Use "empty" member function to test for emptiness

PiperOrigin-RevId: 157483181

tensorflow/compiler/jit/graphcycles/graphcycles_test.cc

@@ -243,7 +243,7 @@ TEST(GraphCycles, RandomizedTest) {
         break;
 
       case 1:  // Remove a node
-        if (nodes.size() > 0) {
+        if (!nodes.empty()) {
           int node_index = RandomNode(&rnd, &nodes);
           int node = nodes[node_index];
           nodes[node_index] = nodes.back();
@@ -263,7 +263,7 @@ TEST(GraphCycles, RandomizedTest) {
         break;
 
       case 2:  // Add an edge
-        if (nodes.size() > 0) {
+        if (!nodes.empty()) {
           int from = RandomNode(&rnd, &nodes);
           int to = RandomNode(&rnd, &nodes);
           if (EdgeIndex(&edges, nodes[from], nodes[to]) == -1) {
@@ -282,7 +282,7 @@ TEST(GraphCycles, RandomizedTest) {
         break;
 
       case 3:  // Remove an edge
-        if (edges.size() > 0) {
+        if (!edges.empty()) {
           int i = RandomEdge(&rnd, &edges);
           int from = edges[i].from;
           int to = edges[i].to;
@@ -296,7 +296,7 @@ TEST(GraphCycles, RandomizedTest) {
         break;
 
       case 4:  // Check a path
-        if (nodes.size() > 0) {
+        if (!nodes.empty()) {
           int from = RandomNode(&rnd, &nodes);
           int to = RandomNode(&rnd, &nodes);
           int32 path[2 * kMaxNodes];

tensorflow/compiler/xla/legacy_flags/layout_util_flags.cc

@@ -53,7 +53,7 @@ static void AllocateRawFlag() {
 static bool ParseDefaultLayout(const string& text, DefaultLayout* layout) {
   bool result = true;
   std::vector<string> field = tensorflow::str_util::Split(text, ':');
-  if (field.size() > 0) {
+  if (!field.empty()) {
     if (field[0] == "random") {
       layout->dimension_order = DefaultLayout::DimensionOrder::kRandom;
       if (field.size() > 1) {

tensorflow/compiler/xla/metric_table_report.cc

@@ -150,7 +150,7 @@ void MetricTableReport::AppendCategoryTable() {
 
     // Show the category.
     string text = category.category_text;
-    if (text == "") {
+    if (text.empty()) {
       text = "[no category]";
     }
     tensorflow::strings::StrAppend(&text, " (", category.entries.size(), " ",
@@ -200,7 +200,7 @@ void MetricTableReport::AppendEntryTable() {
     metric_sum += entry.metric;
 
     string text = entry.text;
-    if (text == "") {
+    if (text.empty()) {
       text = "[no entry text]";
     }
     AppendTableRow(text, entry.metric, metric_sum);

tensorflow/compiler/xla/service/shape_inference.cc

@@ -227,7 +227,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
 /* static */ StatusOr<Shape> ShapeInference::InferConcatOpShape(
     tensorflow::gtl::ArraySlice<const Shape*> arg_shapes,
     const int64 dimension) {
-  if (arg_shapes.size() == 0) {
+  if (arg_shapes.empty()) {
     return InvalidArgument("Concatenate expects at least one argument");
   }
   if (dimension < 0 || dimension >= ShapeUtil::Rank(*arg_shapes[0])) {
@@ -679,7 +679,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 /* static */ StatusOr<Shape> ShapeInference::InferMapShape(
     tensorflow::gtl::ArraySlice<const Shape*> arg_shapes,
     const ProgramShape& to_apply) {
-  if (arg_shapes.size() == 0) {
+  if (arg_shapes.empty()) {
     return InvalidArgument("Map expects at least one argument");
   }
 

