/tensorflow/core/grappler/graph_view_test.cc
index 5b3e140f23d..7be98dc43b4 100644
--- a/tensorflow/core/grappler/graph_view_test.cc
+++ b/tensorflow/core/grappler/graph_view_test.cc
@@ -98,7 +98,7 @@ TEST_F(GraphViewTest, OpPortIdToArgIdSparseSplit) {
TEST_F(GraphViewTest, ParseSingleExample) {
tensorflow::Scope s = tensorflow::Scope::NewRootScope();
- Output a = ops::Const<string>(s.WithOpName("a"), "", {});
+ Output a = ops::Const<tstring>(s.WithOpName("a"), "", {});
Output b = ops::Const<int64>(s.WithOpName("b"), 1, {1, 1});
ops::ParseSingleExample c(s.WithOpName("c"), a, {b, b}, 2, {"w", "x"},
{"y", "z"}, {DT_INT64, DT_INT64}, {{1}, {1}});
diff --git a/tensorflow/core/kernels/as_string_op.cc b/tensorflow/core/kernels/as_string_op.cc
index e6d6c40f760..8341909fbc8 100644
--- a/tensorflow/core/kernels/as_string_op.cc
+++ b/tensorflow/core/kernels/as_string_op.cc
@@ -116,7 +116,7 @@ class AsStringOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_output("output", input_tensor->shape(),
&output_tensor));
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
#define ENCODE_TYPE(type, T, enc_str) \
case (type): { \
diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc
index 89d742c2daf..adbe370395c 100644
--- a/tensorflow/core/kernels/barrier_ops.cc
+++ b/tensorflow/core/kernels/barrier_ops.cc
@@ -308,7 +308,7 @@ class Barrier : public ResourceBase {
int component_index, int i,
std::vector<Tuple>* ready_tuples, bool* new_elements)
EXCLUSIVE_LOCKS_REQUIRED(mu_) {
- auto keys_vec = keys.flat<string>();
+ auto keys_vec = keys.flat<tstring>();
auto values_matrix = values.flat_outer_dims<T>();
PersistentTuple* element_ptr;
@@ -392,7 +392,7 @@ class Barrier : public ResourceBase {
&key, &allocated_key));
ready_tuple.push_back(*element[0].AccessTensor(ctx)); // index
ready_tuple.push_back(*allocated_key); // key
- ready_tuple[1].scalar<string>()() = keys_vec(i); // set the key
+ ready_tuple[1].scalar<tstring>()() = keys_vec(i); // set the key
for (int j = 1; j < num_components() + 1; ++j) {
ready_tuple.push_back(*element[j].AccessTensor(ctx));
}
diff --git a/tensorflow/core/kernels/base64_ops.cc b/tensorflow/core/kernels/base64_ops.cc
index 74e6b39390a..cb235f56615 100644
--- a/tensorflow/core/kernels/base64_ops.cc
+++ b/tensorflow/core/kernels/base64_ops.cc
@@ -36,8 +36,8 @@ class EncodeBase64Op : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
&output_tensor));
- auto input = input_tensor.flat<string>();
- auto output = output_tensor->flat<string>();
+ auto input = input_tensor.flat<tstring>();
+ auto output = output_tensor->flat<tstring>();
for (int64 i = 0; i < input.dimension(0); ++i) {
OP_REQUIRES_OK(context, Base64Encode(input(i), pad_, &output(i)));
@@ -61,8 +61,8 @@ class DecodeBase64Op : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
&output_tensor));
- auto input = input_tensor.flat<string>();
- auto output = output_tensor->flat<string>();
+ auto input = input_tensor.flat<tstring>();
+ auto output = output_tensor->flat<tstring>();
for (int64 i = 0; i < input.dimension(0); ++i) {
OP_REQUIRES_OK(context, Base64Decode(input(i), &output(i)));
diff --git a/tensorflow/core/kernels/boosted_trees/prediction_ops.cc b/tensorflow/core/kernels/boosted_trees/prediction_ops.cc
index 718cf8e4139..7cd62af3a95 100644
--- a/tensorflow/core/kernels/boosted_trees/prediction_ops.cc
+++ b/tensorflow/core/kernels/boosted_trees/prediction_ops.cc
@@ -324,7 +324,7 @@ class BoostedTreesExampleDebugOutputsOp : public OpKernel {
context, context->allocate_output("examples_debug_outputs_serialized",
{batch_size}, &output_debug_info_t));
// Will contain serialized protos, per example.
- auto output_debug_info = output_debug_info_t->flat<string>();
+ auto output_debug_info = output_debug_info_t->flat<tstring>();
const int32 last_tree = resource->num_trees() - 1;
// For each given example, traverse through all trees keeping track of the
diff --git a/tensorflow/core/kernels/boosted_trees/resource_ops.cc b/tensorflow/core/kernels/boosted_trees/resource_ops.cc
index 5a9c3549041..ac1fb5652da 100644
--- a/tensorflow/core/kernels/boosted_trees/resource_ops.cc
+++ b/tensorflow/core/kernels/boosted_trees/resource_ops.cc
@@ -51,7 +51,7 @@ class BoostedTreesCreateEnsembleOp : public OpKernel {
std::unique_ptr<BoostedTreesEnsembleResource> result(
new BoostedTreesEnsembleResource());
if (!result->InitFromSerialized(
- tree_ensemble_serialized_t->scalar<string>()(), stamp_token)) {
+ tree_ensemble_serialized_t->scalar<tstring>()(), stamp_token)) {
result->Unref();
OP_REQUIRES(
context, false,
@@ -152,7 +152,7 @@ class BoostedTreesSerializeEnsembleOp : public OpKernel {
Tensor* output_proto_t = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(1, TensorShape(), &output_proto_t));
- output_proto_t->scalar<string>()() =
+ output_proto_t->scalar<tstring>()() =
tree_ensemble_resource->SerializeAsString();
}
};
@@ -187,7 +187,7 @@ class BoostedTreesDeserializeEnsembleOp : public OpKernel {
OP_REQUIRES(
context,
tree_ensemble_resource->InitFromSerialized(
- tree_ensemble_serialized_t->scalar<string>()(), stamp_token),
+ tree_ensemble_serialized_t->scalar<tstring>()(), stamp_token),
errors::InvalidArgument("Unable to parse tree ensemble proto."));
}
};
diff --git a/tensorflow/core/kernels/boosted_trees/stats_ops.cc b/tensorflow/core/kernels/boosted_trees/stats_ops.cc
index de9e378e704..c421bff44ca 100644
--- a/tensorflow/core/kernels/boosted_trees/stats_ops.cc
+++ b/tensorflow/core/kernels/boosted_trees/stats_ops.cc
@@ -393,7 +393,7 @@ class BoostedTreesCalculateBestFeatureSplitOp : public OpKernel {
OP_REQUIRES_OK(
context, context->allocate_output("split_with_default_directions",
{num_nodes}, &output_split_types_t));
- auto output_split_types_vec = output_split_types_t->vec<string>();
+ auto output_split_types_vec = output_split_types_t->vec<tstring>();
// Sets output tensors from vectors.
for (int i = 0; i < num_nodes; ++i) {
@@ -677,7 +677,7 @@ class BoostedTreesSparseCalculateBestFeatureSplitOp : public OpKernel {
OP_REQUIRES_OK(
context, context->allocate_output("split_with_default_directions",
{num_nodes}, &output_split_types_t));
- auto output_split_types_vec = output_split_types_t->vec<string>();
+ auto output_split_types_vec = output_split_types_t->vec<tstring>();
// Sets output tensors from vectors.
for (int i = 0; i < num_nodes; ++i) {
diff --git a/tensorflow/core/kernels/conditional_accumulator_base_op.h b/tensorflow/core/kernels/conditional_accumulator_base_op.h
index ab54fc1d914..a2bfa2cdc8c 100644
--- a/tensorflow/core/kernels/conditional_accumulator_base_op.h
+++ b/tensorflow/core/kernels/conditional_accumulator_base_op.h
@@ -113,7 +113,7 @@ class ConditionalAccumulatorBaseOp : public OpKernel {
// Verify that the shared accumulator is compatible
// with the requested arguments.
TF_RETURN_IF_ERROR(accumulator->MatchesNodeDef(def()));
- auto h = accumulator_handle_.AccessTensor(ctx)->template flat<string>();
+ auto h = accumulator_handle_.AccessTensor(ctx)->template flat<tstring>();
h(0) = cinfo_.container();
h(1) = cinfo_.name();
accumulator_handle_set_ = true;
diff --git a/tensorflow/core/kernels/conditional_accumulator_op.cc b/tensorflow/core/kernels/conditional_accumulator_op.cc
index 2bbd0ec35fb..3c7fbe0c65a 100644
--- a/tensorflow/core/kernels/conditional_accumulator_op.cc
+++ b/tensorflow/core/kernels/conditional_accumulator_op.cc
@@ -85,7 +85,7 @@ class ResourceConditionalAccumulatorOp : public ConditionalAccumulatorBaseOp {
void SetHandleToOutput(OpKernelContext* ctx)
SHARED_LOCKS_REQUIRED(mu_) override {
- auto h = accumulator_handle_.AccessTensor(ctx)->template flat<string>();
+ auto h = accumulator_handle_.AccessTensor(ctx)->template flat<tstring>();
h(0) = cinfo_.container();
h(1) = cinfo_.name();
OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput(
diff --git a/tensorflow/core/kernels/data/dataset_ops.cc b/tensorflow/core/kernels/data/dataset_ops.cc
index 58cd17482e7..e931755d36e 100644
--- a/tensorflow/core/kernels/data/dataset_ops.cc
+++ b/tensorflow/core/kernels/data/dataset_ops.cc
@@ -40,7 +40,7 @@ void DatasetToGraphOp::Compute(OpKernelContext* ctx) {
ctx, AsGraphDef(ctx, dataset, SerializationContext({}), &graph_def));
Tensor* result;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &result));
- result->scalar<string>()() = graph_def.SerializeAsString();
+ result->scalar<tstring>()() = graph_def.SerializeAsString();
}
void DatasetCardinalityOp::Compute(OpKernelContext* ctx) {
diff --git a/tensorflow/core/kernels/data/experimental/csv_dataset_op.cc b/tensorflow/core/kernels/data/experimental/csv_dataset_op.cc
index a2d9bd8d062..682085262fb 100644
--- a/tensorflow/core/kernels/data/experimental/csv_dataset_op.cc
+++ b/tensorflow/core/kernels/data/experimental/csv_dataset_op.cc
@@ -93,7 +93,7 @@ class CSVDatasetOp : public DatasetOpKernel {
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
io::ZlibCompressionOptions zlib_compression_options =
@@ -719,10 +719,10 @@ class CSVDatasetOp : public DatasetOpKernel {
}
case DT_STRING: {
if (field.empty() || field == dataset()->na_value_) {
- component.scalar<string>()() =
- dataset()->record_defaults_[output_idx].flat<string>()(0);
+ component.scalar<tstring>()() =
+ dataset()->record_defaults_[output_idx].flat<tstring>()(0);
} else {
- component.scalar<string>()() = string(field);
+ component.scalar<tstring>()() = string(field);
}
break;
}
diff --git a/tensorflow/core/kernels/data/experimental/lmdb_dataset_op.cc b/tensorflow/core/kernels/data/experimental/lmdb_dataset_op.cc
index f587fe9e4c7..d19085fc35c 100644
--- a/tensorflow/core/kernels/data/experimental/lmdb_dataset_op.cc
+++ b/tensorflow/core/kernels/data/experimental/lmdb_dataset_op.cc
@@ -38,7 +38,7 @@ class LMDBDatasetOp : public DatasetOpKernel {
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
*output = new Dataset(ctx, filenames);
@@ -95,13 +95,13 @@ class LMDBDatasetOp : public DatasetOpKernel {
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
Tensor& key_tensor = out_tensors->back();
- key_tensor.scalar<string>()() = string(
+ key_tensor.scalar<tstring>()() = string(
static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
Tensor& value_tensor = out_tensors->back();
- value_tensor.scalar<string>()() =
+ value_tensor.scalar<tstring>()() =
string(static_cast<const char*>(mdb_value_.mv_data),
mdb_value_.mv_size);
diff --git a/tensorflow/core/kernels/data/experimental/matching_files_dataset_op.cc b/tensorflow/core/kernels/data/experimental/matching_files_dataset_op.cc
index fd4beb03e57..0ae425556bc 100644
--- a/tensorflow/core/kernels/data/experimental/matching_files_dataset_op.cc
+++ b/tensorflow/core/kernels/data/experimental/matching_files_dataset_op.cc
@@ -42,7 +42,7 @@ class MatchingFilesDatasetOp : public DatasetOpKernel {
void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override {
const Tensor* patterns_t;
OP_REQUIRES_OK(ctx, ctx->input("patterns", &patterns_t));
- const auto patterns = patterns_t->flat<string>();
+ const auto patterns = patterns_t->flat<tstring>();
size_t num_patterns = static_cast<size_t>(patterns.size());
std::vector<string> pattern_strs;
pattern_strs.reserve(num_patterns);
@@ -126,7 +126,7 @@ class MatchingFilesDatasetOp : public DatasetOpKernel {
current_path.first.end(), '/', '\\');
}
- filepath_tensor.scalar<string>()() =
+ filepath_tensor.scalar<tstring>()() =
std::move(current_path.first);
out_tensors->emplace_back(std::move(filepath_tensor));
*end_of_sequence = false;
diff --git a/tensorflow/core/kernels/data/experimental/prefetching_kernels.cc b/tensorflow/core/kernels/data/experimental/prefetching_kernels.cc
index 8a4089b580b..3fbb9bd79b9 100644
--- a/tensorflow/core/kernels/data/experimental/prefetching_kernels.cc
+++ b/tensorflow/core/kernels/data/experimental/prefetching_kernels.cc
@@ -42,7 +42,7 @@ class IteratorGetDeviceOp : public OpKernel {
// NOTE(mrry): Since the operation's input is a resource, we must be
// colocated with it, and so we can simply return the current device's
// name without looking at the input.
- device_name_t->scalar<string>()() = ctx->device()->name();
+ device_name_t->scalar<tstring>()() = ctx->device()->name();
}
};
diff --git a/tensorflow/core/kernels/data/experimental/stats_aggregator_ops.cc b/tensorflow/core/kernels/data/experimental/stats_aggregator_ops.cc
index a2a1330e29f..05dadf084d4 100644
--- a/tensorflow/core/kernels/data/experimental/stats_aggregator_ops.cc
+++ b/tensorflow/core/kernels/data/experimental/stats_aggregator_ops.cc
@@ -267,7 +267,7 @@ class StatsAggregatorSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &summary_t));
Summary summary;
resource->stats_aggregator()->EncodeToProto(&summary);
- summary_t->scalar<string>()() = summary.SerializeAsString();
+ summary_t->scalar<tstring>()() = summary.SerializeAsString();
}
};
diff --git a/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc b/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc
index 5879750bf18..f45b493d851 100644
--- a/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc
+++ b/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc
@@ -122,7 +122,7 @@ class ToTFRecordOp : public AsyncOpKernel {
if (!end_of_sequence) {
OP_REQUIRES_OK_ASYNC(
- ctx, writer->WriteRecord(components[0].scalar<string>()()),
+ ctx, writer->WriteRecord(components[0].scalar<tstring>()()),
done);
}
components.clear();
diff --git a/tensorflow/core/kernels/data/experimental/unique_dataset_op.cc b/tensorflow/core/kernels/data/experimental/unique_dataset_op.cc
index 3ef920107cd..64f728d58c1 100644
--- a/tensorflow/core/kernels/data/experimental/unique_dataset_op.cc
+++ b/tensorflow/core/kernels/data/experimental/unique_dataset_op.cc
@@ -171,7 +171,7 @@ class UniqueDatasetOp : public UnaryDatasetOpKernel {
return Hash64(t.tensor_data().data(), t.tensor_data().size());
} else {
DCHECK_EQ(DT_STRING, t.dtype());
- auto flat_t = t.flat<string>();
+ auto flat_t = t.flat<tstring>();
uint64 hash = 0;
for (int64 i = 0; i < t.NumElements(); ++i) {
hash = Hash64Combine(hash, Hash64(flat_t(i)));
diff --git a/tensorflow/core/kernels/data/fixed_length_record_dataset_op.cc b/tensorflow/core/kernels/data/fixed_length_record_dataset_op.cc
index dd147c6fd95..976288f4a65 100644
--- a/tensorflow/core/kernels/data/fixed_length_record_dataset_op.cc
+++ b/tensorflow/core/kernels/data/fixed_length_record_dataset_op.cc
@@ -141,7 +141,7 @@ class FixedLengthRecordDatasetOp::Dataset : public DatasetBase {
// Produce the record as output.
Tensor record_tensor(ctx->allocator({}), DT_STRING, {});
- record_tensor.scalar<string>()() = record;
+ record_tensor.scalar<tstring>()() = record;
out_tensors->emplace_back(std::move(record_tensor));
*end_of_sequence = false;
return Status::OK();
@@ -264,7 +264,7 @@ class FixedLengthRecordDatasetOp::Dataset : public DatasetBase {
// Produce the record as output.
Tensor record_tensor(ctx->allocator({}), DT_STRING, {});
- record_tensor.scalar<string>()() = std::move(record);
+ record_tensor.scalar<tstring>()() = std::move(record);
out_tensors->emplace_back(std::move(record_tensor));
*end_of_sequence = false;
return Status::OK();
@@ -282,7 +282,7 @@ class FixedLengthRecordDatasetOp::Dataset : public DatasetBase {
lookahead_cache_.substr(dataset()->record_bytes_);
// Produce the record as output.
