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Remove redundant get() calls and string conversions

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PiperOrigin-RevId: 157497932
This commit is contained in:
A. Unique TensorFlower 2017-05-30 12:39:24 -07:00 committed by TensorFlower Gardener
parent af2b9d8757
commit dcc3cdce8d
12 changed files with 41 additions and 38 deletions
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4
tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc
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@ -77,14 +77,14 @@ EventMgr::~EventMgr() {
} }
void EventMgr::StartPollingLoop() { void EventMgr::StartPollingLoop() {
CHECK(polling_stopped_.get() == nullptr); CHECK(polling_stopped_ == nullptr);
stop_polling_.reset(new Notification); stop_polling_.reset(new Notification);
polling_stopped_.reset(new Notification); polling_stopped_.reset(new Notification);
threadpool_.Schedule([this]() { PollLoop(); }); threadpool_.Schedule([this]() { PollLoop(); });
} }
void EventMgr::StopPollingLoop() { void EventMgr::StopPollingLoop() {
if (stop_polling_.get()) { if (stop_polling_) {
stop_polling_->Notify(); stop_polling_->Notify();
polling_stopped_->WaitForNotification(); polling_stopped_->WaitForNotification();
stop_polling_.reset(nullptr); stop_polling_.reset(nullptr);

16
tensorflow/core/common_runtime/simple_graph_execution_state.cc
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@ -74,8 +74,8 @@ SimpleGraphExecutionState::~SimpleGraphExecutionState() {
std::unique_ptr<SimpleGraphExecutionState> ret( std::unique_ptr<SimpleGraphExecutionState> ret(
new SimpleGraphExecutionState(graph_def, options)); new SimpleGraphExecutionState(graph_def, options));
TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&ret->original_graph_def_, TF_RETURN_IF_ERROR(
*ret->flib_def_.get(), 0)); AddDefaultAttrsToGraphDef(&ret->original_graph_def_, *ret->flib_def_, 0));
// TODO(mrry): Refactor InitBaseGraph() so that we don't have to // TODO(mrry): Refactor InitBaseGraph() so that we don't have to
// pass an empty BuildGraphOptions (that isn't going to be used when // pass an empty BuildGraphOptions (that isn't going to be used when
// place_pruned_graph is false). // place_pruned_graph is false).
@ -103,8 +103,8 @@ SimpleGraphExecutionState::~SimpleGraphExecutionState() {
GraphDef temp(graph_def); GraphDef temp(graph_def);
std::unique_ptr<SimpleGraphExecutionState> ret( std::unique_ptr<SimpleGraphExecutionState> ret(
new SimpleGraphExecutionState(&temp, options)); new SimpleGraphExecutionState(&temp, options));
TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(&ret->original_graph_def_, TF_RETURN_IF_ERROR(
*ret->flib_def_.get(), 0)); AddDefaultAttrsToGraphDef(&ret->original_graph_def_, *ret->flib_def_, 0));
TF_RETURN_IF_ERROR(ret->InitBaseGraph(subgraph_options)); TF_RETURN_IF_ERROR(ret->InitBaseGraph(subgraph_options));
TF_RETURN_IF_ERROR(ret->BuildGraph(subgraph_options, out_client_graph)); TF_RETURN_IF_ERROR(ret->BuildGraph(subgraph_options, out_client_graph));
*out_state = std::move(ret); *out_state = std::move(ret);
@ -139,7 +139,7 @@ Status SimpleGraphExecutionState::Extend(
int old_node_size = gdef.node_size(); int old_node_size = gdef.node_size();
gdef.mutable_node()->MergeFrom(extension_def.node()); gdef.mutable_node()->MergeFrom(extension_def.node());
TF_RETURN_IF_ERROR( TF_RETURN_IF_ERROR(
AddDefaultAttrsToGraphDef(&gdef, *flib_def_.get(), old_node_size)); AddDefaultAttrsToGraphDef(&gdef, *flib_def_, old_node_size));
// Merge versions // Merge versions
if (gdef.has_versions()) { if (gdef.has_versions()) {
if (gdef.versions().producer() != extension_def.versions().producer()) { if (gdef.versions().producer() != extension_def.versions().producer()) {
@ -181,7 +181,7 @@ Status SimpleGraphExecutionState::Extend(
if (gdef.versions().producer() >= 5) { if (gdef.versions().producer() >= 5) {
// Validate the graph: we assume that merging two valid graphs // Validate the graph: we assume that merging two valid graphs
// should maintain graph validity. // should maintain graph validity.
TF_RETURN_IF_ERROR(graph::ValidateGraphDef(gdef, *flib_def_.get())); TF_RETURN_IF_ERROR(graph::ValidateGraphDef(gdef, *flib_def_));
} }
// 6. Add the extension. // 6. Add the extension.
@ -196,7 +196,7 @@ Status SimpleGraphExecutionState::Extend(
new SimpleGraphExecutionState(&gdef, combined_options)); new SimpleGraphExecutionState(&gdef, combined_options));
TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef( TF_RETURN_IF_ERROR(AddDefaultAttrsToGraphDef(
&new_execution_state->original_graph_def_, *flib_def_.get(), 0)); &new_execution_state->original_graph_def_, *flib_def_, 0));
if (!session_options_->config.graph_options().place_pruned_graph()) { if (!session_options_->config.graph_options().place_pruned_graph()) {
// TODO(mrry): Refactor InitBaseGraph() so that we don't have to // TODO(mrry): Refactor InitBaseGraph() so that we don't have to
// pass an empty BuildGraphOptions (that isn't going to be used // pass an empty BuildGraphOptions (that isn't going to be used
@ -313,7 +313,7 @@ Status SimpleGraphExecutionState::InitBaseGraph(
CostModel costs(true /*is_global*/); CostModel costs(true /*is_global*/);
{ {
mutex_lock l(mu_); mutex_lock l(mu_);
costs_.InitFromGraph(*new_graph.get()); costs_.InitFromGraph(*new_graph);
costs.MergeFromGlobal(costs_); costs.MergeFromGlobal(costs_);
} }