tensorflow/core/common_runtime/session_state.cc

@@ -66,7 +66,7 @@ Status TensorStore::AddTensor(const string& name, const TensorAndKey& tk) {
 Status TensorStore::SaveTensors(const std::vector<string>& output_names,
                                 SessionState* session_state) {
   mutex_lock l(lock_);
-  if (tensors_.size() != 0) {
+  if (!tensors_.empty()) {
     // Save only the tensors in output_names in the session.
     for (const string& name : output_names) {
       TensorId id(ParseTensorName(name));

tensorflow/core/common_runtime/simple_placer.cc

@@ -244,7 +244,7 @@ class ColocationGraph {
       // members_[old_root].supported_device_types.
       MergeSupportedDevices(&members_[new_root].supported_device_types,
                             members_[old_root].supported_device_types);
-      if (members_[new_root].supported_device_types.size() == 0) {
+      if (members_[new_root].supported_device_types.empty()) {
         string debug_info;
         AddDebugInfo(x_root, &debug_info);
         AddDebugInfo(y_root, &debug_info);

tensorflow/core/distributed_runtime/scheduler.cc

@@ -264,7 +264,7 @@ Microseconds GreedyScheduler::ComputeSchedule(
     for (auto& x : device_states_) {
       Sim* sim = x.second;
       while (sim->num_running < sim->degree_parallelism &&
-             sim->ready_nodes.size() > 0) {
+             !sim->ready_nodes.empty()) {
         Event e;
         e.node = GetNodeWithHighestPriority(sim->ready_nodes);
         e.time = event.time + cost_model_->TimeEstimate(e.node);

tensorflow/core/graph/costmodel.cc

@@ -60,7 +60,7 @@ void CostModel::MergeFromLocal(const Graph& g, const CostModel& cm) {
     time_[global_id] += cm.time_[local_id];
     int num_slots = cm.slot_bytes_[local_id].size();
     if (num_slots > 0) {
-      if (slot_bytes_[global_id].size() == 0) {
+      if (slot_bytes_[global_id].empty()) {
         slot_bytes_[global_id].resize(num_slots);
       } else {
         CHECK_EQ(num_slots, slot_bytes_[global_id].size());
@@ -82,7 +82,7 @@ void CostModel::MergeFromGlobal(const CostModel& cm) {
     time_[i] += cm.time_[i];
     int num_slots = cm.slot_bytes_[i].size();
     if (num_slots > 0) {
-      if (slot_bytes_[i].size() == 0) {
+      if (slot_bytes_[i].empty()) {
         slot_bytes_[i].resize(num_slots);
       } else {
         CHECK_EQ(num_slots, slot_bytes_[i].size());
@@ -138,7 +138,7 @@ void CostModel::SetNumOutputs(const Node* node, int num_outputs) {
   auto perslot = &slot_bytes_[id];
   auto max_mem_usage = &max_mem_usage_[id];
   auto output_port_alloc_ids = &output_port_alloc_ids_[id];
-  if (perslot->size() > 0) {
+  if (!perslot->empty()) {
     CHECK_EQ(num_outputs, perslot->size()) << "Cannot resize slot_bytes, node="
                                            << node->name();
   } else {

tensorflow/core/kernels/fifo_queue.cc

@@ -281,7 +281,7 @@ void FIFOQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
                       }
                     }
                   }
-                  if (allow_small_batch && queues_[0].size() > 0) {
+                  if (allow_small_batch && !queues_[0].empty()) {
                     // Request all remaining elements in the queue.
                     queue_size = queues_[0].size();
                     attempt->tuple.clear();

tensorflow/core/kernels/padding_fifo_queue.cc

@@ -119,7 +119,7 @@ void PaddingFIFOQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
                   }
                 }
               }
-              if (allow_small_batch && queues_[0].size() > 0) {
+              if (allow_small_batch && !queues_[0].empty()) {
                 // Request all remaining elements in the queue.
                 queue_size = queues_[0].size();
                 attempt->tuples.clear();