Tensor record_tensor(ctx->allocator({}), DT_STRING, {});
- record_tensor.scalar<string>()() = std::move(record);
+ record_tensor.scalar<tstring>()() = std::move(record);
out_tensors->emplace_back(std::move(record_tensor));
*end_of_sequence = false;
return Status::OK();
@@ -459,7 +459,7 @@ void FixedLengthRecordDatasetOp::MakeDataset(OpKernelContext* ctx,
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
int64 header_bytes = -1;
diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc
index 4965c2ee09b..1acf4f4c1bd 100644
--- a/tensorflow/core/kernels/data/iterator_ops.cc
+++ b/tensorflow/core/kernels/data/iterator_ops.cc
@@ -1002,7 +1002,7 @@ void IteratorToStringHandleOp::Compute(OpKernelContext* ctx) {
Tensor* string_handle_t;
OP_REQUIRES_OK(ctx,
ctx->allocate_output(0, TensorShape({}), &string_handle_t));
- string_handle_t->scalar<string>()() =
+ string_handle_t->scalar<tstring>()() =
resource_handle_t.scalar<ResourceHandle>()().SerializeAsString();
}
@@ -1026,7 +1026,7 @@ void IteratorFromStringHandleOp::Compute(OpKernelContext* ctx) {
ResourceHandle resource_handle;
OP_REQUIRES(
- ctx, resource_handle.ParseFromString(string_handle_t.scalar<string>()()),
+ ctx, resource_handle.ParseFromString(string_handle_t.scalar<tstring>()()),
errors::InvalidArgument(
"Could not parse string_handle as a valid ResourceHandle"));
diff --git a/tensorflow/core/kernels/data/multi_device_iterator_ops.cc b/tensorflow/core/kernels/data/multi_device_iterator_ops.cc
index 409a50371f0..7a538d77d1b 100644
--- a/tensorflow/core/kernels/data/multi_device_iterator_ops.cc
+++ b/tensorflow/core/kernels/data/multi_device_iterator_ops.cc
@@ -644,7 +644,7 @@ class MultiDeviceIteratorToStringHandleOp : public OpKernel {
Tensor* string_handle_t;
OP_REQUIRES_OK(ctx,
ctx->allocate_output(0, TensorShape({}), &string_handle_t));
- string_handle_t->scalar<string>()() =
+ string_handle_t->scalar<tstring>()() =
resource_handle_t.scalar<ResourceHandle>()().SerializeAsString();
}
};
@@ -675,7 +675,7 @@ class MultiDeviceIteratorFromStringHandleOp : public OpKernel {
ResourceHandle resource_handle;
OP_REQUIRES(
ctx,
- resource_handle.ParseFromString(string_handle_t.scalar<string>()()),
+ resource_handle.ParseFromString(string_handle_t.scalar<tstring>()()),
errors::InvalidArgument(
"Could not parse string_handle as a valid ResourceHandle"));
diff --git a/tensorflow/core/kernels/data/text_line_dataset_op.cc b/tensorflow/core/kernels/data/text_line_dataset_op.cc
index b8302b890c8..a8ebf631c94 100644
--- a/tensorflow/core/kernels/data/text_line_dataset_op.cc
+++ b/tensorflow/core/kernels/data/text_line_dataset_op.cc
@@ -108,7 +108,7 @@ class TextLineDatasetOp::Dataset : public DatasetBase {
line_contents.size());
out_tensors->emplace_back(ctx->allocator({}), DT_STRING,
TensorShape({}));
- out_tensors->back().scalar<string>()() = std::move(line_contents);
+ out_tensors->back().scalar<tstring>()() = std::move(line_contents);
*end_of_sequence = false;
return Status::OK();
} else if (!errors::IsOutOfRange(s)) {
@@ -266,7 +266,7 @@ void TextLineDatasetOp::MakeDataset(OpKernelContext* ctx,
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
*output = new Dataset(ctx, std::move(filenames), compression_type,
diff --git a/tensorflow/core/kernels/data/tf_record_dataset_op.cc b/tensorflow/core/kernels/data/tf_record_dataset_op.cc
index e35743dae60..2b26f61bf7d 100644
--- a/tensorflow/core/kernels/data/tf_record_dataset_op.cc
+++ b/tensorflow/core/kernels/data/tf_record_dataset_op.cc
@@ -108,7 +108,7 @@ class TFRecordDatasetOp::Dataset : public DatasetBase {
reader_->ReadRecord(&out_tensors->back().scalar<string>()());
if (s.ok()) {
metrics::RecordTFDataBytesRead(
- kDatasetType, out_tensors->back().scalar<string>()().size());
+ kDatasetType, out_tensors->back().scalar<tstring>()().size());
*end_of_sequence = false;
return Status::OK();
}
@@ -224,8 +224,8 @@ void TFRecordDatasetOp::MakeDataset(OpKernelContext* ctx,
std::vector<string> filenames;
filenames.reserve(filenames_tensor->NumElements());
for (int i = 0; i < filenames_tensor->NumElements(); ++i) {
- VLOG(2) << "Reading file: " << filenames_tensor->flat<string>()(i);
- filenames.push_back(filenames_tensor->flat<string>()(i));
+ VLOG(2) << "Reading file: " << filenames_tensor->flat<tstring>()(i);
+ filenames.push_back(filenames_tensor->flat<tstring>()(i));
}
string compression_type;
diff --git a/tensorflow/core/kernels/decode_bmp_op.cc b/tensorflow/core/kernels/decode_bmp_op.cc
index 8a9f7b18601..122b7ecb3da 100644
--- a/tensorflow/core/kernels/decode_bmp_op.cc
+++ b/tensorflow/core/kernels/decode_bmp_op.cc
@@ -54,7 +54,7 @@ class DecodeBmpOp : public OpKernel {
contents.shape().DebugString()));
// Start decoding image to get shape details
- const StringPiece input = contents.scalar<string>()();
+ const StringPiece input = contents.scalar<tstring>()();
OP_REQUIRES(context, (32 <= input.size()),
errors::InvalidArgument("Incomplete bmp content, requires at "
diff --git a/tensorflow/core/kernels/decode_compressed_op.cc b/tensorflow/core/kernels/decode_compressed_op.cc
index 3c3d49e1f8f..78376cea569 100644
--- a/tensorflow/core/kernels/decode_compressed_op.cc
+++ b/tensorflow/core/kernels/decode_compressed_op.cc
@@ -84,13 +84,13 @@ class DecodeCompressedOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const Tensor* bytes_tensor;
OP_REQUIRES_OK(context, context->input("bytes", &bytes_tensor));
- const auto& bytes_flat = bytes_tensor->flat<string>();
+ const auto& bytes_flat = bytes_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output("output", bytes_tensor->shape(),
&output_tensor));
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
if (compression_type_.empty()) {
for (int64 i = 0; i < bytes_flat.size(); i++) {
output_flat(i) = bytes_flat(i);
diff --git a/tensorflow/core/kernels/decode_csv_op.cc b/tensorflow/core/kernels/decode_csv_op.cc
index ba6369533ad..470a7b3859a 100644
--- a/tensorflow/core/kernels/decode_csv_op.cc
+++ b/tensorflow/core/kernels/decode_csv_op.cc
@@ -70,7 +70,7 @@ class DecodeCSVOp : public OpKernel {
" has ", record_defaults[i].NumElements()));
}
- auto records_t = records->flat<string>();
+ auto records_t = records->flat<tstring>();
int64 records_size = records_t.size();
OpOutputList output;
@@ -181,10 +181,10 @@ class DecodeCSVOp : public OpKernel {
errors::InvalidArgument(
"Field ", f,
" is required but missing in record ", i, "!"));
- output[f]->flat<string>()(i) =
- record_defaults[f].flat<string>()(0);
+ output[f]->flat<tstring>()(i) =
+ record_defaults[f].flat<tstring>()(0);
} else {
- output[f]->flat<string>()(i) = fields[f];
+ output[f]->flat<tstring>()(i) = fields[f];
}
break;
}
diff --git a/tensorflow/core/kernels/decode_image_op.cc b/tensorflow/core/kernels/decode_image_op.cc
index 052c9f24e4b..f89533d1574 100644
--- a/tensorflow/core/kernels/decode_image_op.cc
+++ b/tensorflow/core/kernels/decode_image_op.cc
@@ -154,7 +154,7 @@ class DecodeImageOp : public OpKernel {
contents.shape().DebugString()));
// Determine format
- const StringPiece input = contents.scalar<string>()();
+ const StringPiece input = contents.scalar<tstring>()();
const auto magic = ClassifyFileFormat(input);
OP_REQUIRES(
context,
diff --git a/tensorflow/core/kernels/decode_padded_raw_op.cc b/tensorflow/core/kernels/decode_padded_raw_op.cc
index 1e6a0cb7606..12e8ec6aff0 100644
--- a/tensorflow/core/kernels/decode_padded_raw_op.cc
+++ b/tensorflow/core/kernels/decode_padded_raw_op.cc
@@ -39,7 +39,7 @@ class DecodePaddedRawOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const auto& input = context->input(0);
- auto flat_in = input.flat<string>();
+ auto flat_in = input.flat<tstring>();
int fixed_length;
const auto& length_input = context->input(1);
diff --git a/tensorflow/core/kernels/decode_proto_op.cc b/tensorflow/core/kernels/decode_proto_op.cc
index 06dc766794c..5717fa53169 100644
--- a/tensorflow/core/kernels/decode_proto_op.cc
+++ b/tensorflow/core/kernels/decode_proto_op.cc
@@ -748,14 +748,14 @@ class DecodeProtoOp : public OpKernel {
if (is_binary_ && !sanitize_) {
// Fast path.
for (int mi = 0; mi < message_count; ++mi) {
- const string* buf = &buf_tensor.flat<string>()(mi);
+ const tstring* buf = &buf_tensor.flat<tstring>()(mi);
bufs.push_back(buf);
}
} else {
// We will have to allocate a copy, either to convert from text to binary
// or to sanitize a binary proto.
for (int mi = 0; mi < message_count; ++mi) {
- ReserializeMessage(ctx, buf_tensor.flat<string>()(mi),
+ ReserializeMessage(ctx, buf_tensor.flat<tstring>()(mi),
&tmp_binary_bufs[mi]);
if (!ctx->status().ok()) {
return;
@@ -895,8 +895,8 @@ class DecodeProtoOp : public OpKernel {
data = tensor->bit_casted_shaped<uint8, 1>(flatshape).data();
} else {
// DataTypeSize() returns 0 for string types.
- stride = last_dim_size * sizeof(string);
- data = reinterpret_cast<uint8*>(tensor->flat<string>().data());
+ stride = last_dim_size * sizeof(tstring);
+ data = reinterpret_cast<uint8*>(tensor->flat<tstring>().data());
}
}
diff --git a/tensorflow/core/kernels/decode_raw_op.cc b/tensorflow/core/kernels/decode_raw_op.cc
index e68fa407534..942589608c0 100644
--- a/tensorflow/core/kernels/decode_raw_op.cc
+++ b/tensorflow/core/kernels/decode_raw_op.cc
@@ -41,7 +41,7 @@ class DecodeRawOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const auto& input = context->input(0);
int64 str_size = -1;
- auto flat_in = input.flat<string>();
+ auto flat_in = input.flat<tstring>();
for (int64 i = 0; i < flat_in.size(); ++i) {
const string& in_str = flat_in(i);
if (str_size == -1) {
diff --git a/tensorflow/core/kernels/decode_wav_op.cc b/tensorflow/core/kernels/decode_wav_op.cc
index 4bd5d7ac2a6..6325c28b13e 100644
--- a/tensorflow/core/kernels/decode_wav_op.cc
+++ b/tensorflow/core/kernels/decode_wav_op.cc
@@ -40,7 +40,7 @@ class DecodeWavOp : public OpKernel {
OP_REQUIRES(context, TensorShapeUtils::IsScalar(contents.shape()),
errors::InvalidArgument("contents must be scalar, got shape ",
contents.shape().DebugString()));
- const string wav_string = contents.scalar<string>()();
+ const string wav_string = contents.scalar<tstring>()();
OP_REQUIRES(context, wav_string.size() <= std::numeric_limits<int>::max(),
errors::InvalidArgument("WAV contents are too large for int: ",
wav_string.size()));
diff --git a/tensorflow/core/kernels/deserialize_sparse_string_op.cc b/tensorflow/core/kernels/deserialize_sparse_string_op.cc
index d26d8188d51..398df428994 100644
--- a/tensorflow/core/kernels/deserialize_sparse_string_op.cc
+++ b/tensorflow/core/kernels/deserialize_sparse_string_op.cc
@@ -75,7 +75,7 @@ class DeserializeSparseOp : public OpKernel {
if (num_sparse_tensors == 1 && ndims == 1) {
// Special case with a single sparse tensor. We can avoid data
// motion in the Concat and Reshape.
- const auto& serialized_sparse_t = serialized_sparse.vec<string>();
+ const auto& serialized_sparse_t = serialized_sparse.vec<tstring>();
Tensor output_indices;
Tensor output_values;
@@ -98,7 +98,7 @@ class DeserializeSparseOp : public OpKernel {
values.reserve(num_sparse_tensors);
const auto& serialized_sparse_t =
- serialized_sparse.flat_inner_dims<string, 2>();
+ serialized_sparse.flat_inner_dims<tstring, 2>();
for (int i = 0; i < num_sparse_tensors; ++i) {
Tensor output_indices;
Tensor output_values;
diff --git a/tensorflow/core/kernels/encode_proto_op.cc b/tensorflow/core/kernels/encode_proto_op.cc
index b023f1cdeb8..12bbd34ec71 100644
--- a/tensorflow/core/kernels/encode_proto_op.cc
+++ b/tensorflow/core/kernels/encode_proto_op.cc
@@ -303,7 +303,7 @@ Status WriteVarLenField(const FieldDescriptor& field_desc, const Tensor& input,
// code it ourselves.
Status WriteGroup(const FieldDescriptor& field_desc, const Tensor& input,
int message_index, int size, CodedOutputStream* output) {
- auto input_t = input.flat_inner_dims<string>();
+ auto input_t = input.flat_inner_dims<tstring>();
for (int64 i = 0; i < size; i++) {
const string& value = input_t(static_cast<int64>(message_index), i);
WireFormatLite::WriteTag(field_desc.number(),
@@ -587,7 +587,7 @@ class EncodeProtoOp : public OpKernel {
Tensor* output_tensor;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, common_prefix, &output_tensor));
- auto bufs = output_tensor->flat<string>();
+ auto bufs = output_tensor->flat<tstring>();
for (int message_index = 0; message_index < message_count;
message_index++) {
// TODO(nix): possibly optimize allocation here by calling
diff --git a/tensorflow/core/kernels/example_parsing_ops.cc b/tensorflow/core/kernels/example_parsing_ops.cc
index 708b52a5174..783190b50ef 100644
--- a/tensorflow/core/kernels/example_parsing_ops.cc
+++ b/tensorflow/core/kernels/example_parsing_ops.cc
@@ -63,10 +63,10 @@ class ParseExampleOp : public OpKernel {
// Copy from OpInputList to std::vector<string>.
for (int di = 0; di < attrs_.num_dense; ++di) {
- dense_keys_t[di] = dense_keys[di].scalar<string>()();
+ dense_keys_t[di] = dense_keys[di].scalar<tstring>()();
}
for (int di = 0; di < attrs_.num_sparse; ++di) {
- sparse_keys_t[di] = sparse_keys[di].scalar<string>()();
+ sparse_keys_t[di] = sparse_keys[di].scalar<tstring>()();
}
if (names->NumElements() > 0) {
@@ -234,7 +234,7 @@ class ParseSingleExampleOp : public OpKernel {
config.sparse.push_back({attrs_.sparse_keys[d], attrs_.sparse_types[d]});
}
- const string& serialized_proto = serialized->scalar<string>()();
+ const string& serialized_proto = serialized->scalar<tstring>()();
OP_REQUIRES_OK(ctx,
FastParseSingleExample(config, serialized_proto, &result));
@@ -473,7 +473,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"Expected context_dense_keys[", di,
"] to be a scalar, got shape: ",
context_dense_keys[di].shape().DebugString()));
- context_dense_keys_t[di] = context_dense_keys[di].scalar<string>()();
+ context_dense_keys_t[di] = context_dense_keys[di].scalar<tstring>()();
}
for (int di = 0; di < attrs_.num_context_sparse; ++di) {
OP_REQUIRES(ctx,
@@ -482,7 +482,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"Expected context_sparse_keys[", di,
"] to be a scalar, got shape: ",
context_sparse_keys[di].shape().DebugString()));
- context_sparse_keys_t[di] = context_sparse_keys[di].scalar<string>()();
+ context_sparse_keys_t[di] = context_sparse_keys[di].scalar<tstring>()();
}
for (int di = 0; di < attrs_.num_feature_list_dense; ++di) {
OP_REQUIRES(
@@ -492,7 +492,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"] to be a scalar, got shape: ",
feature_list_dense_keys[di].shape().DebugString()));
feature_list_dense_keys_t[di] =
- feature_list_dense_keys[di].scalar<string>()();
+ feature_list_dense_keys[di].scalar<tstring>()();
}
for (int di = 0; di < attrs_.num_feature_list_sparse; ++di) {
OP_REQUIRES(
@@ -502,7 +502,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"] to be a scalar, got shape: ",
feature_list_sparse_keys[di].shape().DebugString()));
feature_list_sparse_keys_t[di] =
- feature_list_sparse_keys[di].scalar<string>()();
+ feature_list_sparse_keys[di].scalar<tstring>()();
}
OP_REQUIRES(
ctx,
@@ -513,7 +513,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"to be a vector, got shape: ",
feature_list_dense_missing_assumed_empty->shape().DebugString()));
auto feature_list_dense_missing_assumped_empty_t =
- feature_list_dense_missing_assumed_empty->vec<string>();
+ feature_list_dense_missing_assumed_empty->vec<tstring>();
for (int de = 0;
de < feature_list_dense_missing_assumed_empty->NumElements(); ++de) {
feature_list_dense_missing_assumed_empty_set.insert(
@@ -527,7 +527,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
"Expected debug_name to be a scalar, got shape: ",
debug_name->shape().DebugString()));
}
- auto debug_name_t = debug_name->scalar<string>();
+ auto debug_name_t = debug_name->scalar<tstring>();
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(serialized->shape()),
errors::InvalidArgument(
@@ -561,7 +561,7 @@ class ParseSingleSequenceExampleOp : public OpKernel {
}
}
- auto serialized_t = serialized->scalar<string>();
+ auto serialized_t = serialized->scalar<tstring>();
OpOutputList context_sparse_indices;
OpOutputList context_sparse_values;
diff --git a/tensorflow/core/kernels/example_parsing_ops_test.cc b/tensorflow/core/kernels/example_parsing_ops_test.cc
index 4d843ab02cc..db1672e70a0 100644
--- a/tensorflow/core/kernels/example_parsing_ops_test.cc
+++ b/tensorflow/core/kernels/example_parsing_ops_test.cc
@@ -114,7 +114,7 @@ struct ExampleStore {
Example example;
Filler fill;
Tensor record_string(DT_STRING, TensorShape({batch_size}));
- auto string_t = record_string.vec<string>();
+ auto string_t = record_string.vec<tstring>();
example.Clear();
for (int b = 0; b < batch_size; ++b) {
for (int k = 0; k < num_keys; ++k) {
@@ -163,7 +163,7 @@ static Graph* ParseExample(int batch_size, int num_keys, int feature_size) {
Options opt;
for (int i = 0; i < num_keys; ++i) {
Tensor key(DT_STRING, TensorShape());
- key.scalar<string>()() = strings::Printf("feature_%d", i);
+ key.scalar<tstring>()() = strings::Printf("feature_%d", i);
switch (opt.benchmark_type) {
case kDense:
dense_keys.emplace_back(test::graph::Constant(g, key));
@@ -205,7 +205,7 @@ static Graph* ParseSingleExample(int num_keys, int feature_size) {
Options::Store::GetSerializedExample()[std::make_tuple(1, num_keys,
feature_size)];
Tensor serialized(DT_STRING, TensorShape());
- serialized.scalar<string>()() = serialized_batch_1.vec<string>()(0);
+ serialized.scalar<tstring>()() = serialized_batch_1.vec<tstring>()(0);
std::vector<string> sparse_keys;
std::vector<string> dense_keys;
diff --git a/tensorflow/core/kernels/extract_jpeg_shape_op.cc b/tensorflow/core/kernels/extract_jpeg_shape_op.cc
index ab424595c1a..c74245dcf85 100644
--- a/tensorflow/core/kernels/extract_jpeg_shape_op.cc
+++ b/tensorflow/core/kernels/extract_jpeg_shape_op.cc
@@ -41,7 +41,7 @@ class ExtractJpegShapeOp : public OpKernel {
OP_REQUIRES(context, TensorShapeUtils::IsScalar(contents.shape()),
errors::InvalidArgument("contents must be scalar, got shape ",
contents.shape().DebugString()));
- const StringPiece input = contents.scalar<string>()();
+ const StringPiece input = contents.scalar<tstring>()();
OP_REQUIRES(context, input.size() <= std::numeric_limits<int>::max(),
errors::InvalidArgument("JPEG contents are too large for int: ",
input.size()));
diff --git a/tensorflow/core/kernels/fact_op.cc b/tensorflow/core/kernels/fact_op.cc
index 4a1aa433bc9..6c11ab7a2d2 100644
--- a/tensorflow/core/kernels/fact_op.cc
+++ b/tensorflow/core/kernels/fact_op.cc
@@ -85,7 +85,7 @@ class FactOpKernel : public OpKernel {
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(
context, context->allocate_output(0, TensorShape({}), &output_tensor));
- auto output = output_tensor->template scalar<string>();
+ auto output = output_tensor->template scalar<tstring>();
string coded = facts[context->env()->NowMicros() % count];
E(&coded);
diff --git a/tensorflow/core/kernels/fingerprint_op.cc b/tensorflow/core/kernels/fingerprint_op.cc
index 20529326b3d..660f900c405 100644
--- a/tensorflow/core/kernels/fingerprint_op.cc
+++ b/tensorflow/core/kernels/fingerprint_op.cc
@@ -110,14 +110,14 @@ class FingerprintOp : public OpKernel {
// and each row contains the fingerprint value of corresponding string.
