experiments/STT-tensorflow
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Qualify uses of std::string

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PiperOrigin-RevId: 317000789
Change-Id: I6f847b235496d1dc8f8b3380e21ce890566a9a88
This commit is contained in:
A. Unique TensorFlower 2020-06-17 17:37:35 -07:00 committed by TensorFlower Gardener
parent 70e2387ecc
commit adf0573f9d
5 changed files with 17 additions and 14 deletions
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5
tensorflow/lite/toco/graph_transformations/tests/fuse_binary_into_following_affine_test.cc
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@ -43,14 +43,15 @@ class FuseBinaryIntoFollowingAffineTest : public ::testing::Test {
void SetUp() override { model_.reset(new Model); }
void CreateArray(const string& name, const std::vector<int>& shape) {
void CreateArray(const std::string& name, const std::vector<int>& shape) {
Array& array = model_->GetOrCreateArray(name);
array.data_type = ArrayDataType::kFloat;
Shape* array_shape = array.mutable_shape();
*(array_shape->mutable_dims()) = shape;
}
void CreateConstantArray(const string& name, const std::vector<int>& shape,
void CreateConstantArray(const std::string& name,
const std::vector<int>& shape,
const std::vector<float>& data) {
CreateArray(name, shape);
Array& array = model_->GetOrCreateArray(name);

5
tensorflow/lite/toco/graph_transformations/tests/fuse_binary_into_preceding_affine_test.cc
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@ -43,14 +43,15 @@ class FuseBinaryIntoPrecedingAffineTest : public ::testing::Test {
void SetUp() override { model_.reset(new Model); }
void CreateArray(const string& name, const std::vector<int>& shape) {
void CreateArray(const std::string& name, const std::vector<int>& shape) {
Array& array = model_->GetOrCreateArray(name);
array.data_type = ArrayDataType::kFloat;
Shape* array_shape = array.mutable_shape();
*(array_shape->mutable_dims()) = shape;
}
void CreateConstantArray(const string& name, const std::vector<int>& shape,
void CreateConstantArray(const std::string& name,
const std::vector<int>& shape,
const std::vector<float>& data) {
CreateArray(name, shape);
Array& array = model_->GetOrCreateArray(name);

4
tensorflow/lite/toco/graph_transformations/tests/lstm_utils_test.cc
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@ -46,12 +46,12 @@ class CopyArrayDataTest : public ::testing::Test {
int src_dim_1, int src_dim_2,
std::initializer_list<float> dst_data, int dst_dim_1,
int dst_dim_2) {
string src_array = "src_array";
std::string src_array = "src_array";
src_buffer_ = CreateFloatArrayBuffer(
model, &src_array,
src_dim_2 == 1 ? Shape({src_dim_1}) : Shape({src_dim_1, src_dim_2}));
PopulateBuffer(src_buffer_, src_data);
string dst_array = "dst_array";
std::string dst_array = "dst_array";
dst_buffer_ = CreateFloatArrayBuffer(
model, &dst_array,
dst_dim_2 == 1 ? Shape({dst_dim_1}) : Shape({dst_dim_1, dst_dim_2}));

8
tensorflow/lite/toco/graph_transformations/tests/resolve_constant_concatenation_test.cc
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@ -107,10 +107,10 @@ class ResolveConstantConcatenationTest : public ::testing::Test {
// together with 4 arrays as its inputs.
// It receives the dimension of concatenation as input.
void PrepareModel(Model* model, int axis) {
const string output_name("concat_op_output");
const std::string output_name("concat_op_output");
model->flags.add_output_arrays(output_name);
std::vector<string> concat_input_names = {"array0", "array1", "array2",
"array3"};
std::vector<std::string> concat_input_names = {"array0", "array1", "array2",
"array3"};
const int kDim = 3;
const int kElementPerDim = 2;
@ -122,7 +122,7 @@ class ResolveConstantConcatenationTest : public ::testing::Test {
{20., 21., 22., 23., 24., 25., 26., 27.},
{30., 31., 32., 33., 34., 35., 36., 37.}};
int cnt = 0;
for (const string& concat_input_name : concat_input_names) {
for (const std::string& concat_input_name : concat_input_names) {
Array& in_array = model->GetOrCreateArray(concat_input_name);
in_array.data_type = ArrayDataType::kFloat;

9
tensorflow/lite/toco/graph_transformations/tests/unpack_quantize_test.cc
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@ -40,10 +40,11 @@ class UnpackQuantizeTest : public ::testing::Test {
// 1. calculate min and max of the input.
// 2. insert dequantization nodes after quantized outputs of Unpack operation.
void PrepareModel(Model* model, int axis) {
std::vector<string> unpack_output_names = {"unpack_out0", "unpack_out1"};
std::vector<std::string> unpack_output_names = {"unpack_out0",
"unpack_out1"};
model->flags.add_output_arrays(unpack_output_names[0]);
model->flags.add_output_arrays(unpack_output_names[1]);
const string unpack_input_name("unpack_op_input");
const std::string unpack_input_name("unpack_op_input");
const int kDim = 2;
const int kElementPerDim = 2;
@ -75,7 +76,7 @@ class UnpackQuantizeTest : public ::testing::Test {
// Configuring the necessary outputs. The outputs also happen to be in
// kFloat. This is because during quantization transformation data types for
// these arrays are going to be forced to be kUint8.
for (const string& unpack_output_name : unpack_output_names) {
for (const std::string& unpack_output_name : unpack_output_names) {
Array& out_array = model->GetOrCreateArray(unpack_output_name);
out_array.GetOrCreateMinMax();
out_array.data_type = ArrayDataType::kFloat;
@ -109,7 +110,7 @@ TEST_F(UnpackQuantizeTest, CheckUnpackPreservesQuantizationParameters) {
->Run(&model, /*op_index=*/0, &modified)
.ok());
const string output_name = model.flags.output_arrays(0);
const std::string output_name = model.flags.output_arrays(0);
// Quantization transformation inserts NODE_NAME_DEQUANTIZE operations,
// effectively making them the new outputs of the array. Old outputs of the