tensorflow/core/kernels/random_shuffle_queue_op.cc

@@ -358,7 +358,7 @@ void RandomShuffleQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx,
                       }
                     }
                   }
-                  if (allow_small_batch && queues_[0].size() > 0) {
+                  if (allow_small_batch && !queues_[0].empty()) {
                     // Request all remaining elements in the queue.
                     queue_size = queues_[0].size();
                     attempt->tuple.clear();

tensorflow/core/kernels/range_sampler.cc

@@ -105,7 +105,7 @@ void RangeSampler::SampleBatchGetExpectedCountAvoid(
     num_tries = batch_size;
   }
   // Compute the expected counts of the batch and the extra values
-  if (batch_expected_count.size() > 0) {
+  if (!batch_expected_count.empty()) {
     CHECK_EQ(batch_size, batch_expected_count.size());
     for (int i = 0; i < batch_size; i++) {
       batch_expected_count[i] =
@@ -131,7 +131,7 @@ void AllSampler::SampleBatchGetExpectedCountAvoid(
   for (int i = 0; i < batch_size; i++) {
     batch[i] = i;
   }
-  if (batch_expected_count.size() > 0) {
+  if (!batch_expected_count.empty()) {
     CHECK_EQ(batch_size, batch_expected_count.size());
     for (int i = 0; i < batch_size; i++) {
       batch_expected_count[i] = 1;
@@ -290,7 +290,7 @@ Status FixedUnigramSampler::LoadFromFile(Env* env, const string& vocab_file,
     // The vocabulary file should be in csv like format, with the last
     // field the weight associated with the word.
     std::vector<string> cols = str_util::Split(line, ',');
-    if (cols.size() == 0) continue;
+    if (cols.empty()) continue;
     // Skip entries that do not belong to this shard.
     if (word_id % num_shards_ == shard_) {
       float w = 0.0;

tensorflow/core/kernels/sample_distorted_bounding_box_op.cc

@@ -298,7 +298,7 @@ class SampleDistortedBoundingBoxOp : public OpKernel {
 
     // Insert the entire image if no bounding boxes are supplied.
     const Rectangle image_rect(0, 0, width, height);
-    if (bounding_boxes.size() < 1) {
+    if (bounding_boxes.empty()) {
       OP_REQUIRES(context, use_image_if_no_bounding_boxes_,
                   errors::InvalidArgument(
                       "No bounding boxes provided as input. One must "

tensorflow/core/kernels/shuffle_dataset_op.cc

@@ -127,7 +127,7 @@ class ShuffleDatasetOp : public OpKernel {
           }
         }
 
-        if (buffer_.size() > 0) {
+        if (!buffer_.empty()) {
           *end_of_sequence = false;
           // Choose an element to produce uniformly at random, and
           // swap the last element into its place in the buffer.

tensorflow/core/kernels/sparse_matmul_op.cc

@@ -607,8 +607,8 @@ inline void GEPP(
   }
   for (const auto* left_slice : left_slices) {
     const auto& left = *left_slice;
-    const auto* data3 = (left.data3.size() > 0) ? &left.data3[0] : nullptr;
+    const auto* data3 = (!left.data3.empty()) ? &left.data3[0] : nullptr;
-    const auto* data = (left.data.size() > 0) ? &left.data[0] : nullptr;
+    const auto* data = (!left.data.empty()) ? &left.data[0] : nullptr;
     const int num_blocks = left.index3_offset.size();
     int begin3 = 0;
     int begin = 0;

tensorflow/core/kernels/spectrogram_test_utils.cc

@@ -100,7 +100,7 @@ void ReadCSVFileToComplexVectorOrDie(
   }
   std::vector<string> lines = str_util::Split(data_string, '\n');
   for (const string& line : lines) {
-    if (line == "") {
+    if (line.empty()) {
       continue;
     }
     std::vector<std::complex<double> > data_line;