// To compute fingerprints of multiple strings, this op fingerprints the
// buffer containing the string fingerprints.
- FarmhashFingerprint64(input.flat<string>(), temp.tensor<uint8, 2>());
+ FarmhashFingerprint64(input.flat<tstring>(), temp.tensor<uint8, 2>());
FarmhashFingerprint64(static_cast<const Tensor&>(temp).shaped<uint8, 2>(
{dim0, dim1 * kFingerprintSize}),
output->matrix<uint8>());
} else {
// In case dim1 == 1, each string computes into its own fingerprint
// value. There is no need to fingerprint twice.
- FarmhashFingerprint64(input.flat<string>(), output->matrix<uint8>());
+ FarmhashFingerprint64(input.flat<tstring>(), output->matrix<uint8>());
}
} else {
auto data = input.bit_casted_shaped<uint8, 2>(
diff --git a/tensorflow/core/kernels/fingerprint_op_test.cc b/tensorflow/core/kernels/fingerprint_op_test.cc
index 14376cb2d35..d9a9a97798d 100644
--- a/tensorflow/core/kernels/fingerprint_op_test.cc
+++ b/tensorflow/core/kernels/fingerprint_op_test.cc
@@ -51,7 +51,7 @@ class FingerprintOpTest : public OpsTestBase {
inputs_.push_back(TensorValue(data));
method_ = Tensor(DT_STRING, TensorShape{});
- method_.scalar<string>()() = method;
+ method_.scalar<tstring>()() = method;
inputs_.push_back(TensorValue(&method_));
return Status::OK();
}
@@ -77,7 +77,7 @@ TEST_F(FingerprintOpTest, GoldenValue) {
// special-case handling.
TEST_F(FingerprintOpTest, StringGoldenValue) {
Tensor data(DT_STRING, {1, 2, 2});
- auto buffer = data.flat<string>();
+ auto buffer = data.flat<tstring>();
buffer(0).resize(10);
buffer(1).resize(7);
buffer(2).resize(0);
@@ -134,7 +134,7 @@ TEST_F(FingerprintOpTest, CollisionString) {
constexpr int64 size = 256;
Tensor tensor(DT_STRING, {1});
- auto& input = tensor.vec<string>()(0);
+ auto& input = tensor.vec<tstring>()(0);
input.resize(size);
TTypes<uint8>::UnalignedFlat buffer(reinterpret_cast<uint8*>(&*input.begin()),
@@ -163,7 +163,7 @@ TEST_F(FingerprintOpTest, CompareBytesAndString) {
auto pods = pods_tensor.matrix<float>();
pods.setRandom();
- auto strings = strings_tensor.vec<string>();
+ auto strings = strings_tensor.vec<tstring>();
for (int64 i = 0; i < strings.size(); ++i) {
strings(i).assign(reinterpret_cast<const char*>(&pods(i, 0)),
pods.dimension(1) * sizeof(pods(i, 0)));
@@ -199,7 +199,7 @@ TEST(FingerprintOpShapeFnTest, MethodKnownStatically) {
ShapeInferenceTestOp op("Fingerprint");
Tensor method(DT_STRING, TensorShape{});
- method.scalar<string>()() = "farmhash64";
+ method.scalar<tstring>()() = "farmhash64";
op.input_tensors.assign({nullptr, &method});
TF_ASSERT_OK(MakeNodeDef(DT_UINT8, &op.node_def));
@@ -229,12 +229,12 @@ TEST(FingerprintOpShapeFnTest, InvalidMethod) {
// When `method` shape is unknown statically.
Tensor method(DT_STRING, TensorShape{1});
- method.vec<string>()(0) = "farmhash64";
+ method.vec<tstring>()(0) = "farmhash64";
op.input_tensors.assign({nullptr, &method});
INFER_ERROR("must be rank 0", op, "?;?");
method = Tensor(DT_STRING, TensorShape{});
- method.scalar<string>()() = "unsupported_method";
+ method.scalar<tstring>()() = "unsupported_method";
op.input_tensors.assign({nullptr, &method});
INFER_ERROR("unsupported_method", op, "?;?");
}
diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc
index 33bed217003..087ff2ee847 100644
--- a/tensorflow/core/kernels/function_ops.cc
+++ b/tensorflow/core/kernels/function_ops.cc
@@ -318,7 +318,7 @@ void RemoteCallOp::ComputeAsync(OpKernelContext* ctx, DoneCallback done) {
string target_device;
OP_REQUIRES_OK_ASYNC(
ctx,
- DeviceNameUtils::CanonicalizeDeviceName(target->scalar<string>()(),
+ DeviceNameUtils::CanonicalizeDeviceName(target->scalar<tstring>()(),
source_device, &target_device),
done);
diff --git a/tensorflow/core/kernels/functional_ops.cc b/tensorflow/core/kernels/functional_ops.cc
index 920c14b36ac..d7d15d5f14b 100644
--- a/tensorflow/core/kernels/functional_ops.cc
+++ b/tensorflow/core/kernels/functional_ops.cc
@@ -82,7 +82,7 @@ Status ToBool(gtl::ArraySlice<Tensor> t, bool* v) {
*v = t[0].scalar<bool>()();
break;
case DT_STRING:
- *v = !t[0].scalar<string>()().empty();
+ *v = !t[0].scalar<tstring>()().empty();
break;
default:
return errors::InvalidArgument(DataTypeString(t[0].dtype()),
diff --git a/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
index f72dfb39b31..35cd0fba1a6 100644
--- a/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
+++ b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc
@@ -51,7 +51,7 @@ class FuzzExampleProtoFastParsing : public FuzzSession {
void FuzzImpl(const uint8_t* data, size_t size) final {
// TODO(dga): Test the batch case also.
Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
- input_tensor.scalar<string>()() =
+ input_tensor.scalar<tstring>()() =
string(reinterpret_cast<const char*>(data), size);
RunInputs({{"input", input_tensor}});
}
diff --git a/tensorflow/core/kernels/fuzzing/fuzz_session.h b/tensorflow/core/kernels/fuzzing/fuzz_session.h
index 4b036b181de..dc0435cdc32 100644
--- a/tensorflow/core/kernels/fuzzing/fuzz_session.h
+++ b/tensorflow/core/kernels/fuzzing/fuzz_session.h
@@ -145,7 +145,7 @@ class FuzzSession {
class FuzzStringInputOp : public FuzzSession {
void FuzzImpl(const uint8_t* data, size_t size) final {
Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
- input_tensor.scalar<string>()() =
+ input_tensor.scalar<tstring>()() =
string(reinterpret_cast<const char*>(data), size);
RunInputs({{"input", input_tensor}});
}
diff --git a/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
index 0ce4206fc3c..a71f2902559 100644
--- a/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
+++ b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc
@@ -61,7 +61,7 @@ class FuzzParseTensor : public FuzzSession {
// Now we can do the actual fuzz implementation
Tensor input_tensor(tensorflow::DT_STRING, TensorShape({}));
- input_tensor.scalar<string>()() = as_string;
+ input_tensor.scalar<tstring>()() = as_string;
RunInputs({{"input", input_tensor}});
}
};
diff --git a/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
index b3b637bac72..d4e64181ab2 100644
--- a/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
+++ b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc
@@ -42,9 +42,9 @@ class FuzzStringSplit : public FuzzSession {
if (delim_len > size) {
delim_len = size - 1;
}
- delimiter_tensor.scalar<string>()() =
+ delimiter_tensor.scalar<tstring>()() =
string(reinterpret_cast<const char*>(data), delim_len);
- input_tensor.scalar<string>()() = string(
+ input_tensor.scalar<tstring>()() = string(
reinterpret_cast<const char*>(data + delim_len), size - delim_len);
RunInputs({{"input", input_tensor}, {"delimiter", delimiter_tensor}});
diff --git a/tensorflow/core/kernels/fuzzing/string_split_v2_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_split_v2_fuzz.cc
index f7e3da80437..367759d374e 100644
--- a/tensorflow/core/kernels/fuzzing/string_split_v2_fuzz.cc
+++ b/tensorflow/core/kernels/fuzzing/string_split_v2_fuzz.cc
@@ -46,10 +46,10 @@ class FuzzStringSplitV2 : public FuzzSession {
if (sep_len > size) {
sep_len = size - 1;
}
- separator_tensor.scalar<string>()() =
+ separator_tensor.scalar<tstring>()() =
string(reinterpret_cast<const char*>(data), sep_len);
- input_tensor.scalar<string>()() = string(
- reinterpret_cast<const char*>(data + sep_len), size - sep_len);
+ input_tensor.scalar<tstring>()() =
+ string(reinterpret_cast<const char*>(data + sep_len), size - sep_len);
RunInputs({{"input", input_tensor}, {"separator", separator_tensor}});
}
diff --git a/tensorflow/core/kernels/generate_vocab_remapping_op.cc b/tensorflow/core/kernels/generate_vocab_remapping_op.cc
index 2b97677e385..03d9191423d 100644
--- a/tensorflow/core/kernels/generate_vocab_remapping_op.cc
+++ b/tensorflow/core/kernels/generate_vocab_remapping_op.cc
@@ -57,7 +57,7 @@ class GenerateVocabRemappingOp : public OpKernel {
// Build a new ID->token lookup table.
const string& new_vocab_filename =
- new_vocab_file_tensor->scalar<string>()();
+ new_vocab_file_tensor->scalar<tstring>()();
OP_REQUIRES(context, !new_vocab_filename.empty(),
errors::InvalidArgument("new vocab filename cannot be empty."));
lookup::HashTable<int64, string>* new_vocab_table =
@@ -88,7 +88,7 @@ class GenerateVocabRemappingOp : public OpKernel {
old_vocab_file_tensor->shape().DebugString()));
// Build a token->old ID lookup table.
const string& old_vocab_filename =
- old_vocab_file_tensor->scalar<string>()();
+ old_vocab_file_tensor->scalar<tstring>()();
OP_REQUIRES(context, !old_vocab_filename.empty(),
errors::InvalidArgument("new vocab filename cannot be empty."));
lookup::HashTable<string, int64>* old_vocab_table =
@@ -118,7 +118,7 @@ class GenerateVocabRemappingOp : public OpKernel {
OP_REQUIRES_OK(
context, context->allocate_temp(
DT_STRING, TensorShape({num_new_vocab_}), &default_token));
- auto default_token_vec = default_token.vec<string>();
+ auto default_token_vec = default_token.vec<tstring>();
default_token_vec.setConstant("" /* NOT_FOUND_TOKEN */);
Tensor default_id;
diff --git a/tensorflow/core/kernels/inplace_ops.cc b/tensorflow/core/kernels/inplace_ops.cc
index c0d39d9d46d..a6f026150ea 100644
--- a/tensorflow/core/kernels/inplace_ops.cc
+++ b/tensorflow/core/kernels/inplace_ops.cc
@@ -319,8 +319,8 @@ void DoInplaceOp(const CPUDevice& d, InplaceOpType op, const Tensor& i,
void DoInplaceStringUpdateOp(const CPUDevice& d, const Tensor& i,
const Tensor& v, Tensor* y) {
auto Ti = i.flat<int32>();
- auto Tv = v.flat_outer_dims<string>();
- auto Ty = y->flat_outer_dims<string>();
+ auto Tv = v.flat_outer_dims<tstring>();
+ auto Ty = y->flat_outer_dims<tstring>();
auto nrows = Ty.dimension(0);
for (int64 j = 0; j < Ti.size(); ++j) {
auto r = (Ti(j) % nrows + nrows) % nrows; // Guard index range.
diff --git a/tensorflow/core/kernels/load_and_remap_matrix_op.cc b/tensorflow/core/kernels/load_and_remap_matrix_op.cc
index 9d5a4b2f035..3b086517178 100644
--- a/tensorflow/core/kernels/load_and_remap_matrix_op.cc
+++ b/tensorflow/core/kernels/load_and_remap_matrix_op.cc
@@ -123,12 +123,12 @@ class LoadAndRemapMatrixOp : public OpKernel {
// Processes the checkpoint source and the provided Tensor name.