2
tensorflow/core/kernels/resize_bilinear_op_test.cc
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@ -194,7 +194,7 @@ TEST_F(ResizeBilinearOpTest, TestBilinearRandom2x2To1x1) {
ResizeBilinearBaseline(input->tensor<float, 4>(), ResizeBilinearBaseline(input->tensor<float, 4>(),
expected->tensor<float, 4>()); expected->tensor<float, 4>());
EXPECT_EQ(input->flat<float>()(0), output->flat<float>()(0)); EXPECT_EQ(input->flat<float>()(0), output->flat<float>()(0));
test::ExpectTensorEqual<float>(*expected.get(), *output); test::ExpectTensorEqual<float>(*expected, *output);
} }
TEST_F(ResizeBilinearOpAlignCornersTest, TestBilinearAlignCorners2x2To1x1) { TEST_F(ResizeBilinearOpAlignCornersTest, TestBilinearAlignCorners2x2To1x1) {

2
tensorflow/core/kernels/xsmm_conv2d.cc
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@ -17,7 +17,7 @@ limitations under the License.
// libxsmm is not available. // libxsmm is not available.
#ifndef TENSORFLOW_USE_LIBXSMM #ifndef TENSORFLOW_USE_LIBXSMM
void dummy_xsmm_conv2d_ensure_file_is_not_empty(void); void dummy_xsmm_conv2d_ensure_file_is_not_empty();
#else #else
#define USE_EIGEN_TENSOR #define USE_EIGEN_TENSOR

2
tensorflow/core/lib/io/zlib_inputstream.cc
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@ -37,7 +37,7 @@ ZlibInputStream::ZlibInputStream(
} }
ZlibInputStream::~ZlibInputStream() { ZlibInputStream::~ZlibInputStream() {
if (z_stream_.get()) { if (z_stream_) {
inflateEnd(z_stream_.get()); inflateEnd(z_stream_.get());
} }
} }

2
tensorflow/core/lib/io/zlib_outputbuffer.cc
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@ -36,7 +36,7 @@ ZlibOutputBuffer::ZlibOutputBuffer(
z_stream_(new z_stream) {} z_stream_(new z_stream) {}
ZlibOutputBuffer::~ZlibOutputBuffer() { ZlibOutputBuffer::~ZlibOutputBuffer() {
if (z_stream_.get()) { if (z_stream_) {
LOG(WARNING) << "ZlibOutputBuffer::Close() not called. Possible data loss"; LOG(WARNING) << "ZlibOutputBuffer::Close() not called. Possible data loss";
} }
} }