tensorflow/core/kernels/stack_ops.cc

@@ -147,7 +147,7 @@ class StackOp : public OpKernel {
   explicit StackOp(OpKernelConstruction* context) : OpKernel(context) {
     OP_REQUIRES_OK(context, context->GetAttr("elem_type", &elem_type_));
     OP_REQUIRES_OK(context, context->GetAttr("stack_name", &stack_name_));
-    if (stack_name_ == "") stack_name_ = name();
+    if (stack_name_.empty()) stack_name_ = name();
   }
 
   void Compute(OpKernelContext* ctx) override {

tensorflow/core/kernels/string_split_op.cc

@@ -29,7 +29,7 @@ namespace tensorflow {
 namespace {
 
 std::vector<string> Split(const string& str, const string& delimiter) {
-  if (delimiter.size()) {
+  if (!delimiter.empty()) {
     return str_util::Split(str, delimiter, str_util::SkipEmpty());
   }
   std::vector<string> char_vector(str.size());

tensorflow/core/kernels/summary_op.cc

@@ -149,7 +149,7 @@ class SummaryMergeOp : public OpKernel {
           const string& tag = summary_in.value(v).tag();
           // The tag is unused by the TensorSummary op, so no need to check
           // for duplicates.
-          if ((tag != "") && !tags.insert(tag).second) {
+          if ((!tag.empty()) && !tags.insert(tag).second) {
             c->SetStatus(errors::InvalidArgument(strings::StrCat(
                 "Duplicate tag ", tag, " found in summary inputs")));
             return;

tensorflow/core/kernels/tensor_array_ops.cc

@@ -156,7 +156,7 @@ class TensorArrayOp : public TensorArrayCreationOp {
                    context->GetAttr("clear_after_read", &clear_after_read_));
     OP_REQUIRES_OK(context,
                    context->GetAttr("tensor_array_name", &tensor_array_name_));
-    if (tensor_array_name_ == "") tensor_array_name_ = name();
+    if (tensor_array_name_.empty()) tensor_array_name_ = name();
   }
 
   Status CreateTensorArray(OpKernelContext* ctx, ResourceMgr* rm,

tensorflow/core/lib/io/record_reader.cc

@@ -102,7 +102,7 @@ Status RecordReader::ReadChecksummed(uint64 offset, size_t n,
     TF_RETURN_IF_ERROR(zlib_input_stream_->ReadNBytes(expected, storage));
 
     if (storage->size() != expected) {
-      if (storage->size() == 0) {
+      if (storage->empty()) {
         return errors::OutOfRange("eof");
       } else {
         return errors::DataLoss("truncated record at ", offset);
@@ -121,7 +121,7 @@ Status RecordReader::ReadChecksummed(uint64 offset, size_t n,
     StringPiece data;
     TF_RETURN_IF_ERROR(src_->Read(offset, expected, &data, &(*storage)[0]));
     if (data.size() != expected) {
-      if (data.size() == 0) {
+      if (data.empty()) {
         return errors::OutOfRange("eof");
       } else {
         return errors::DataLoss("truncated record at ", offset);

tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc

@@ -183,7 +183,7 @@ Status SnappyInputBuffer::ReadFromFile() {
   // possible that on the last read there isn't enough data in the file to
   // fill up the buffer in which case file_->ReadNBytes would return an
   // OutOfRange error.
-  if (data.size() == 0) {
+  if (data.empty()) {
     return errors::OutOfRange("EOF reached");
   }
   if (errors::IsOutOfRange(s)) {

tensorflow/core/lib/io/table_test.cc

@@ -396,7 +396,7 @@ class Harness : public ::testing::Test {
           break;
         case 1: {
           // Attempt to return something smaller than an existing key
-          if (result.size() > 0 && result[result.size() - 1] > '\0') {
+          if (!result.empty() && result[result.size() - 1] > '\0') {
             result[result.size() - 1]--;
           }
           break;