const Tensor* ckpt_path_t;
OP_REQUIRES_OK(context, context->input("ckpt_path", &ckpt_path_t));
- const string ckpt_path = *(ckpt_path_t->scalar<string>().data());
+ const string ckpt_path = *(ckpt_path_t->scalar<tstring>().data());
const Tensor* old_tensor_name_t;
OP_REQUIRES_OK(context,
context->input("old_tensor_name", &old_tensor_name_t));
const string old_tensor_name =
- *(old_tensor_name_t->scalar<string>().data());
+ *(old_tensor_name_t->scalar<tstring>().data());
LOG(INFO) << "Processing checkpoint : " << ckpt_path;
BundleReader reader(context->env(), ckpt_path);
diff --git a/tensorflow/core/kernels/logging_ops.cc b/tensorflow/core/kernels/logging_ops.cc
index f93d3246af4..e4d04c4245c 100644
--- a/tensorflow/core/kernels/logging_ops.cc
+++ b/tensorflow/core/kernels/logging_ops.cc
@@ -143,7 +143,7 @@ class PrintV2Op : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* input_;
OP_REQUIRES_OK(ctx, ctx->input("input", &input_));
- const string& msg = input_->scalar<string>()();
+ const string& msg = input_->scalar<tstring>()();
string ended_msg = strings::StrCat(msg, end_);
diff --git a/tensorflow/core/kernels/lookup_table_init_op.cc b/tensorflow/core/kernels/lookup_table_init_op.cc
index 6e77e1ee012..83721b2cea4 100644
--- a/tensorflow/core/kernels/lookup_table_init_op.cc
+++ b/tensorflow/core/kernels/lookup_table_init_op.cc
@@ -130,7 +130,7 @@ class InitializeTableFromTextFileOp : public OpKernel {
errors::InvalidArgument("filename should be a single string, but got ",
vocab_filename_tensor.shape().DebugString()));
- string vocab_filename = vocab_filename_tensor.scalar<string>()();
+ string vocab_filename = vocab_filename_tensor.scalar<tstring>()();
OP_REQUIRES(ctx, !vocab_filename.empty(),
errors::InvalidArgument("filename cannot be empty."));
diff --git a/tensorflow/core/kernels/lookup_table_op.h b/tensorflow/core/kernels/lookup_table_op.h
index 28a3d94e579..28d63cbf797 100644
--- a/tensorflow/core/kernels/lookup_table_op.h
+++ b/tensorflow/core/kernels/lookup_table_op.h
@@ -92,7 +92,7 @@ class LookupTableOp : public OpKernel {
cinfo_.name());
} else {
if (!table_handle_set_) {
- auto h = table_handle_.AccessTensor(ctx)->template flat<string>();
+ auto h = table_handle_.AccessTensor(ctx)->template flat<tstring>();
h(0) = cinfo_.container();
h(1) = cinfo_.name();
}
diff --git a/tensorflow/core/kernels/lookup_util.cc b/tensorflow/core/kernels/lookup_util.cc
index c3b80f04ed2..1fe7988aa67 100644
--- a/tensorflow/core/kernels/lookup_util.cc
+++ b/tensorflow/core/kernels/lookup_util.cc
@@ -238,7 +238,7 @@ class TextFileLineIterator
tensor->flat<double>()(0) = value;
} break;
case DT_STRING:
- tensor->flat<string>()(0) = token;
+ tensor->flat<tstring>()(0) = token;
break;
default:
valid_ = false;
@@ -264,7 +264,7 @@ Status GetTableHandle(const string& input_name, OpKernelContext* ctx,
"Lookup table handle must be scalar, but had shape: ",
tensor.shape().DebugString());
}
- auto h = tensor.flat<string>();
+ auto h = tensor.flat<tstring>();
*container = h(0);
*table_handle = h(1);
}
diff --git a/tensorflow/core/kernels/matching_files_op.cc b/tensorflow/core/kernels/matching_files_op.cc
index 7912ca1563c..0ba718c88ec 100644
--- a/tensorflow/core/kernels/matching_files_op.cc
+++ b/tensorflow/core/kernels/matching_files_op.cc
@@ -40,7 +40,7 @@ class MatchingFilesOp : public OpKernel {
errors::InvalidArgument(
"Input patterns tensor must be scalar or vector, but had shape: ",
patterns_t->shape().DebugString()));
- const auto patterns = patterns_t->flat<string>();
+ const auto patterns = patterns_t->flat<tstring>();
int num_patterns = patterns.size();
int num_files = 0;
std::vector<std::vector<string>> all_fnames(num_patterns);
@@ -53,7 +53,7 @@ class MatchingFilesOp : public OpKernel {
OP_REQUIRES_OK(
context, context->allocate_output("filenames", TensorShape({num_files}),
&output_t));
- auto output = output_t->vec<string>();
+ auto output = output_t->vec<tstring>();
int index = 0;
for (int i = 0; i < num_patterns; ++i) {
for (int j = 0; j < all_fnames[i].size(); j++) {
diff --git a/tensorflow/core/kernels/parse_tensor_op.cc b/tensorflow/core/kernels/parse_tensor_op.cc
index 8e175fe8d4b..d273f671c6c 100644
--- a/tensorflow/core/kernels/parse_tensor_op.cc
+++ b/tensorflow/core/kernels/parse_tensor_op.cc
@@ -39,7 +39,7 @@ class ParseTensorOp : public OpKernel {
"Expected `serialized` to be a scalar, got shape: ",
serialized.shape().DebugString()));
- auto serialized_t = serialized.scalar<string>();
+ auto serialized_t = serialized.scalar<tstring>();
TensorProto proto;
OP_REQUIRES(ctx, ParseProtoUnlimited(&proto, serialized_t()),
diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc
index 6ed5bb0c752..67e8c943a65 100644
--- a/tensorflow/core/kernels/queue_ops.cc
+++ b/tensorflow/core/kernels/queue_ops.cc
@@ -84,8 +84,8 @@ class FakeQueueOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const ResourceHandle& ref = context->input(0).flat<ResourceHandle>()(0);
- handle_.AccessTensor(context)->flat<string>()(0) = ref.container();
- handle_.AccessTensor(context)->flat<string>()(1) = ref.name();
+ handle_.AccessTensor(context)->flat<tstring>()(0) = ref.container();
+ handle_.AccessTensor(context)->flat<tstring>()(1) = ref.name();
context->set_output_ref(0, &mu_, handle_.AccessTensor(context));
}
diff --git a/tensorflow/core/kernels/reader_ops.cc b/tensorflow/core/kernels/reader_ops.cc
index abd16de6a1c..d93197c5b04 100644
--- a/tensorflow/core/kernels/reader_ops.cc
+++ b/tensorflow/core/kernels/reader_ops.cc
@@ -139,8 +139,8 @@ class ReaderReadUpToOp : public ReaderVerbAsyncOpKernel {
context->allocate_output(
"values", TensorShape({num_actually_read}), &values));
- auto keys_t = keys->vec<string>();
- auto values_t = values->vec<string>();
+ auto keys_t = keys->vec<tstring>();
+ auto values_t = values->vec<tstring>();
for (int i = 0; i < num_actually_read; ++i) {
keys_t(i) = std::move(keys_vec[i]);
values_t(i) = std::move(values_vec[i]);
@@ -221,7 +221,7 @@ class ReaderRestoreStateOp : public ReaderVerbSyncOpKernel {
context, TensorShapeUtils::IsScalar(tensor->shape()),
errors::InvalidArgument("Reader state must be scalar, but had shape: ",
tensor->shape().DebugString()));
- OP_REQUIRES_OK(context, reader->RestoreState(tensor->scalar<string>()()));
+ OP_REQUIRES_OK(context, reader->RestoreState(tensor->scalar<tstring>()()));
}
};
diff --git a/tensorflow/core/kernels/reduce_join_op.cc b/tensorflow/core/kernels/reduce_join_op.cc
index 7a81dfd0369..562281ea308 100644
--- a/tensorflow/core/kernels/reduce_join_op.cc
+++ b/tensorflow/core/kernels/reduce_join_op.cc
@@ -122,7 +122,7 @@ class ReduceJoinOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const Tensor& input = context->input(0);
- const auto input_flat = input.flat<string>();
+ const auto input_flat = input.flat<tstring>();
const TensorShape& input_shape = input.shape();
const int32 input_dims = input_shape.dims();
@@ -156,7 +156,7 @@ class ReduceJoinOp : public OpKernel {
GetOutputShape(index_is_reduced, input_shape, keep_dims_);
OP_REQUIRES_OK(context, context->allocate_output("output", output_shape,
&output_tensor));
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
const int64 reduction_iter_size =
GetReductionIterSize(reduced_indices, input_shape);
diff --git a/tensorflow/core/kernels/regex_full_match_op.cc b/tensorflow/core/kernels/regex_full_match_op.cc
index 7edaaad8f78..04da969df12 100644
--- a/tensorflow/core/kernels/regex_full_match_op.cc
+++ b/tensorflow/core/kernels/regex_full_match_op.cc
@@ -31,14 +31,14 @@ class RegexFullMatchOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
const Tensor* pattern_tensor;
OP_REQUIRES_OK(ctx, ctx->input("pattern", &pattern_tensor));
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(pattern_tensor->shape()),
errors::InvalidArgument("Pattern must be scalar, but received ",
pattern_tensor->shape().DebugString()));
- const string pattern = pattern_tensor->flat<string>()(0);
+ const string pattern = pattern_tensor->flat<tstring>()(0);
const RE2 match(pattern);
OP_REQUIRES(ctx, match.ok(),
errors::InvalidArgument("Invalid pattern: ", pattern,
@@ -71,7 +71,7 @@ class StaticRegexFullMatchOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", input_tensor->shape(),
diff --git a/tensorflow/core/kernels/regex_replace_op.cc b/tensorflow/core/kernels/regex_replace_op.cc
index a1b948891d6..76c57350c52 100644
--- a/tensorflow/core/kernels/regex_replace_op.cc
+++ b/tensorflow/core/kernels/regex_replace_op.cc
@@ -44,9 +44,9 @@ Status InternalCompute(const RE2& match, const string& rewrite,
} else {
TF_RETURN_IF_ERROR(
ctx->allocate_output("output", input_tensor->shape(), &output_tensor));
- output_tensor->flat<string>() = input_tensor->flat<string>();
+ output_tensor->flat<tstring>() = input_tensor->flat<tstring>();
}
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
for (size_t i = 0; i < output_flat.size(); ++i) {
if (replace_global) {
RE2::GlobalReplace(&output_flat(i), match, rewrite);
@@ -70,7 +70,7 @@ class RegexReplaceOp : public OpKernel {
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(pattern_tensor->shape()),
errors::InvalidArgument("Pattern must be scalar, but received ",
pattern_tensor->shape().DebugString()));
- const string pattern = pattern_tensor->flat<string>()(0);
+ const string pattern = pattern_tensor->flat<tstring>()(0);
const RE2 match(pattern);
OP_REQUIRES(ctx, match.ok(),
errors::InvalidArgument("Invalid pattern: ", pattern,
@@ -81,7 +81,7 @@ class RegexReplaceOp : public OpKernel {
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(rewrite_tensor->shape()),
errors::InvalidArgument("Rewrite must be scalar, but received ",
rewrite_tensor->shape().DebugString()));
- const string rewrite = rewrite_tensor->flat<string>()(0);
+ const string rewrite = rewrite_tensor->flat<tstring>()(0);
OP_REQUIRES_OK(ctx, InternalCompute(match, rewrite, replace_global_, ctx));
}
diff --git a/tensorflow/core/kernels/regex_replace_op_test.cc b/tensorflow/core/kernels/regex_replace_op_test.cc
index 9691d4a89f5..bfc45e8bc07 100644
--- a/tensorflow/core/kernels/regex_replace_op_test.cc
+++ b/tensorflow/core/kernels/regex_replace_op_test.cc
@@ -60,7 +60,7 @@ const char kRewrite[] = " ";
Tensor GetTestTensor(int batch) {
const int sz = TF_ARRAYSIZE(lines);
Tensor t(DT_STRING, {batch});
- auto s = t.flat<string>();
+ auto s = t.flat<tstring>();
for (int i = 0; i < batch; ++i) {
s(i) = lines[i % sz];
}
diff --git a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
index 26f107f9403..5e01f4d2d33 100644
--- a/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
+++ b/tensorflow/core/kernels/remote_fused_graph_execute_utils.cc
@@ -1356,7 +1356,7 @@ RemoteFusedGraphExecuteUtils::FuseRemoteGraphByPlacedArguments(
dst_ptr = tensor->flat<int8>().data();
break;
case DT_STRING:
- dst_ptr = tensor->flat<string>().data();
+ dst_ptr = tensor->flat<tstring>().data();
break;
case DT_INT64:
dst_ptr = tensor->flat<int64>().data();
diff --git a/tensorflow/core/kernels/restore_op_test.cc b/tensorflow/core/kernels/restore_op_test.cc
index b6f15a9dc25..1e6ca10d4a1 100644
--- a/tensorflow/core/kernels/restore_op_test.cc
+++ b/tensorflow/core/kernels/restore_op_test.cc
@@ -94,7 +94,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// Input #0 is the file name
Tensor input_0(DT_STRING, TensorShape({}));
- input_0.scalar<string>()() = filename;
+ input_0.scalar<tstring>()() = filename;
inputs.push_back({nullptr, &input_0});
// Input #1 is the tensor names
@@ -203,7 +203,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d integer tensor
{
MakeRestoreOp(DT_INT32);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[1];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[1];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({10});
@@ -215,7 +215,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d float tensor
{
MakeRestoreOp(DT_FLOAT);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[2];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[2];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
@@ -227,7 +227,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d double tensor
{
MakeRestoreOp(DT_DOUBLE);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[3];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[3];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
@@ -239,7 +239,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d qint8 tensor
{
MakeRestoreOp(DT_QINT8);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[4];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[4];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({3, 2});
@@ -251,7 +251,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d qint32 tensor
{
MakeRestoreOp(DT_QINT32);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[5];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[5];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 3});
@@ -264,7 +264,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d uint8 tensor
{
MakeRestoreOp(DT_UINT8);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[6];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[6];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({11});
@@ -276,7 +276,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d int8 tensor
{
MakeRestoreOp(DT_INT8);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[7];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[7];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({7});
@@ -288,7 +288,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d int16 tensor
{
MakeRestoreOp(DT_INT16);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[8];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[8];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({7});
@@ -300,7 +300,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d int64 tensor
{
MakeRestoreOp(DT_INT64);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[9];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[9];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({9});
@@ -312,18 +312,18 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 1-d string tensor
{
MakeRestoreOp(DT_STRING);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[10];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[10];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2});
EXPECT_TRUE(output->shape().IsSameSize(expected));
- EXPECT_EQ("no", output->flat<string>()(0));
- EXPECT_EQ("yes", output->flat<string>()(1));
+ EXPECT_EQ("no", output->flat<tstring>()(0));
+ EXPECT_EQ("yes", output->flat<tstring>()(1));
}
// The 2-d complex64 tensor
{
MakeRestoreOp(DT_COMPLEX64);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[11];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[11];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 3});
@@ -335,7 +335,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d half tensor
{
MakeRestoreOp(DT_HALF);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[12];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[12];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
@@ -348,7 +348,7 @@ TEST_F(RestoreOpTest, RestoreSimple) {
// The 2-d empty float tensor
{
MakeRestoreOp(DT_FLOAT);
- (*mutable_input(1).tensor).scalar<string>()() = tensor_names[13];
+ (*mutable_input(1).tensor).scalar<tstring>()() = tensor_names[13];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 0});
@@ -398,12 +398,12 @@ TEST_F(RestoreSliceOpTest, RestoreInt) {
// Input #0 is the file name
Tensor input_0(DT_STRING, TensorShape({}));
- input_0.scalar<string>()() = filename;
+ input_0.scalar<tstring>()() = filename;
inputs.push_back({nullptr, &input_0});
// Input #1 is the tensor name
Tensor input_1(DT_STRING, TensorShape({}));
- input_1.scalar<string>()() = tensor_name;
+ input_1.scalar<tstring>()() = tensor_name;
inputs.push_back({nullptr, &input_1});
// Input #2 is a 4x16 integer tensor.
diff --git a/tensorflow/core/kernels/restore_v2_op_test.cc b/tensorflow/core/kernels/restore_v2_op_test.cc
index 36631570c7b..22eb99d2153 100644
--- a/tensorflow/core/kernels/restore_v2_op_test.cc
+++ b/tensorflow/core/kernels/restore_v2_op_test.cc
@@ -105,7 +105,7 @@ class RestoreV2OpTest : public OpsTestBase {
// Input #0 is the file name
Tensor input_0(DT_STRING, TensorShape({}));
- input_0.scalar<string>()() = filename;
+ input_0.scalar<tstring>()() = filename;
inputs.push_back({nullptr, &input_0});
// Input #1 is the tensor names
@@ -213,7 +213,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 1-d integer tensor
{
MakeRestoreOp(DT_INT32);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[1];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[1];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({10});
@@ -225,7 +225,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d float tensor
{
MakeRestoreOp(DT_FLOAT);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[2];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[2];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
@@ -237,7 +237,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d double tensor
{
MakeRestoreOp(DT_DOUBLE);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[3];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[3];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
@@ -249,7 +249,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d qint8 tensor
{
MakeRestoreOp(DT_QINT8);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[4];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[4];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({3, 2});
@@ -261,7 +261,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d qint32 tensor
{
MakeRestoreOp(DT_QINT32);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[5];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[5];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 3});
@@ -274,7 +274,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 1-d uint8 tensor
{
MakeRestoreOp(DT_UINT8);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[6];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[6];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({11});
@@ -286,7 +286,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 1-d int8 tensor
{
MakeRestoreOp(DT_INT8);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[7];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[7];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({7});
@@ -298,7 +298,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 1-d int16 tensor
{
MakeRestoreOp(DT_INT16);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[8];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[8];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({7});
@@ -310,7 +310,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 1-d int64 tensor
{
MakeRestoreOp(DT_INT64);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[9];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[9];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({9});
@@ -322,7 +322,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d complex64 tensor
{
MakeRestoreOp(DT_COMPLEX64);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[10];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[10];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 3});
@@ -334,7 +334,7 @@ class RestoreV2OpTest : public OpsTestBase {
// The 2-d half tensor
{
MakeRestoreOp(DT_HALF);
- (*mutable_input(1).tensor).flat<string>()(0) = tensor_names[11];
+ (*mutable_input(1).tensor).flat<tstring>()(0) = tensor_names[11];
TF_ASSERT_OK(RunOpKernel());
Tensor* output = GetOutput(0);
TensorShape expected({2, 4});
diff --git a/tensorflow/core/kernels/save_op.cc b/tensorflow/core/kernels/save_op.cc
index f87e0fa0e9c..f53976cae28 100644
--- a/tensorflow/core/kernels/save_op.cc
+++ b/tensorflow/core/kernels/save_op.cc
@@ -62,8 +62,8 @@ class ShardedFilenameOp : public OpKernel {
}
Tensor* out = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &out));
- out->scalar<string>()() = strings::Printf(
- "%s-%05d-of-%05d", ctx->input(0).scalar<string>()().c_str(),
+ out->scalar<tstring>()() = strings::Printf(
+ "%s-%05d-of-%05d", ctx->input(0).scalar<tstring>()().c_str(),
ctx->input(1).scalar<int32>()(), ctx->input(2).scalar<int32>()());
}
};
@@ -85,8 +85,8 @@ class ShardedFilespecOp : public OpKernel {
}
Tensor* out = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &out));
- out->scalar<string>()() = strings::Printf(
- "%s-\?\?\?\?\?-of-%05d", ctx->input(0).scalar<string>()().c_str(),
+ out->scalar<tstring>()() = strings::Printf(
+ "%s-\?\?\?\?\?-of-%05d", ctx->input(0).scalar<tstring>()().c_str(),
ctx->input(1).scalar<int32>()());
}
};
diff --git a/tensorflow/core/kernels/save_restore_tensor.cc b/tensorflow/core/kernels/save_restore_tensor.cc
index faafed367d3..f0a286721e1 100644
--- a/tensorflow/core/kernels/save_restore_tensor.cc
+++ b/tensorflow/core/kernels/save_restore_tensor.cc
@@ -70,7 +70,7 @@ void SaveTensors(
"shapes and slices but got ",
tensor_shapes_and_slices_t.NumElements()));
tensor_shapes_and_slices_ptr =
- tensor_shapes_and_slices_t.flat<string>().data();
+ tensor_shapes_and_slices_t.flat<tstring>().data();
}
OP_REQUIRES(context, context->num_inputs() == N + kFixedInputs,
errors::InvalidArgument("Expected totally ", N + kFixedInputs,
@@ -79,13 +79,13 @@ void SaveTensors(
N, " names, but received ",
context->num_inputs(), " inputs"));
- VLOG(1) << "About to save tensors to file " << filename_t.flat<string>()(0)
+ VLOG(1) << "About to save tensors to file " << filename_t.flat<tstring>()(0)
<< "...";
- checkpoint::TensorSliceWriter writer(filename_t.flat<string>()(0),
+ checkpoint::TensorSliceWriter writer(filename_t.flat<tstring>()(0),
std::move(builder_func));
Status s;
- auto tensor_names_flat = tensor_names_t.flat<string>();
+ auto tensor_names_flat = tensor_names_t.flat<tstring>();
// Process tensors in sorted name order. This allows us to avoid seeking
// during restoration in the common case where we are restoring a full
@@ -153,10 +153,10 @@ void RestoreTensor(OpKernelContext* context,
"Input 0 (file_pattern) must be a string scalar; got a tensor of ",
size, "elements"));
}
- const string& file_pattern = file_pattern_t.flat<string>()(0);
+ const string& file_pattern = file_pattern_t.flat<tstring>()(0);
const Tensor& tensor_name_t = context->input(1);
- const string& tensor_name = tensor_name_t.flat<string>()(restore_index);
+ const string& tensor_name = tensor_name_t.flat<tstring>()(restore_index);
// If we cannot find a cached reader we will allocate our own.