8
tensorflow/core/lib/jpeg/jpeg_mem_unittest.cc
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@ -71,17 +71,17 @@ void TestJPEG(Env* env, const string& jpegfile) {
// Set min_acceptable_fraction to something insufficient // Set min_acceptable_fraction to something insufficient
flags.min_acceptable_fraction = 0.8; flags.min_acceptable_fraction = 0.8;
imgdata.reset(Uncompress(temp, fsize / 2, flags, &w, &h, &c, nullptr)); imgdata.reset(Uncompress(temp, fsize / 2, flags, &w, &h, &c, nullptr));
CHECK(imgdata.get() == nullptr); CHECK(imgdata == nullptr);
// Now, use a value that makes fsize/2 be enough for a black-filling // Now, use a value that makes fsize/2 be enough for a black-filling
flags.min_acceptable_fraction = 0.01; flags.min_acceptable_fraction = 0.01;
imgdata.reset(Uncompress(temp, fsize / 2, flags, &w, &h, &c, nullptr)); imgdata.reset(Uncompress(temp, fsize / 2, flags, &w, &h, &c, nullptr));
CHECK(imgdata.get() != nullptr); CHECK(imgdata != nullptr);
// Finally, uncompress the whole data // Finally, uncompress the whole data
flags.min_acceptable_fraction = 1.0; flags.min_acceptable_fraction = 1.0;
imgdata.reset(Uncompress(temp, fsize, flags, &w, &h, &c, nullptr)); imgdata.reset(Uncompress(temp, fsize, flags, &w, &h, &c, nullptr));
CHECK(imgdata.get() != nullptr); CHECK(imgdata != nullptr);
} }
TEST(JpegMemTest, Jpeg) { TEST(JpegMemTest, Jpeg) {
@ -267,7 +267,7 @@ TEST(JpegMemTest, ChromaDownsampling) {
int64 num_warnings; int64 num_warnings;
std::unique_ptr<uint8[]> uncompressed(Uncompress( std::unique_ptr<uint8[]> uncompressed(Uncompress(
jpeg.c_str(), jpeg.size(), unflags, &w, &h, &c, &num_warnings)); jpeg.c_str(), jpeg.size(), unflags, &w, &h, &c, &num_warnings));
CHECK(uncompressed.get() != nullptr); CHECK(uncompressed != nullptr);
CHECK_EQ(num_warnings, 0); CHECK_EQ(num_warnings, 0);
// Recompress the JPEG with and without chroma downsampling // Recompress the JPEG with and without chroma downsampling

8
tensorflow/core/ops/training_ops_test.cc
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@ -32,15 +32,15 @@ static void TestGradAndIndicesErrorHandling(const ShapeInferenceTestOp& op,
// mismatch between grad[1] and var[1]. // mismatch between grad[1] and var[1].
INFER_ERROR("Dimension 1 in both shapes must be equal", op, INFER_ERROR("Dimension 1 in both shapes must be equal", op,
shape_spec("[?,1]", "[?,2];[?]").c_str()); shape_spec("[?,1]", "[?,2];[?]"));
// grad[0] and indices[0] must match. // grad[0] and indices[0] must match.
INFER_ERROR("Dimensions must be equal, but are 1 and 2", op, INFER_ERROR("Dimensions must be equal, but are 1 and 2", op,
shape_spec("?", "[2,?];[1]").c_str()); shape_spec("?", "[2,?];[1]"));
// grad is wrong rank. // grad is wrong rank.
INFER_ERROR("must be equal rank", op, shape_spec("[1]", "[?,2];[?]").c_str()); INFER_ERROR("must be equal rank", op, shape_spec("[1]", "[?,2];[?]"));
// indices is wrong rank. // indices is wrong rank.
INFER_ERROR("Shape must be rank 1 but is rank 2", op, INFER_ERROR("Shape must be rank 1 but is rank 2", op,
shape_spec("[?]", "[?];[1,2]").c_str()); shape_spec("[?]", "[?];[1,2]"));
} }
TEST(TrainingOpsTest, ApplyGradientDescent_ShapeFn) { TEST(TrainingOpsTest, ApplyGradientDescent_ShapeFn) {

10
tensorflow/core/util/events_writer.cc
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@ -36,7 +36,7 @@ EventsWriter::EventsWriter(const string& file_prefix)
num_outstanding_events_(0) {} num_outstanding_events_(0) {}
bool EventsWriter::InitIfNeeded() { bool EventsWriter::InitIfNeeded() {
if (recordio_writer_.get() != nullptr) { if (recordio_writer_ != nullptr) {
CHECK(!filename_.empty()); CHECK(!filename_.empty());
if (FileHasDisappeared()) { if (FileHasDisappeared()) {
// Warn user of data loss and let .reset() below do basic cleanup. // Warn user of data loss and let .reset() below do basic cleanup.
@ -63,7 +63,7 @@ bool EventsWriter::InitIfNeeded() {
return false; return false;
} }
recordio_writer_.reset(new io::RecordWriter(recordio_file_.get())); recordio_writer_.reset(new io::RecordWriter(recordio_file_.get()));
if (recordio_writer_.get() == nullptr) { if (recordio_writer_ == nullptr) {
LOG(ERROR) << "Could not create record writer"; LOG(ERROR) << "Could not create record writer";
return false; return false;
} }
@ -90,7 +90,7 @@ string EventsWriter::FileName() {
} }
void EventsWriter::WriteSerializedEvent(StringPiece event_str) { void EventsWriter::WriteSerializedEvent(StringPiece event_str) {
if (recordio_writer_.get() == nullptr) { if (recordio_writer_ == nullptr) {
if (!InitIfNeeded()) { if (!InitIfNeeded()) {
LOG(ERROR) << "Write failed because file could not be opened."; LOG(ERROR) << "Write failed because file could not be opened.";
return; return;
@ -110,7 +110,7 @@ void EventsWriter::WriteEvent(const Event& event) {
bool EventsWriter::Flush() { bool EventsWriter::Flush() {
if (num_outstanding_events_ == 0) return true; if (num_outstanding_events_ == 0) return true;
CHECK(recordio_file_.get() != nullptr) << "Unexpected NULL file"; CHECK(recordio_file_ != nullptr) << "Unexpected NULL file";
if (!recordio_writer_->Flush().ok()) { if (!recordio_writer_->Flush().ok()) {
LOG(ERROR) << "Failed to flush " << num_outstanding_events_ << " events to " LOG(ERROR) << "Failed to flush " << num_outstanding_events_ << " events to "
@ -139,7 +139,7 @@ bool EventsWriter::Flush() {
bool EventsWriter::Close() { bool EventsWriter::Close() {
bool return_value = Flush(); bool return_value = Flush();
if (recordio_file_.get() != nullptr) { if (recordio_file_ != nullptr) {
Status s = recordio_file_->Close(); Status s = recordio_file_->Close();
if (!s.ok()) { if (!s.ok()) {
LOG(ERROR) << "Error when closing previous event file: " << filename_ LOG(ERROR) << "Error when closing previous event file: " << filename_