std::unique_ptr<checkpoint::TensorSliceReader> allocated_reader;
@@ -192,7 +192,7 @@ void RestoreTensor(OpKernelContext* context,
TensorShape output_shape(saved_shape);
TensorSlice slice_to_load(saved_shape.dims());
if (restore_slice) {
- const string& shape_spec = context->input(2).flat<string>()(restore_index);
+ const string& shape_spec = context->input(2).flat<tstring>()(restore_index);
if (!shape_spec.empty()) {
TensorShape parsed_shape;
OP_REQUIRES_OK(context, checkpoint::ParseShapeAndSlice(
@@ -318,10 +318,10 @@ Status RestoreTensorsV2(OpKernelContext* context, const Tensor& prefix,
const Tensor& tensor_names,
const Tensor& shape_and_slices,
gtl::ArraySlice<DataType> dtypes) {
- const string& prefix_string = prefix.scalar<string>()();
+ const string& prefix_string = prefix.scalar<tstring>()();
- const auto& tensor_names_flat = tensor_names.flat<string>();
- const auto& shape_and_slices_flat = shape_and_slices.flat<string>();
+ const auto& tensor_names_flat = tensor_names.flat<tstring>();
+ const auto& shape_and_slices_flat = shape_and_slices.flat<tstring>();
// Sort lookup keys to improve locality when reading multiple tensors.
std::vector<size_t> sorted_name_idx(tensor_names_flat.size());
diff --git a/tensorflow/core/kernels/save_restore_v2_ops.cc b/tensorflow/core/kernels/save_restore_v2_ops.cc
index ed1195c0535..512fd9bebfe 100644
--- a/tensorflow/core/kernels/save_restore_v2_ops.cc
+++ b/tensorflow/core/kernels/save_restore_v2_ops.cc
@@ -101,9 +101,9 @@ class SaveV2 : public OpKernel {
const int kFixedInputs = 3; // Prefix, tensor names, shape_and_slices.
const int num_tensors = static_cast<int>(tensor_names.NumElements());
- const string& prefix_string = prefix.scalar<string>()();
- const auto& tensor_names_flat = tensor_names.flat<string>();
- const auto& shape_and_slices_flat = shape_and_slices.flat<string>();
+ const string& prefix_string = prefix.scalar<tstring>()();
+ const auto& tensor_names_flat = tensor_names.flat<tstring>();
+ const auto& shape_and_slices_flat = shape_and_slices.flat<tstring>();
BundleWriter writer(Env::Default(), prefix_string);
OP_REQUIRES_OK(context, writer.status());
@@ -157,7 +157,7 @@ class RestoreV2 : public OpKernel {
ValidateInputs(false /* not save op */, context, prefix, tensor_names,
shape_and_slices);
- const string& prefix_string = prefix.scalar<string>()();
+ const string& prefix_string = prefix.scalar<tstring>()();
// Intention: we plan to use the RestoreV2 op as a backward-compatible
// reader as we upgrade to the V2 format. This allows transparent upgrade.
@@ -215,7 +215,7 @@ class MergeV2Checkpoints : public OpKernel {
const gtl::ArraySlice<string> input_prefixes =
gtl::ArraySlice<string>(checkpoint_prefixes.flat<string>());
Env* env = Env::Default();
- const string& merged_prefix = destination_prefix.scalar<string>()();
+ const string& merged_prefix = destination_prefix.scalar<tstring>()();
OP_REQUIRES_OK(
context, tensorflow::MergeBundles(env, input_prefixes, merged_prefix));
diff --git a/tensorflow/core/kernels/sdca_ops.cc b/tensorflow/core/kernels/sdca_ops.cc
index d0e0b15da78..4fdb7d1e257 100644
--- a/tensorflow/core/kernels/sdca_ops.cc
+++ b/tensorflow/core/kernels/sdca_ops.cc
@@ -312,7 +312,7 @@ class SdcaFprint : public OpKernel {
OP_REQUIRES_OK(context, context->allocate_output(
0, TensorShape({num_elements, 2}), &out));
- const auto in_values = input.flat<string>();
+ const auto in_values = input.flat<tstring>();
auto out_values = out->matrix<int64>();
for (int64 i = 0; i < num_elements; ++i) {
diff --git a/tensorflow/core/kernels/session_ops.cc b/tensorflow/core/kernels/session_ops.cc
index f2dd2812b53..d83a714452f 100644
--- a/tensorflow/core/kernels/session_ops.cc
+++ b/tensorflow/core/kernels/session_ops.cc
@@ -57,7 +57,7 @@ class GetSessionHandleOp : public OpKernel {
handle->scalar<ResourceHandle>()() = resource_handle;
} else {
// Legacy behavior in V1.
- handle->flat<string>().setConstant(tk.GetHandle(name()));
+ handle->flat<tstring>().setConstant(tk.GetHandle(name()));
}
}
@@ -110,7 +110,7 @@ class GetSessionTensorOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor& handle = ctx->input(0);
- const string& name = handle.scalar<string>()();
+ const string& name = handle.scalar<tstring>()();
Tensor val;
OP_REQUIRES_OK(ctx, ctx->session_state()->GetTensor(name, &val));
ctx->set_output(0, val);
@@ -153,7 +153,7 @@ class DeleteSessionTensorOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor& handle = ctx->input(0);
- const string& name = handle.scalar<string>()();
+ const string& name = handle.scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->session_state()->DeleteTensor(name));
}
diff --git a/tensorflow/core/kernels/sparse_cross_op.cc b/tensorflow/core/kernels/sparse_cross_op.cc
index 8e92c9e5517..a6a4060b26e 100644
--- a/tensorflow/core/kernels/sparse_cross_op.cc
+++ b/tensorflow/core/kernels/sparse_cross_op.cc
@@ -78,7 +78,7 @@ template <>
int64 SparseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
const int64 start = feature_start_indices_[batch];
if (DT_STRING == values_.dtype())
- return Fingerprint64(values_.vec<string>().data()[start + n]);
+ return Fingerprint64(values_.vec<tstring>().data()[start + n]);
return values_.vec<int64>().data()[start + n];
}
@@ -87,7 +87,7 @@ template <>
string SparseTensorColumn<string>::Feature(int64 batch, int64 n) const {
const int64 start = feature_start_indices_[batch];
if (DT_STRING == values_.dtype())
- return values_.vec<string>().data()[start + n];
+ return values_.vec<tstring>().data()[start + n];
return std::to_string(values_.vec<int64>().data()[start + n]);
}
@@ -95,7 +95,7 @@ template <>
StringPiece SparseTensorColumn<StringPiece>::Feature(int64 batch,
int64 n) const {
const int64 start = feature_start_indices_[batch];
- return values_.vec<string>().data()[start + n];
+ return values_.vec<tstring>().data()[start + n];
}
// A column that is backed by a dense tensor.
@@ -118,21 +118,21 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
template <>
int64 DenseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
if (DT_STRING == tensor_.dtype())
- return Fingerprint64(tensor_.matrix<string>()(batch, n));
+ return Fingerprint64(tensor_.matrix<tstring>()(batch, n));
return tensor_.matrix<int64>()(batch, n);
}
// Internal type is string or StringPiece when using StringCrosser.
template <>
string DenseTensorColumn<string>::Feature(int64 batch, int64 n) const {
- if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
+ if (DT_STRING == tensor_.dtype()) return tensor_.matrix<tstring>()(batch, n);
return std::to_string(tensor_.matrix<int64>()(batch, n));
}
template <>
StringPiece DenseTensorColumn<StringPiece>::Feature(int64 batch,
int64 n) const {
- return tensor_.matrix<string>()(batch, n);
+ return tensor_.matrix<tstring>()(batch, n);
}
// Updates Output tensors with sparse crosses.
diff --git a/tensorflow/core/kernels/stack.cc b/tensorflow/core/kernels/stack.cc
index d6a79049277..af8f760d47f 100644
--- a/tensorflow/core/kernels/stack.cc
+++ b/tensorflow/core/kernels/stack.cc
@@ -134,8 +134,8 @@ Status GetStack(OpKernelContext* ctx, Stack** stack) {
"Stack handle must have two elements, but had shape: ",
Tstack_handle.shape().DebugString());
}
- const string& container = Tstack_handle.flat<string>()(0);
- const string& stack_name = Tstack_handle.flat<string>()(1);
+ const string& container = Tstack_handle.flat<tstring>()(0);
+ const string& stack_name = Tstack_handle.flat<tstring>()(1);
string key = strings::StrCat(container, stack_name);
ResourceMgr* rm = ctx->resource_manager();
if (rm == nullptr) {
@@ -196,7 +196,7 @@ void StackOp::Compute(OpKernelContext* ctx) {
OP_REQUIRES_OK(ctx, ctx->allocate_temp(tensorflow::DT_STRING,
tensorflow::TensorShape({2}),
&stack->handle_, alloc_attr));
- auto handle = stack->handle_.flat<string>();
+ auto handle = stack->handle_.flat<tstring>();
handle(0) = kContainer;
handle(1) = std::move(stack_name);
ctx->set_output_ref(0, stack->mu(), &stack->handle_);
diff --git a/tensorflow/core/kernels/string_format_op.cc b/tensorflow/core/kernels/string_format_op.cc
index e4a1887f8d3..e42854cedd3 100644
--- a/tensorflow/core/kernels/string_format_op.cc
+++ b/tensorflow/core/kernels/string_format_op.cc
@@ -50,7 +50,7 @@ class StringFormatOp : public OpKernel {
strings::StrAppend(&msg, split_template_[i + 1].c_str());
}
- formatted_string->scalar<string>()() = msg;
+ formatted_string->scalar<tstring>()() = msg;
}
private:
diff --git a/tensorflow/core/kernels/string_join_op.cc b/tensorflow/core/kernels/string_join_op.cc
index 4b9c19da691..5532f6d6fe9 100644
--- a/tensorflow/core/kernels/string_join_op.cc
+++ b/tensorflow/core/kernels/string_join_op.cc
@@ -42,7 +42,7 @@ class StringJoinOp : public OpKernel {
std::vector<TTypes<string>::ConstFlat> inputs;
for (const auto& input : input_list) {
- inputs.push_back(input.flat<string>());
+ inputs.push_back(input.flat<tstring>());
is_scalar.push_back(TensorShapeUtils::IsScalar(input.shape()));
if (!TensorShapeUtils::IsScalar(input.shape())) {
if (TensorShapeUtils::IsScalar(input_shape)) {
@@ -60,7 +60,7 @@ class StringJoinOp : public OpKernel {
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("output", input_shape,
&output_tensor));
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
std::vector<StringPiece> strings(input_list.size());
for (size_t i = 0; i < input_shape.num_elements(); ++i) {
diff --git a/tensorflow/core/kernels/string_length_op.cc b/tensorflow/core/kernels/string_length_op.cc
index 435a7abdcac..53a161353f0 100644
--- a/tensorflow/core/kernels/string_length_op.cc
+++ b/tensorflow/core/kernels/string_length_op.cc
@@ -34,7 +34,7 @@ class StringLengthOp : public OpKernel {
OP_REQUIRES_OK(context,
context->allocate_output(0, input.shape(), &output));
- auto src = input.flat<string>();
+ auto src = input.flat<tstring>();
auto dst = output->flat<int32>();
switch (unit_) {
diff --git a/tensorflow/core/kernels/string_lower_op.cc b/tensorflow/core/kernels/string_lower_op.cc
index e24eedcc3ae..07065d2777e 100644
--- a/tensorflow/core/kernels/string_lower_op.cc
+++ b/tensorflow/core/kernels/string_lower_op.cc
@@ -45,8 +45,8 @@ class StringLowerOp : public OpKernel {
OP_REQUIRES_OK(
ctx, ctx->allocate_output(0, input_tensor->shape(), &output_tensor));
- const auto input = input_tensor->flat<string>();
- auto output = output_tensor->flat<string>();
+ const auto input = input_tensor->flat<tstring>();
+ auto output = output_tensor->flat<tstring>();
if (encoding_.empty()) {
for (int64 i = 0; i < input.size(); ++i) {
diff --git a/tensorflow/core/kernels/string_ngrams_op.cc b/tensorflow/core/kernels/string_ngrams_op.cc
index 37a7aa956d0..430d91bef88 100644
--- a/tensorflow/core/kernels/string_ngrams_op.cc
+++ b/tensorflow/core/kernels/string_ngrams_op.cc
@@ -54,14 +54,14 @@ class StringNGramsOp : public tensorflow::OpKernel {
void Compute(tensorflow::OpKernelContext* context) override {
const tensorflow::Tensor* data;
OP_REQUIRES_OK(context, context->input("data", &data));
- const auto& input_data = data->flat<string>().data();
+ const auto& input_data = data->flat<tstring>().data();
const tensorflow::Tensor* splits;
OP_REQUIRES_OK(context, context->input("data_splits", &splits));
const auto& splits_vec = splits->flat<SPLITS_TYPE>();
// If there is no data or size, return an empty RT.
- if (data->flat<string>().size() == 0 || splits_vec.size() == 0) {
+ if (data->flat<tstring>().size() == 0 || splits_vec.size() == 0) {
tensorflow::Tensor* empty;
OP_REQUIRES_OK(context,
context->allocate_output(0, data->shape(), &empty));
@@ -93,7 +93,7 @@ class StringNGramsOp : public tensorflow::OpKernel {
context,
context->allocate_output(
0, TensorShape({ngrams_splits_data[num_batch_items]}), &ngrams));
- auto ngrams_data = ngrams->flat<string>().data();
+ auto ngrams_data = ngrams->flat<tstring>().data();
for (int i = 0; i < num_batch_items; ++i) {
auto data_start = &input_data[splits_vec(i)];
diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc
index 3884370a6c6..d6d27debf89 100644
--- a/tensorflow/core/kernels/string_split_op.cc
+++ b/tensorflow/core/kernels/string_split_op.cc
@@ -178,7 +178,7 @@ class StringSplitOp : public OpKernel {
errors::InvalidArgument("input must be a vector, got shape: ",
input_tensor->shape().DebugString()));
- const auto input_vec = input_tensor->vec<string>();
+ const auto input_vec = input_tensor->vec<tstring>();
const int64 batch_size = input_vec.dimension(0);
const Tensor* delimiter_tensor;
@@ -220,7 +220,7 @@ class StringSplitOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t));
auto sp_indices = sp_indices_t->matrix<int64>();
- auto sp_tokens = sp_tokens_t->vec<string>();
+ auto sp_tokens = sp_tokens_t->vec<tstring>();
auto sp_shape = sp_shape_t->vec<int64>();
sp_shape(0) = batch_size;
sp_shape(1) = max_num_entries;
@@ -253,7 +253,7 @@ class StringSplitV2Op : public OpKernel {
errors::InvalidArgument("input must be a vector, got shape: ",
input_tensor->shape().DebugString()));
- const auto input_vec = input_tensor->vec<string>();
+ const auto input_vec = input_tensor->vec<tstring>();
const int64 batch_size = input_vec.dimension(0);
const Tensor* sep_tensor;
@@ -261,7 +261,7 @@ class StringSplitV2Op : public OpKernel {
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sep_tensor->shape()),
errors::InvalidArgument("sep must be a scalar, got shape: ",
sep_tensor->shape().DebugString()));
- const auto sep_vec = sep_tensor->flat<string>();
+ const auto sep_vec = sep_tensor->flat<tstring>();
StringPiece sep(sep_vec(0));
std::vector<StringPiece> tokens;
// Guess that we'll be unpacking a handful of tokens per example.
@@ -290,7 +290,7 @@ class StringSplitV2Op : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t));
auto sp_indices = sp_indices_t->matrix<int64>();
- auto sp_tokens = sp_tokens_t->vec<string>();
+ auto sp_tokens = sp_tokens_t->vec<tstring>();
auto sp_shape = sp_shape_t->vec<int64>();
sp_shape(0) = batch_size;
sp_shape(1) = max_num_entries;
diff --git a/tensorflow/core/kernels/string_split_op_test.cc b/tensorflow/core/kernels/string_split_op_test.cc
index 58ad61adc86..4494cf9dcf3 100644
--- a/tensorflow/core/kernels/string_split_op_test.cc
+++ b/tensorflow/core/kernels/string_split_op_test.cc
@@ -57,7 +57,7 @@ const char* lines[] = {
Tensor GetTestTensor(int batch) {
const int sz = TF_ARRAYSIZE(lines);
Tensor t(DT_STRING, {batch});
- auto s = t.flat<string>();
+ auto s = t.flat<tstring>();
for (int i = 0; i < batch; ++i) {
s(i) = lines[i % sz];
}
@@ -67,7 +67,7 @@ Tensor GetTestTensor(int batch) {
Graph* SetupStringSplitGraph(const Tensor& input) {
Graph* g = new Graph(OpRegistry::Global());
Tensor delim(DT_STRING, TensorShape({}));
- delim.flat<string>().setConstant(" ");
+ delim.flat<tstring>().setConstant(" ");
TF_CHECK_OK(NodeBuilder("string_split_op", "StringSplit")
.Input(test::graph::Constant(g, input))
@@ -98,7 +98,7 @@ BENCHMARK(BM_StringSplit)
Graph* SetupStringSplitV2Graph(const Tensor& input) {
Graph* g = new Graph(OpRegistry::Global());
Tensor sep(DT_STRING, TensorShape({}));
- sep.flat<string>().setConstant(" ");
+ sep.flat<tstring>().setConstant(" ");
TF_CHECK_OK(NodeBuilder("string_split_op", "StringSplitV2")
.Input(test::graph::Constant(g, input))
diff --git a/tensorflow/core/kernels/string_strip_op.cc b/tensorflow/core/kernels/string_strip_op.cc
index 544dca96ba7..715ec271db5 100644
--- a/tensorflow/core/kernels/string_strip_op.cc
+++ b/tensorflow/core/kernels/string_strip_op.cc
@@ -37,8 +37,8 @@ class StringStripOp : public OpKernel {
OP_REQUIRES_OK(
ctx, ctx->allocate_output(0, input_tensor->shape(), &output_tensor));
- const auto input = input_tensor->flat<string>();
- auto output = output_tensor->flat<string>();
+ const auto input = input_tensor->flat<tstring>();
+ auto output = output_tensor->flat<tstring>();
for (int64 i = 0; i < input.size(); ++i) {
StringPiece entry(input(i));
diff --git a/tensorflow/core/kernels/string_to_hash_bucket_op.cc b/tensorflow/core/kernels/string_to_hash_bucket_op.cc
index 10fc6ee5434..1505ddbb9bc 100644
--- a/tensorflow/core/kernels/string_to_hash_bucket_op.cc
+++ b/tensorflow/core/kernels/string_to_hash_bucket_op.cc
@@ -33,7 +33,7 @@ class LegacyStringToHashBucketOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(context, context->input("string_tensor", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
diff --git a/tensorflow/core/kernels/string_to_hash_bucket_op.h b/tensorflow/core/kernels/string_to_hash_bucket_op.h
index 62ef35bbba4..8647695cf46 100644
--- a/tensorflow/core/kernels/string_to_hash_bucket_op.h
+++ b/tensorflow/core/kernels/string_to_hash_bucket_op.h
@@ -36,7 +36,7 @@ class StringToHashBucketOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(context, context->input("input", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
@@ -78,7 +78,7 @@ class StringToKeyedHashBucketOp : public OpKernel {
void Compute(OpKernelContext* context) override {
const Tensor* input_tensor;
OP_REQUIRES_OK(context, context->input("input", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
diff --git a/tensorflow/core/kernels/string_to_number_op.cc b/tensorflow/core/kernels/string_to_number_op.cc
index 22742dd38e5..8340f35428b 100644
--- a/tensorflow/core/kernels/string_to_number_op.cc
+++ b/tensorflow/core/kernels/string_to_number_op.cc
@@ -40,7 +40,7 @@ class StringToNumberOp : public OpKernel {
// underlying storage.
const Tensor* input_tensor;
OP_REQUIRES_OK(context, context->input("string_tensor", &input_tensor));
- const auto& input_flat = input_tensor->flat<string>();
+ const auto& input_flat = input_tensor->flat<tstring>();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
diff --git a/tensorflow/core/kernels/string_upper_op.cc b/tensorflow/core/kernels/string_upper_op.cc
index f2a1d33e7a6..d9f088a7b78 100644
--- a/tensorflow/core/kernels/string_upper_op.cc
+++ b/tensorflow/core/kernels/string_upper_op.cc
@@ -45,8 +45,8 @@ class StringUpperOp : public OpKernel {
OP_REQUIRES_OK(
ctx, ctx->allocate_output(0, input_tensor->shape(), &output_tensor));
- const auto input = input_tensor->flat<string>();
- auto output = output_tensor->flat<string>();
+ const auto input = input_tensor->flat<tstring>();
+ auto output = output_tensor->flat<tstring>();
if (encoding_.empty()) {
for (int64 i = 0; i < input.size(); ++i) {
StringPiece entry(input(i));
diff --git a/tensorflow/core/kernels/substr_op.cc b/tensorflow/core/kernels/substr_op.cc
index 77b16b9384d..458d67ccc5e 100644
--- a/tensorflow/core/kernels/substr_op.cc
+++ b/tensorflow/core/kernels/substr_op.cc
@@ -59,13 +59,13 @@ class SubstrOp : public OpKernel {
// Do not need to do broadcasting
// Reshape input
- auto input = input_tensor.flat<string>();
+ auto input = input_tensor.flat<tstring>();
// Allocate output
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output("output", input_tensor.shape(),
&output_tensor));
- auto output = output_tensor->flat<string>();
+ auto output = output_tensor->flat<tstring>();
if (is_scalar) {
// Perform Op with scalar pos/len
const T pos =
@@ -141,8 +141,8 @@ class SubstrOp : public OpKernel {
switch (ndims) {
case 1: {
// Reshape tensors according to BCast results
- auto input = input_tensor.shaped<string, 1>(bcast.x_reshape());
- auto output = output_tensor->shaped<string, 1>(bcast.result_shape());
+ auto input = input_tensor.shaped<tstring, 1>(bcast.x_reshape());
+ auto output = output_tensor->shaped<tstring, 1>(bcast.result_shape());
auto pos_shaped = pos_tensor.shaped<T, 1>(bcast.y_reshape());
auto len_shaped = len_tensor.shaped<T, 1>(bcast.y_reshape());
@@ -204,8 +204,8 @@ class SubstrOp : public OpKernel {
}
case 2: {
// Reshape tensors according to BCast results
- auto input = input_tensor.shaped<string, 2>(bcast.x_reshape());
- auto output = output_tensor->shaped<string, 2>(bcast.result_shape());
+ auto input = input_tensor.shaped<tstring, 2>(bcast.x_reshape());
+ auto output = output_tensor->shaped<tstring, 2>(bcast.result_shape());
auto pos_shaped = pos_tensor.shaped<T, 2>(bcast.y_reshape());
auto len_shaped = len_tensor.shaped<T, 2>(bcast.y_reshape());
diff --git a/tensorflow/core/kernels/substr_op_test.cc b/tensorflow/core/kernels/substr_op_test.cc
index ea6b1ed5006..3aebfe3a212 100644
--- a/tensorflow/core/kernels/substr_op_test.cc
+++ b/tensorflow/core/kernels/substr_op_test.cc
@@ -115,7 +115,7 @@ const char* const kUTF8Unit = "UTF8_CHAR";
Tensor GetTestTensor(int batch) {
const int sz = TF_ARRAYSIZE(ascii_lines);
Tensor t(DT_STRING, {batch});
- auto s = t.flat<string>();
+ auto s = t.flat<tstring>();
for (int i = 0; i < batch; ++i) {
s(i) = ascii_lines[i % sz];
}
@@ -125,7 +125,7 @@ Tensor GetTestTensor(int batch) {
Tensor GetTestUTF8Tensor(int batch) {
const int sz = TF_ARRAYSIZE(unicode_lines);
Tensor t(DT_STRING, {batch});
- auto s = t.flat<string>();
+ auto s = t.flat<tstring>();
for (int i = 0; i < batch; ++i) {
s(i) = unicode_lines[i % sz];
}
diff --git a/tensorflow/core/kernels/summary_audio_op.cc b/tensorflow/core/kernels/summary_audio_op.cc
index f5ddb9081d6..fbb1c2c6473 100644
--- a/tensorflow/core/kernels/summary_audio_op.cc
+++ b/tensorflow/core/kernels/summary_audio_op.cc
@@ -44,7 +44,7 @@ class SummaryAudioOp : public OpKernel {
OP_REQUIRES(c, tensor.dims() >= 2 && tensor.dims() <= 3,
errors::InvalidArgument("Tensor must be 3-D or 2-D, got: ",
tensor.shape().DebugString()));
- const string& base_tag = tag.scalar<string>()();
+ const string& base_tag = tag.scalar<tstring>()();
float sample_rate = sample_rate_attr_;
if (!has_sample_rate_attr_) {
diff --git a/tensorflow/core/kernels/summary_audio_op_test.cc b/tensorflow/core/kernels/summary_audio_op_test.cc
index 1b957c548b6..7c6ec045b2d 100644
--- a/tensorflow/core/kernels/summary_audio_op_test.cc
+++ b/tensorflow/core/kernels/summary_audio_op_test.cc
@@ -93,7 +93,7 @@ TEST_F(SummaryAudioOpTest, Basic3D) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
CheckAndRemoveEncodedAudio(&summary);
EXPECT_SummaryMatches(summary, R"(
@@ -127,7 +127,7 @@ TEST_F(SummaryAudioOpTest, Basic2D) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
CheckAndRemoveEncodedAudio(&summary);
EXPECT_SummaryMatches(summary, R"(
diff --git a/tensorflow/core/kernels/summary_image_op.cc b/tensorflow/core/kernels/summary_image_op.cc
index 68f17c2e78d..bfba449c782 100644
--- a/tensorflow/core/kernels/summary_image_op.cc
+++ b/tensorflow/core/kernels/summary_image_op.cc
@@ -61,7 +61,7 @@ class SummaryImageOp : public OpKernel {
errors::InvalidArgument(
"Tensor must be 4-D with last dim 1, 3, or 4, not ",
tensor.shape().DebugString()));
- const string& base_tag = tags.scalar<string>()();
+ const string& base_tag = tags.scalar<tstring>()();
OP_REQUIRES(c,
tensor.dim_size(0) < (1LL << 31) &&
diff --git a/tensorflow/core/kernels/summary_image_op_test.cc b/tensorflow/core/kernels/summary_image_op_test.cc
index 74e0d092c2d..be8e44d7511 100644
--- a/tensorflow/core/kernels/summary_image_op_test.cc
+++ b/tensorflow/core/kernels/summary_image_op_test.cc
@@ -87,7 +87,7 @@ TEST_F(SummaryImageOpTest, ThreeGrayImagesOutOfFive4dInput) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
CheckAndRemoveEncodedImages(&summary);
EXPECT_SummaryMatches(summary, R"(
@@ -110,7 +110,7 @@ TEST_F(SummaryImageOpTest, OneGrayImage4dInput) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
CheckAndRemoveEncodedImages(&summary);
EXPECT_SummaryMatches(summary, R"(
@@ -142,7 +142,7 @@ TEST_F(SummaryImageOpTest, OneColorImage4dInput) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
CheckAndRemoveEncodedImages(&summary);
EXPECT_SummaryMatches(summary, R"(
diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc
index e17e28efc63..7f888da69d6 100644
--- a/tensorflow/core/kernels/summary_kernels.cc
+++ b/tensorflow/core/kernels/summary_kernels.cc
@@ -38,13 +38,13 @@ class CreateSummaryFileWriterOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* tmp;
OP_REQUIRES_OK(ctx, ctx->input("logdir", &tmp));
- const string logdir = tmp->scalar<string>()();
+ const string logdir = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("max_queue", &tmp));
const int32 max_queue = tmp->scalar<int32>()();
OP_REQUIRES_OK(ctx, ctx->input("flush_millis", &tmp));
const int32 flush_millis = tmp->scalar<int32>()();
OP_REQUIRES_OK(ctx, ctx->input("filename_suffix", &tmp));
- const string filename_suffix = tmp->scalar<string>()();
+ const string filename_suffix = tmp->scalar<tstring>()();
core::RefCountPtr<SummaryWriterInterface> s;
OP_REQUIRES_OK(ctx, LookupOrCreateResource<SummaryWriterInterface>(
@@ -67,13 +67,13 @@ class CreateSummaryDbWriterOp : public OpKernel {
void Compute(OpKernelContext* ctx) override {
const Tensor* tmp;
OP_REQUIRES_OK(ctx, ctx->input("db_uri", &tmp));
- const string db_uri = tmp->scalar<string>()();
+ const string db_uri = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("experiment_name", &tmp));
- const string experiment_name = tmp->scalar<string>()();
+ const string experiment_name = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("run_name", &tmp));
- const string run_name = tmp->scalar<string>()();
+ const string run_name = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("user_name", &tmp));
- const string user_name = tmp->scalar<string>()();
+ const string user_name = tmp->scalar<tstring>()();
core::RefCountPtr<SummaryWriterInterface> s;
OP_REQUIRES_OK(
@@ -132,9 +132,9 @@ class WriteSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
const int64 step = tmp->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
- const string& tag = tmp->scalar<string>()();
+ const string& tag = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("summary_metadata", &tmp));
- const string& serialized_metadata = tmp->scalar<string>()();
+ const string& serialized_metadata = tmp->scalar<tstring>()();
const Tensor* t;
OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
@@ -166,7 +166,7 @@ class WriteRawProtoSummaryOp : public OpKernel {
// Each Summary proto contains just one repeated field "value" of Value
// messages with the actual data, so repeated Merge() is equivalent to
// concatenating all the Value entries together into a single Event.
- const auto summary_pbs = t->flat<string>();
+ const auto summary_pbs = t->flat<tstring>();
for (int i = 0; i < summary_pbs.size(); ++i) {
if (!event->mutable_summary()->MergeFromString(summary_pbs(i))) {
ctx->CtxFailureWithWarning(errors::DataLoss(
@@ -191,7 +191,7 @@ class ImportEventOp : public OpKernel {
const Tensor* t;
OP_REQUIRES_OK(ctx, ctx->input("event", &t));
std::unique_ptr<Event> event{new Event};
- if (!ParseProtoUnlimited(event.get(), t->scalar<string>()())) {
+ if (!ParseProtoUnlimited(event.get(), t->scalar<tstring>()())) {
ctx->CtxFailureWithWarning(
errors::DataLoss("Bad tf.Event binary proto tensor string"));
return;
@@ -212,7 +212,7 @@ class WriteScalarSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
const int64 step = tmp->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
- const string& tag = tmp->scalar<string>()();
+ const string& tag = tmp->scalar<tstring>()();
const Tensor* t;
OP_REQUIRES_OK(ctx, ctx->input("value", &t));
@@ -234,7 +234,7 @@ class WriteHistogramSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
const int64 step = tmp->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
- const string& tag = tmp->scalar<string>()();
+ const string& tag = tmp->scalar<tstring>()();
const Tensor* t;
OP_REQUIRES_OK(ctx, ctx->input("values", &t));
@@ -262,7 +262,7 @@ class WriteImageSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
const int64 step = tmp->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
- const string& tag = tmp->scalar<string>()();
+ const string& tag = tmp->scalar<tstring>()();
const Tensor* bad_color;
OP_REQUIRES_OK(ctx, ctx->input("bad_color", &bad_color));
OP_REQUIRES(
@@ -297,7 +297,7 @@ class WriteAudioSummaryOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
const int64 step = tmp->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
- const string& tag = tmp->scalar<string>()();
+ const string& tag = tmp->scalar<tstring>()();
OP_REQUIRES_OK(ctx, ctx->input("sample_rate", &tmp));
const float sample_rate = tmp->scalar<float>()();
@@ -326,7 +326,7 @@ class WriteGraphSummaryOp : public OpKernel {
const int64 step = t->scalar<int64>()();
OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
std::unique_ptr<GraphDef> graph{new GraphDef};
- if (!ParseProtoUnlimited(graph.get(), t->scalar<string>()())) {
+ if (!ParseProtoUnlimited(graph.get(), t->scalar<tstring>()())) {
ctx->CtxFailureWithWarning(
errors::DataLoss("Bad tf.GraphDef binary proto tensor string"));
return;
diff --git a/tensorflow/core/kernels/summary_op.cc b/tensorflow/core/kernels/summary_op.cc
index 1053aa7d53a..a765825e5b0 100644
--- a/tensorflow/core/kernels/summary_op.cc
+++ b/tensorflow/core/kernels/summary_op.cc
@@ -47,7 +47,7 @@ class SummaryScalarOp : public OpKernel {
errors::InvalidArgument(
"tags and values not the same shape: ", tags.shape().DebugString(),
" != ", values.shape().DebugString(), SingleTag(tags)));
- auto Ttags = tags.flat<string>();
+ auto Ttags = tags.flat<tstring>();
auto Tvalues = values.flat<T>();
Summary s;
for (int i = 0; i < Ttags.size(); i++) {
@@ -64,7 +64,7 @@ class SummaryScalarOp : public OpKernel {
// If there's only one tag, include it in the error message
static string SingleTag(const Tensor& tags) {
if (tags.NumElements() == 1) {
- return strings::StrCat(" (tag '", tags.flat<string>()(0), "')");
+ return strings::StrCat(" (tag '", tags.flat<tstring>()(0), "')");
} else {
return "";
}
@@ -138,7 +138,7 @@ class SummaryMergeOp : public OpKernel {
std::unordered_set<string> tags;
for (int input_num = 0; input_num < c->num_inputs(); input_num++) {
const Tensor& in = c->input(input_num);
- auto in_vec = in.flat<string>();
+ auto in_vec = in.flat<tstring>();
for (int i = 0; i < in_vec.dimension(0); i++) {
const string& s_in = in_vec(i);
Summary summary_in;
diff --git a/tensorflow/core/kernels/summary_op_test.cc b/tensorflow/core/kernels/summary_op_test.cc
index 697c03a0082..9dcc98eeefe 100644
--- a/tensorflow/core/kernels/summary_op_test.cc
+++ b/tensorflow/core/kernels/summary_op_test.cc
@@ -88,7 +88,7 @@ TEST_F(SummaryScalarOpTest, SimpleDouble) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
EXPECT_SummaryMatches(summary, R"(
value { tag: 'tag1' simple_value: 1.0 }
value { tag: 'tag2' simple_value: -0.73 }
@@ -100,7 +100,7 @@ TEST_F(SummaryScalarOpTest, SimpleHalf) {
MakeOp(DT_HALF);
// Feed and run
- AddInputFromList<string>(TensorShape({3}), {"tag1", "tag2", "tag3"});
+ AddInputFromList<tstring>(TensorShape({3}), {"tag1", "tag2", "tag3"});
AddInputFromList<Eigen::half>(TensorShape({3}), {1.0, -2.0, 10000.0});
TF_ASSERT_OK(RunOpKernel());
@@ -108,7 +108,7 @@ TEST_F(SummaryScalarOpTest, SimpleHalf) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
EXPECT_SummaryMatches(summary, R"(
value { tag: 'tag1' simple_value: 1.0 }
value { tag: 'tag2' simple_value: -2.0 }
@@ -177,7 +177,7 @@ TEST_F(SummaryHistoOpTest, SimpleFloat) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
ASSERT_EQ(summary.value_size(), 1);
EXPECT_EQ(summary.value(0).tag(), "taghisto");
histogram::Histogram histo;
@@ -205,7 +205,7 @@ TEST_F(SummaryHistoOpTest, SimpleDouble) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
ASSERT_EQ(summary.value_size(), 1);
EXPECT_EQ(summary.value(0).tag(), "taghisto");
histogram::Histogram histo;
@@ -234,7 +234,7 @@ TEST_F(SummaryHistoOpTest, SimpleHalf) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
ASSERT_EQ(summary.value_size(), 1);
EXPECT_EQ(summary.value(0).tag(), "taghisto");
histogram::Histogram histo;
@@ -308,7 +308,7 @@ TEST_F(SummaryMergeOpTest, Simple) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
EXPECT_SummaryMatches(summary,
"value { tag: \"tag1\" simple_value: 1.0 } "
@@ -342,7 +342,7 @@ TEST_F(SummaryMergeOpTest, Simple_MultipleInputs) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
EXPECT_SummaryMatches(summary,
"value { tag: \"tag1\" simple_value: 1.0 } "
diff --git a/tensorflow/core/kernels/summary_tensor_op_test.cc b/tensorflow/core/kernels/summary_tensor_op_test.cc
index 55a0cb3ec5a..6bc4d150c2a 100644
--- a/tensorflow/core/kernels/summary_tensor_op_test.cc
+++ b/tensorflow/core/kernels/summary_tensor_op_test.cc
@@ -80,14 +80,14 @@ TEST_F(SummaryTensorOpV2Test, BasicPluginData) {
Tensor* out_tensor = GetOutput(0);
ASSERT_EQ(0, out_tensor->dims());
Summary summary;
- ParseProtoUnlimited(&summary, out_tensor->scalar<string>()());
+ ParseProtoUnlimited(&summary, out_tensor->scalar<tstring>()());
ASSERT_EQ(1, summary.value_size());
// Check the content of the tensor stored in the summary.