2
tensorflow/stream_executor/cuda/cuda_diagnostics.cc
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@ -369,7 +369,7 @@ port::StatusOr<DriverVersion> Diagnostician::FindKernelDriverVersion() {
LOG(INFO) << "driver version file contents: \"\"\"" << contents.begin() LOG(INFO) << "driver version file contents: \"\"\"" << contents.begin()
<< "\"\"\""; << "\"\"\"";
fclose(driver_version_file); fclose(driver_version_file);
return FindKernelModuleVersion(string{contents.begin()}); return FindKernelModuleVersion(contents.begin());
} }
auto status = auto status =

19
tensorflow/tools/tfprof/internal/tfprof_op.cc
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@ -177,10 +177,11 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
root->proto().total_exec_micros(); root->proto().total_exec_micros();
} }
attrs.push_back(strings::Printf("%30s", strings::Printf( attrs.push_back(strings::Printf(
"%s (%.2f%%, %.2f%%)", "%30s", strings::Printf("%s (%.2f%%, %.2f%%)",
FormatTime(node->proto().exec_micros()).c_str(), FormatTime(node->proto().exec_micros()).c_str(),
accu_pct, pct).c_str()).c_str()); accu_pct, pct)
.c_str()));
} }
if (opts.select.find(kShown[2]) != opts.select.end()) { if (opts.select.find(kShown[2]) != opts.select.end()) {
@ -192,10 +193,12 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) {
pct = 100.0 * node->proto().parameters() / pct = 100.0 * node->proto().parameters() /
root->proto().total_parameters(); root->proto().total_parameters();
} }
attrs.push_back(strings::Printf("%30s", strings::Printf( attrs.push_back(strings::Printf(
"%s params (%.2f%%, %.2f%%)", "%30s",
FormatNumber(node->proto().parameters()).c_str(), strings::Printf("%s params (%.2f%%, %.2f%%)",
accu_pct, pct).c_str()).c_str()); FormatNumber(node->proto().parameters()).c_str(),
accu_pct, pct)
.c_str()));
} }
if (opts.select.find(kShown[3]) != opts.select.end()) { if (opts.select.find(kShown[3]) != opts.select.end()) {

4
tensorflow/tools/tfprof/tfprof_main.cc
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@ -41,7 +41,7 @@ limitations under the License.
using tensorflow::str_util::Split; using tensorflow::str_util::Split;
void completion(const char* buf, linenoiseCompletions* lc) { void completion(const char* buf, linenoiseCompletions* lc) {
tensorflow::string buf_str = tensorflow::string(buf); tensorflow::string buf_str = buf;
if (buf_str.find(" ") == buf_str.npos) { if (buf_str.find(" ") == buf_str.npos) {
for (const char* opt : tensorflow::tfprof::kCmds) { for (const char* opt : tensorflow::tfprof::kCmds) {
if (tensorflow::string(opt).find(buf_str) == 0) { if (tensorflow::string(opt).find(buf_str) == 0) {
@ -246,7 +246,7 @@ int main(int argc, char** argv) {
linenoiseHistoryLoad(".tfprof_history.txt"); linenoiseHistoryLoad(".tfprof_history.txt");
for (char* line = nullptr; (line = linenoise("tfprof> ")) != nullptr;) { for (char* line = nullptr; (line = linenoise("tfprof> ")) != nullptr;) {
tensorflow::string line_s = tensorflow::string(line); tensorflow::string line_s = line;
free(line); free(line);
if (line_s.empty()) { if (line_s.empty()) {