Tensor string_content_tensor;
CHECK(string_content_tensor.FromProto(summary.value(0).tensor()));
ASSERT_EQ("some string tensor content",
- string_content_tensor.scalar<string>()());
+ string_content_tensor.scalar<tstring>()());
// Check plugin-related data.
ASSERT_EQ("tag_foo", summary.value(0).tag());
diff --git a/tensorflow/core/kernels/tensor_array.cc b/tensorflow/core/kernels/tensor_array.cc
index 8e8faf89837..2bd6ac0b08d 100644
--- a/tensorflow/core/kernels/tensor_array.cc
+++ b/tensorflow/core/kernels/tensor_array.cc
@@ -91,8 +91,8 @@ Status TensorArray::CopyShapesFrom(TensorArray* rhs,
if (tensors_.size() != rhs->tensors_.size()) {
return errors::InvalidArgument(
"TensorArray sizes do not match during CopyShapesFrom: ",
- handle_.vec<string>()(1), " has size ", tensors_.size(), " but rhs ",
- rhs->handle_.vec<string>()(1), " has size ", rhs->tensors_.size());
+ handle_.vec<tstring>()(1), " has size ", tensors_.size(), " but rhs ",
+ rhs->handle_.vec<tstring>()(1), " has size ", rhs->tensors_.size());
}
for (std::size_t i = 0; i < tensors_.size(); ++i) {
// Skip "soft copy" of indices which have not been written.
diff --git a/tensorflow/core/kernels/tensor_array.h b/tensorflow/core/kernels/tensor_array.h
index 964b4631023..bea97d1a1f1 100644
--- a/tensorflow/core/kernels/tensor_array.h
+++ b/tensorflow/core/kernels/tensor_array.h
@@ -365,7 +365,7 @@ class TensorArray : public ResourceBase {
Status LockedReturnIfClosed() const EXCLUSIVE_LOCKS_REQUIRED(mu_) {
if (closed_) {
- return errors::InvalidArgument("TensorArray ", handle_.vec<string>()(1),
+ return errors::InvalidArgument("TensorArray ", handle_.vec<tstring>()(1),
" has already been closed.");
}
return Status::OK();
@@ -447,7 +447,7 @@ Status TensorArray::LockedWriteOrAggregate(OpKernelContext* ctx,
size_t index_size = static_cast<size_t>(index);
if (index < 0 || (!dynamic_size_ && index_size >= tensors_.size())) {
return errors::InvalidArgument(
- "TensorArray ", handle_.vec<string>()(1), ": Tried to write to index ",
+ "TensorArray ", handle_.vec<tstring>()(1), ": Tried to write to index ",
index, " but array is not resizeable and size is: ", tensors_.size());
}
if (dynamic_size_) {
@@ -464,14 +464,14 @@ Status TensorArray::LockedWriteOrAggregate(OpKernelContext* ctx,
Tensor* value_t = value->AccessTensor(ctx);
if (value_t->dtype() != dtype_) {
return errors::InvalidArgument(
- "TensorArray ", handle_.vec<string>()(1),
+ "TensorArray ", handle_.vec<tstring>()(1),
": Could not write to TensorArray index ", index,
" because the value dtype is ", DataTypeString(value_t->dtype()),
" but TensorArray dtype is ", DataTypeString(dtype_), ".");
}
if (!element_shape_.IsCompatibleWith(value_t->shape())) {
return errors::InvalidArgument(
- "TensorArray ", handle_.vec<string>()(1),
+ "TensorArray ", handle_.vec<tstring>()(1),
": Could not write to TensorArray index ", index,
" because the value shape is ", value_t->shape().DebugString(),
" which is incompatible with the TensorArray's inferred element "
@@ -482,13 +482,13 @@ Status TensorArray::LockedWriteOrAggregate(OpKernelContext* ctx,
}
if (t.read) {
- return errors::InvalidArgument("TensorArray ", handle_.vec<string>()(1),
+ return errors::InvalidArgument("TensorArray ", handle_.vec<tstring>()(1),
": Could not write to TensorArray index ",
index, " because it has already been read.");
}
if (!multiple_writes_aggregate_ && t.written) {
- return errors::InvalidArgument("TensorArray ", handle_.vec<string>()(1),
+ return errors::InvalidArgument("TensorArray ", handle_.vec<tstring>()(1),
": Could not write to TensorArray index ",
index,
" because it has already been written to.");
@@ -500,7 +500,7 @@ Status TensorArray::LockedWriteOrAggregate(OpKernelContext* ctx,
// Check that value_t shape matches t.shape
if (value_t->shape() != t.shape) {
return errors::InvalidArgument(
- "TensorArray ", handle_.vec<string>()(1),
+ "TensorArray ", handle_.vec<tstring>()(1),
": Could not aggregate to TensorArray index ", index,
" because the existing shape is ", t.shape.DebugString(),
" but the new input shape is ", value_t->shape().DebugString(), ".");
@@ -568,7 +568,7 @@ Status TensorArray::LockedRead(OpKernelContext* ctx, const int32 index,
element_shape = tensors_[index].shape;
} else if (!element_shape_.IsFullyDefined()) {
return errors::InvalidArgument(
- "TensorArray ", handle_.vec<string>()(1),
+ "TensorArray ", handle_.vec<tstring>()(1),
": Could not read from TensorArray index ", index,
". Furthermore, the element shape is not fully defined: ",
element_shape_.DebugString(),
@@ -598,7 +598,7 @@ Status TensorArray::LockedRead(OpKernelContext* ctx, const int32 index,
TensorAndState& t = tensors_[index];
if (t.cleared) {
- return errors::InvalidArgument("TensorArray ", handle_.vec<string>()(1),
+ return errors::InvalidArgument("TensorArray ", handle_.vec<tstring>()(1),
": Could not read index ", index,
" twice because it was cleared after a "
"previous read (perhaps try setting "
diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc
index d5c9470cc89..52162e94650 100644
--- a/tensorflow/core/kernels/tensor_array_ops.cc
+++ b/tensorflow/core/kernels/tensor_array_ops.cc
@@ -65,7 +65,7 @@ Status GetHandle(OpKernelContext* ctx, string* container, string* ta_handle) {
"Tensor array handle must be 2-element vector, but had shape: ",
tensor.shape().DebugString());
}
- auto h = tensor.flat<string>();
+ auto h = tensor.flat<tstring>();
*container = h(0);
*ta_handle = h(1);
}
@@ -194,7 +194,7 @@ class TensorArrayOp : public TensorArrayCreationOp {
return errors::InvalidArgument("Size should be >= 0.");
}
- auto handle = tensor_array_output_handle->flat<string>();
+ auto handle = tensor_array_output_handle->flat<tstring>();
string unique_tensor_array_name =
strings::StrCat(tensor_array_name_, "_",
TensorArray::tensor_array_counter.fetch_add(1));
@@ -301,7 +301,7 @@ class TensorArrayGradOp : public TensorArrayCreationOp {
string(StringPiece(resource.name()).substr(container.size()));
}
- auto output_handle = tensor_array_output_handle->flat<string>();
+ auto output_handle = tensor_array_output_handle->flat<tstring>();
output_handle(0) = "_tensor_array_grads";
output_handle(1) = strings::StrCat(tensor_array_name, "@", source_);
diff --git a/tensorflow/core/kernels/tensor_forest/resource_ops.cc b/tensorflow/core/kernels/tensor_forest/resource_ops.cc
index c225d83674f..0c7b9e91263 100644
--- a/tensorflow/core/kernels/tensor_forest/resource_ops.cc
+++ b/tensorflow/core/kernels/tensor_forest/resource_ops.cc
@@ -34,7 +34,7 @@ class TensorForestCreateTreeVariableOp : public OpKernel {
auto* const result = new TensorForestTreeResource();
- if (!result->InitFromSerialized(tree_config_t->scalar<string>()())) {
+ if (!result->InitFromSerialized(tree_config_t->scalar<tstring>()())) {
result->Unref();
OP_REQUIRES(context, false,
errors::InvalidArgument("Unable to parse tree config."));
@@ -63,7 +63,7 @@ class TensorForestTreeSerializeOp : public OpKernel {
Tensor* output_config_t = nullptr;
OP_REQUIRES_OK(
context, context->allocate_output(0, TensorShape(), &output_config_t));
- output_config_t->scalar<string>()() =
+ output_config_t->scalar<tstring>()() =
decision_tree_resource->decision_tree().SerializeAsString();
}
};
@@ -86,7 +86,7 @@ class TensorForestTreeDeserializeOp : public OpKernel {
decision_tree_resource->Reset();
if (!decision_tree_resource->InitFromSerialized(
- tree_config_t->scalar<string>()())) {
+ tree_config_t->scalar<tstring>()())) {
OP_REQUIRES(context, false,
errors::InvalidArgument("Unable to parse tree config."));
}
diff --git a/tensorflow/core/kernels/unicode_ops.cc b/tensorflow/core/kernels/unicode_ops.cc
index 59ebbedcd7f..0bb5f0f7ef6 100644
--- a/tensorflow/core/kernels/unicode_ops.cc
+++ b/tensorflow/core/kernels/unicode_ops.cc
@@ -295,10 +295,10 @@ class UnicodeTranscodeOp : public OpKernel {
} else {
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", input_tensor->shape(),
&output_tensor));
- output_tensor->flat<string>() = input_tensor->flat<string>();
+ output_tensor->flat<tstring>() = input_tensor->flat<tstring>();
}
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
bool found_any_format_error = false;
for (size_t i = 0; i < output_flat.size(); ++i) {
Transcode(&(output_flat(i)), input_encoder->converter_,
@@ -404,7 +404,7 @@ class UnicodeDecodeBaseOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor));
// Go through all the strings in `input`.
- const auto& input_vec = input_tensor->flat<string>();
+ const auto& input_vec = input_tensor->flat<tstring>();
std::unique_ptr<WrappedConverter> input_encoder =
absl::make_unique<WrappedConverter>();
@@ -538,7 +538,7 @@ class UnicodeEncodeOp : public OpKernel {
Tensor* output_tensor;
OP_REQUIRES_OK(context, context->allocate_output("output", output_shape,
&output_tensor));
- auto output_tensor_flat = output_tensor->flat<string>();
+ auto output_tensor_flat = output_tensor->flat<tstring>();
// Use a single index over the flattened input values tensor.
int idx = 0;
diff --git a/tensorflow/core/kernels/unsorted_segment_join_op.cc b/tensorflow/core/kernels/unsorted_segment_join_op.cc
index 4ab890c44bd..f0b9388f7cf 100644
--- a/tensorflow/core/kernels/unsorted_segment_join_op.cc
+++ b/tensorflow/core/kernels/unsorted_segment_join_op.cc
@@ -115,9 +115,9 @@ class UnsortedSegmentJoinOp : public OpKernel {
&output_tensor));
// Preprating flat tensors.
- auto output_flat = output_tensor->flat<string>();
+ auto output_flat = output_tensor->flat<tstring>();
auto flat_segment_id = segment_id.flat<INDICES_TYPE>();
- auto flat_input = input.flat<string>();
+ auto flat_input = input.flat<tstring>();
for (int i = 0; i < flat_segment_id.size(); i++) {
OP_REQUIRES(
diff --git a/tensorflow/core/kernels/whole_file_read_ops.cc b/tensorflow/core/kernels/whole_file_read_ops.cc
index b617b76a508..1e3b7fd6b30 100644
--- a/tensorflow/core/kernels/whole_file_read_ops.cc
+++ b/tensorflow/core/kernels/whole_file_read_ops.cc
@@ -135,14 +135,14 @@ class WriteFileOp : public OpKernel {
errors::InvalidArgument(
"Contents tensor must be scalar, but had shape: ",
contents_input->shape().DebugString()));
- const string& filename = filename_input->scalar<string>()();
+ const string& filename = filename_input->scalar<tstring>()();
const string dir(io::Dirname(filename));
if (!context->env()->FileExists(dir).ok()) {
OP_REQUIRES_OK(context, context->env()->RecursivelyCreateDir(dir));
}
OP_REQUIRES_OK(context,
WriteStringToFile(context->env(), filename,
- contents_input->scalar<string>()()));
+ contents_input->scalar<tstring>()()));
}
};
diff --git a/tensorflow/core/kernels/word2vec_kernels.cc b/tensorflow/core/kernels/word2vec_kernels.cc
index 3477445197a..42b70e92bab 100644
--- a/tensorflow/core/kernels/word2vec_kernels.cc
+++ b/tensorflow/core/kernels/word2vec_kernels.cc
@@ -209,14 +209,14 @@ class SkipgramOp : public OpKernel {
vocab_size_ = static_cast<int32>(1 + ordered.size());
Tensor word(DT_STRING, TensorShape({vocab_size_}));
Tensor freq(DT_INT32, TensorShape({vocab_size_}));
- word.flat<string>()(0) = "UNK";
+ word.flat<tstring>()(0) = "UNK";
static const int32 kUnkId = 0;
std::unordered_map<string, int32> word_id;
int64 total_counted = 0;
for (std::size_t i = 0; i < ordered.size(); ++i) {
const auto& w = ordered[i].first;
auto id = i + 1;
- word.flat<string>()(id) = w;
+ word.flat<tstring>()(id) = w;
auto word_count = ordered[i].second;
freq.flat<int32>()(id) = word_count;
total_counted += word_count;
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 4034e97e010..3f0f0c28569 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -3398,7 +3398,7 @@ REGISTER_OP("Fingerprint")
return errors::InvalidArgument("`method` must be rank 0: ",
method->shape());
}
- const string& method_string = method->scalar<string>()();
+ const string& method_string = method->scalar<tstring>()();
if (method_string != "farmhash64") {
return errors::InvalidArgument("Unsupported method: ", method_string);
}
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index 1f2edee9054..e2078e001e4 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -101,7 +101,7 @@ REGISTER_OP("RestoreV2")
const Tensor* shape_and_slices_tensor = c->input_tensor(2);
if (shape_and_slices_tensor) {
const auto& shape_and_slices_flat =
- shape_and_slices_tensor->flat<string>();
+ shape_and_slices_tensor->flat<tstring>();
if (shape_and_slices_flat.size() != c->num_outputs()) {
return errors::InvalidArgument(
"The number of shape_and_slice doesn't match tensor outputs.");
@@ -222,7 +222,7 @@ REGISTER_OP("RestoreSlice")
const Tensor* shape_and_slices_tensor = c->input_tensor(2);
if (shape_and_slices_tensor) {
const auto& shape_and_slice =
- shape_and_slices_tensor->flat<string>()(0);
+ shape_and_slices_tensor->flat<tstring>()(0);
if (shape_and_slice.empty()) {
c->set_output(0, c->UnknownShape());
} else {
diff --git a/tensorflow/core/summary/summary_db_writer.cc b/tensorflow/core/summary/summary_db_writer.cc
index b203d439ccf..1a9bd33a110 100644
--- a/tensorflow/core/summary/summary_db_writer.cc
+++ b/tensorflow/core/summary/summary_db_writer.cc
@@ -676,7 +676,7 @@ class SeriesWriter {
const Tensor& t) SQLITE_TRANSACTIONS_EXCLUDED(*db) {
if (t.dtype() == DT_STRING) {
if (t.dims() == 0) {
- return Update(db, step, computed_time, t, t.scalar<string>()(), rowid);
+ return Update(db, step, computed_time, t, t.scalar<tstring>()(), rowid);
} else {
SqliteTransaction txn(*db);
TF_RETURN_IF_ERROR(
@@ -735,7 +735,7 @@ class SeriesWriter {
)sql";
SqliteStatement inserter;
TF_RETURN_IF_ERROR(db->Prepare(inserter_sql, &inserter));
- auto flat = t.flat<string>();
+ auto flat = t.flat<tstring>();
for (int64 i = 0; i < flat.size(); ++i) {
inserter.BindInt(1, tensor_rowid);
inserter.BindInt(2, i);
@@ -751,7 +751,7 @@ class SeriesWriter {
unflushed_bytes_ = 0;
if (t.dtype() == DT_STRING) {
if (t.dims() == 0) {
- TF_RETURN_IF_ERROR(ReserveData(db, &txn, t.scalar<string>()().size()));
+ TF_RETURN_IF_ERROR(ReserveData(db, &txn, t.scalar<tstring>()().size()));
} else {
TF_RETURN_IF_ERROR(ReserveTensors(db, &txn, kReserveMinBytes));
}
@@ -1106,9 +1106,9 @@ class SummaryDbWriter : public SummaryWriterInterface {
// See tensorboard/plugins/image/summary.py and data_compat.py
Tensor t{DT_STRING, {3}};
auto img = s->mutable_image();
- t.flat<string>()(0) = strings::StrCat(img->width());
- t.flat<string>()(1) = strings::StrCat(img->height());
- t.flat<string>()(2) = std::move(*img->mutable_encoded_image_string());
+ t.flat<tstring>()(0) = strings::StrCat(img->width());
+ t.flat<tstring>()(1) = strings::StrCat(img->height());
+ t.flat<tstring>()(2) = std::move(*img->mutable_encoded_image_string());
int64 tag_id;
PatchPluginName(s->mutable_metadata(), kImagePluginName);
TF_RETURN_IF_ERROR(meta_.GetTagId(db_, now, e->wall_time(), s->tag(),
@@ -1120,8 +1120,8 @@ class SummaryDbWriter : public SummaryWriterInterface {
// See tensorboard/plugins/audio/summary.py and data_compat.py
Tensor t{DT_STRING, {1, 2}};
auto wav = s->mutable_audio();
- t.flat<string>()(0) = std::move(*wav->mutable_encoded_audio_string());
- t.flat<string>()(1) = "";
+ t.flat<tstring>()(0) = std::move(*wav->mutable_encoded_audio_string());
+ t.flat<tstring>()(1) = "";
int64 tag_id;
PatchPluginName(s->mutable_metadata(), kAudioPluginName);
TF_RETURN_IF_ERROR(meta_.GetTagId(db_, now, e->wall_time(), s->tag(),
diff --git a/tensorflow/core/summary/summary_file_writer_test.cc b/tensorflow/core/summary/summary_file_writer_test.cc
index 41060d7fe64..932ae80ab09 100644
--- a/tensorflow/core/summary/summary_file_writer_test.cc
+++ b/tensorflow/core/summary/summary_file_writer_test.cc
@@ -109,7 +109,7 @@ TEST_F(SummaryFileWriterTest, WriteTensor) {
"string_tensor_test",
[](SummaryWriterInterface* writer) {
Tensor hello(DT_STRING, TensorShape({}));
- hello.scalar<string>()() = "hello";
+ hello.scalar<tstring>()() = "hello";
TF_RETURN_IF_ERROR(writer->WriteTensor(
2, hello, "name", SummaryMetadata().SerializeAsString()));
TF_RETURN_IF_ERROR(writer->Flush());
diff --git a/tensorflow/core/util/batch_util.cc b/tensorflow/core/util/batch_util.cc
index e1c32cd0069..3d704c4cdee 100644
--- a/tensorflow/core/util/batch_util.cc
+++ b/tensorflow/core/util/batch_util.cc
@@ -107,8 +107,8 @@ void HandleSliceToElement(Tensor* parent, Tensor* element, int64 index,
template <>
void HandleSliceToElement<string>(Tensor* parent, Tensor* element, int64 index,
bool can_move) {
- auto parent_as_matrix = parent->flat_outer_dims<string>();
- auto element_flat = element->flat<string>();
+ auto parent_as_matrix = parent->flat_outer_dims<tstring>();
+ auto element_flat = element->flat<tstring>();
if (can_move) {
for (int64 i = 0; i < element->NumElements(); ++i) {
element_flat(i) = std::move(parent_as_matrix(index, i));
diff --git a/tensorflow/core/util/example_proto_fast_parsing.cc b/tensorflow/core/util/example_proto_fast_parsing.cc
index 06179d49a8b..4e49d4e6329 100644
--- a/tensorflow/core/util/example_proto_fast_parsing.cc
+++ b/tensorflow/core/util/example_proto_fast_parsing.cc
@@ -852,8 +852,8 @@ Status FastParseSerializedExample(
break;
}
case DT_STRING: {
- std::copy_n(in.flat<string>().data(), num_elements,
- out.flat<string>().data() + offset);
+ std::copy_n(in.flat<tstring>().data(), num_elements,
+ out.flat<tstring>().data() + offset);
break;
}
default:
@@ -1194,7 +1194,7 @@ Status FastParseExample(const Config& config,
}
case DT_STRING: {
std::move(buffer.bytes_list.begin(), buffer.bytes_list.end(),
- values->flat<string>().data() + offset);
+ values->flat<tstring>().data() + offset);
break;
}
default:
@@ -1578,7 +1578,7 @@ Status FastParseSingleExample(const Config& config,
case DT_STRING: {
*out = Tensor(out_dtype, out_shape);
CopyOrMoveBlock(bytes_list.begin(), bytes_list.end(),
- out->flat<string>().data());
+ out->flat<tstring>().data());
break;
}
default:
@@ -2079,7 +2079,7 @@ Status FastParseSequenceExample(
int64* out_int64 = nullptr;
switch (dtype) {
case DT_STRING:
- out_bytes = context_result->dense_values[t].flat<string>().data();
+ out_bytes = context_result->dense_values[t].flat<tstring>().data();
break;
case DT_FLOAT:
out_float = context_result->dense_values[t].flat<float>().data();
@@ -2113,7 +2113,7 @@ Status FastParseSequenceExample(
size_t num = 0;
switch (dtype) {
case DT_STRING:
- in_bytes = c.default_value.flat<string>().data();
+ in_bytes = c.default_value.flat<tstring>().data();
num = c.default_value.NumElements();
for (int p = 0; p < num; p++) {
*out_bytes++ = *in_bytes++;
@@ -2190,7 +2190,7 @@ Status FastParseSequenceExample(
int64* out_int64 = nullptr;
switch (dtype) {
case DT_STRING:
- out_bytes = context_result->sparse_values[t].flat<string>().data();
+ out_bytes = context_result->sparse_values[t].flat<tstring>().data();
break;
case DT_FLOAT:
out_float = context_result->sparse_values[t].flat<float>().data();
@@ -2281,7 +2281,7 @@ Status FastParseSequenceExample(
int64* out_int64 = nullptr;
switch (dtype) {
case DT_STRING:
- out_bytes = feature_list_result->dense_values[t].flat<string>().data();
+ out_bytes = feature_list_result->dense_values[t].flat<tstring>().data();
break;
case DT_FLOAT:
out_float = feature_list_result->dense_values[t].flat<float>().data();
@@ -2392,7 +2392,8 @@ Status FastParseSequenceExample(
int64* out_int64 = nullptr;
switch (dtype) {
case DT_STRING:
- out_bytes = feature_list_result->sparse_values[t].flat<string>().data();
+ out_bytes =
+ feature_list_result->sparse_values[t].flat<tstring>().data();
break;
case DT_FLOAT:
out_float = feature_list_result->sparse_values[t].flat<float>().data();
diff --git a/tensorflow/core/util/example_proto_helper_test.cc b/tensorflow/core/util/example_proto_helper_test.cc
index 1bf430b2c78..141c2400e91 100644
--- a/tensorflow/core/util/example_proto_helper_test.cc
+++ b/tensorflow/core/util/example_proto_helper_test.cc
@@ -57,7 +57,7 @@ class SingleExampleProtoToTensorsTest : public ::testing::Test {
string_dense_config.dtype = DT_STRING;
string_dense_config.shape = TensorShape({1});
string_dense_config.default_value = Tensor(DT_STRING, TensorShape({1}));
- string_dense_config.default_value.scalar<string>()() = "default";
+ string_dense_config.default_value.scalar<tstring>()() = "default";
dense_vec_.push_back(string_dense_config);
// Setup sparse feature configuration.
@@ -115,7 +115,7 @@ TEST_F(SingleExampleProtoToTensorsTest, SparseOnlyTrivial) {
const std::vector<Tensor>& string_tensor_vec = output_sparse_values_tmp[2];
EXPECT_EQ(1, string_tensor_vec.size());
- EXPECT_EQ("forty-two", string_tensor_vec[0].vec<string>()(0));
+ EXPECT_EQ("forty-two", string_tensor_vec[0].vec<tstring>()(0));
}
TEST_F(SingleExampleProtoToTensorsTest, SparseOnlyEmpty) {
@@ -143,7 +143,7 @@ TEST_F(SingleExampleProtoToTensorsTest, SparseOnlyEmpty) {
const std::vector<Tensor>& string_tensor_vec = output_sparse_values_tmp[2];
EXPECT_EQ(1, string_tensor_vec.size());
- EXPECT_EQ(0, string_tensor_vec[0].vec<string>().size());
+ EXPECT_EQ(0, string_tensor_vec[0].vec<tstring>().size());
}
TEST_F(SingleExampleProtoToTensorsTest, DenseOnlyTrivial) {
@@ -182,8 +182,8 @@ TEST_F(SingleExampleProtoToTensorsTest, DenseOnlyTrivial) {
EXPECT_EQ(1, float_dense_output.matrix<float>().size());
EXPECT_NEAR(4.2, float_dense_output.matrix<float>()(0, 0), 0.001);
- EXPECT_EQ(1, str_dense_output.matrix<string>().size());
- EXPECT_EQ("forty-two", str_dense_output.matrix<string>()(0, 0));
+ EXPECT_EQ(1, str_dense_output.matrix<tstring>().size());
+ EXPECT_EQ("forty-two", str_dense_output.matrix<tstring>()(0, 0));
}
TEST_F(SingleExampleProtoToTensorsTest, DenseOnlyDefaults) {
@@ -211,8 +211,8 @@ TEST_F(SingleExampleProtoToTensorsTest, DenseOnlyDefaults) {
EXPECT_EQ(1, float_dense_output.matrix<float>().size());
EXPECT_NEAR(0.0, float_dense_output.matrix<float>()(0, 0), 0.001);
- EXPECT_EQ(1, str_dense_output.matrix<string>().size());
- EXPECT_EQ("default", str_dense_output.matrix<string>()(0, 0));
+ EXPECT_EQ(1, str_dense_output.matrix<tstring>().size());
+ EXPECT_EQ("default", str_dense_output.matrix<tstring>()(0, 0));
}
} // namespace
diff --git a/tensorflow/core/util/sparse/sparse_tensor_test.cc b/tensorflow/core/util/sparse/sparse_tensor_test.cc
index 5ab0a3d084e..f2faad23313 100644
--- a/tensorflow/core/util/sparse/sparse_tensor_test.cc
+++ b/tensorflow/core/util/sparse/sparse_tensor_test.cc
@@ -181,7 +181,7 @@ TEST(SparseTensorTest, SparseTensorConstruction) {
Tensor vals(DT_STRING, TensorShape({N}));
auto ix_t = ix.matrix<int64>();
- auto vals_t = vals.vec<string>();
+ auto vals_t = vals.vec<tstring>();
vals_t = vals_c;
ix_t = ix_c;
@@ -362,7 +362,7 @@ TEST(SparseTensorTest, SparseTensorToDenseTensor) {
Tensor vals(DT_STRING, TensorShape({N}));
auto ix_t = GetSimpleIndexTensor(N, NDIM);
- auto vals_t = vals.vec<string>();
+ auto vals_t = vals.vec<tstring>();
ix.matrix<int64>() = ix_t;
@@ -402,7 +402,7 @@ TEST(SparseTensorTest, SparseTensorToLargerDenseTensor) {
Tensor vals(DT_STRING, TensorShape({N}));
auto ix_t = GetSimpleIndexTensor(N, NDIM);
- auto vals_t = vals.vec<string>();
+ auto vals_t = vals.vec<tstring>();
ix.matrix<int64>() = ix_t;
@@ -540,7 +540,7 @@ TEST(SparseTensorTest, Concat) {
auto ix_c = GetSimpleIndexTensor(N, NDIM);
auto ix_t = ix.matrix<int64>();
- auto vals_t = vals.vec<string>();
+ auto vals_t = vals.vec<tstring>();
ix_t = ix_c;
@@ -561,7 +561,7 @@ TEST(SparseTensorTest, Concat) {
TF_EXPECT_OK(concatted.IndicesValid());
auto conc_ix_t = concatted.indices().matrix<int64>();
- auto conc_vals_t = concatted.values().vec<string>();
+ auto conc_vals_t = concatted.values().vec<tstring>();
for (int n = 0; n < 4; ++n) {
for (int i = 0; i < N; ++i) {
@@ -750,7 +750,7 @@ static void BM_SparseReorderString(int iters, int N32, int NDIM32) {
TensorShape shape;
std::vector<int64> order;
auto ix_t = ix.matrix<int64>();
- auto vals_t = vals.vec<string>();
+ auto vals_t = vals.vec<tstring>();
for (int i = 0; i < N32; ++i) {
int len = rnd.Rand32() % 1000;
vals_t(i).resize(len);
diff --git a/tensorflow/python/framework/test_ops.cc b/tensorflow/python/framework/test_ops.cc
index 5d1386c26d7..550d5babcf7 100644
--- a/tensorflow/python/framework/test_ops.cc
+++ b/tensorflow/python/framework/test_ops.cc
@@ -96,13 +96,13 @@ class KernelLabelOp : public OpKernel {
ctx->allocate_output("result", TensorShape({}), &output));
switch (KL) {
case DEFAULT_LABEL:
- output->scalar<string>()() = "My label is: default";
+ output->scalar<tstring>()() = "My label is: default";
break;
case OVERLOAD_1_LABEL:
- output->scalar<string>()() = "My label is: overload_1";
+ output->scalar<tstring>()() = "My label is: overload_1";
break;
case OVERLOAD_2_LABEL:
- output->scalar<string>()() = "My label is: overload_2";
+ output->scalar<tstring>()() = "My label is: overload_2";
break;
}
}
@@ -676,7 +676,7 @@ class DevicePlacementOp : public OpKernel {
Tensor* output;
OP_REQUIRES_OK(ctx,
ctx->allocate_output("device", TensorShape({}), &output));
- output->scalar<string>()() = ctx->device()->name();
+ output->scalar<tstring>()() = ctx->device()->name();
}
};
diff --git a/tensorflow/python/kernel_tests/ackermann_op.cc b/tensorflow/python/kernel_tests/ackermann_op.cc
index d42ca6f662e..2d885b7a0f0 100644
--- a/tensorflow/python/kernel_tests/ackermann_op.cc
+++ b/tensorflow/python/kernel_tests/ackermann_op.cc
@@ -35,7 +35,7 @@ class AckermannOp : public OpKernel {
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, TensorShape(), &output_tensor));
- auto output = output_tensor->scalar<string>();
+ auto output = output_tensor->scalar<tstring>();
output() = "A(m, 0) == A(m-1, 1)";
}
diff --git a/tensorflow/tools/benchmark/benchmark_model.cc b/tensorflow/tools/benchmark/benchmark_model.cc
index e5187ab8727..7ebba437e4c 100644
--- a/tensorflow/tools/benchmark/benchmark_model.cc
+++ b/tensorflow/tools/benchmark/benchmark_model.cc
@@ -101,7 +101,7 @@ void CreateTensorsFromInputInfo(
if (!input.initialization_values.empty()) {
LOG(FATAL) << "Initialization values are not supported for strings";
}
- auto type_tensor = input_tensor.flat<string>();
+ auto type_tensor = input_tensor.flat<tstring>();
type_tensor = type_tensor.constant("");
break;
}
diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc
index 49e5cca461f..cc4078dfb85 100644
--- a/tensorflow/tools/graph_transforms/sparsify_gather.cc
+++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc
@@ -126,7 +126,7 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def,
if (node.name() == tensor_names_node) {
Tensor tensor_names_tensor;
TF_RETURN_IF_ERROR(GetNodeAttr(node, "value", &tensor_names_tensor));
- const auto& tensor_names_value = tensor_names_tensor.flat<string>();
+ const auto& tensor_names_value = tensor_names_tensor.flat<tstring>();
for (int i = 0; i < tensor_names_value.size(); i++) {
if (tensor_names_value(i) == GetMonolithicTensorKey(target_name)) {
offset = i;
@@ -144,7 +144,7 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def,
Tensor shape_and_slices_tensor;
TF_RETURN_IF_ERROR(GetNodeAttr(node, "value", &shape_and_slices_tensor));
const auto& shape_and_slices_value =
- shape_and_slices_tensor.flat<string>();
+ shape_and_slices_tensor.flat<tstring>();
*shape_slice_string = shape_and_slices_value(offset);
return Status::OK();
}
diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
index b8d6ba00de8..dfe8fb0e32b 100644
--- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
+++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc
@@ -116,7 +116,7 @@ class SparsifyGatherTest : public ::testing::Test {
NodeDef* tensor_shapes_slices_node = CreateNode(
"save/RestoreV2/shape_and_slices", "Const", {}, &graph_def);
Tensor shapes_slices_val(DT_STRING, TensorShape({1}));
- shapes_slices_val.flat<string>()(0) = "4 1 0,4:0,1";
+ shapes_slices_val.flat<tstring>()(0) = "4 1 0,4:0,1";
SetNodeTensorAttr<string>("value", shapes_slices_val,
tensor_shapes_slices_node);
@@ -327,8 +327,8 @@ class SparsifyGatherTest : public ::testing::Test {
NodeDef* tensor_shapes_slices_node = CreateNode(
"save/RestoreV2/shape_and_slices", "Const", {}, &graph_def);
Tensor shapes_slices_val(DT_STRING, TensorShape({2}));
- shapes_slices_val.flat<string>()(0) = "4 1 0,4:0,1";
- shapes_slices_val.flat<string>()(1) = "4 1 0,4:0,1";
+ shapes_slices_val.flat<tstring>()(0) = "4 1 0,4:0,1";
+ shapes_slices_val.flat<tstring>()(1) = "4 1 0,4:0,1";
SetNodeTensorAttr<string>("value", shapes_slices_val,
tensor_shapes_slices_node);