tensorflow/core/lib/io/zlib_inputstream.cc

@@ -110,7 +110,7 @@ Status ZlibInputStream::ReadFromStream() {
   // possible that on the last read there isn't enough data in the stream to
   // fill up the buffer in which case input_stream_->ReadNBytes would return an
   // OutOfRange error.
-  if (data.size() == 0) {
+  if (data.empty()) {
     return errors::OutOfRange("EOF reached");
   }
   if (errors::IsOutOfRange(s)) {

tensorflow/core/platform/file_system_test.cc

@@ -264,7 +264,7 @@ class TestFileSystem : public NullFileSystem {
  public:
   // Only allow for a single root directory.
   Status IsDirectory(const string& dirname) override {
-    if (dirname == "." || dirname == "") {
+    if (dirname == "." || dirname.empty()) {
       return Status::OK();
     }
     return Status(tensorflow::error::FAILED_PRECONDITION, "Not a dir");
@@ -272,7 +272,7 @@ class TestFileSystem : public NullFileSystem {
 
   // Simulating a FS with a root dir and a single file underneath it.
   Status GetChildren(const string& dir, std::vector<string>* result) override {
-    if (dir == "." || dir == "") {
+    if (dir == "." || dir.empty()) {
       result->push_back("test");
     }
     return Status::OK();

tensorflow/core/platform/posix/load_library.cc

@@ -49,7 +49,7 @@ string FormatLibraryFileName(const string& name, const string& version) {
     filename = "lib" + name + "." + version + ".dylib";
   }
 #else
-  if (version.size() == 0) {
+  if (version.empty()) {
     filename = "lib" + name + ".so";
   } else {
     filename = "lib" + name + ".so" + "." + version;

tensorflow/core/util/example_proto_fast_parsing.cc

@@ -935,8 +935,8 @@ Status FastParseExample(const Config& config,
     for (size_t e = start; e < end; ++e) {
       status_of_minibatch[minibatch] = FastParseSerializedExample(
           serialized[e],
-          (example_names.size() > 0 ? example_names[e] : "<unknown>"), e,
+          (!example_names.empty() ? example_names[e] : "<unknown>"), e, config,
-          config, config_index, hasher, &fixed_dense_values,
+          config_index, hasher, &fixed_dense_values,
           &varlen_dense_buffers[minibatch], &sparse_buffers[minibatch]);
       if (!status_of_minibatch[minibatch].ok()) break;
     }

tensorflow/core/util/example_proto_helper.cc

@@ -323,7 +323,7 @@ Status BatchExampleProtoToTensors(
     std::vector<Tensor>* output_sparse_shapes_tensor) {
   const int batch_size = examples.size();
 
-  const bool has_names = (names.size() > 0);
+  const bool has_names = (!names.empty());
   if (has_names) {
     if (names.size() != examples.size()) {
       return errors::InvalidArgument(

tensorflow/core/util/memmapped_file_system_writer.cc

@@ -41,7 +41,7 @@ Status MemmappedFileSystemWriter::SaveTensor(const Tensor& tensor,
         " and include [A-Za-z0-9_.]");
   }
   const auto tensor_data = tensor.tensor_data();
-  if (0 == tensor_data.size()) {
+  if (tensor_data.empty()) {
     return errors::InvalidArgument(
         "MemmappedEnvWritter: saving tensor with 0 size");
   }

tensorflow/stream_executor/kernel_spec.cc

@@ -103,7 +103,7 @@ const char *CudaPtxInMemory::default_text() const {
   if (decompressed_ptx_iter != decompressed_ptx_.end()) {
     // If the decompressed string is empty, which means the ptx hasn't been
     // decompressed, decompress it here.
-    if (decompressed_ptx_iter->second.size() == 0) {
+    if (decompressed_ptx_iter->second.empty()) {
       decompressed_ptx_iter->second = DecompressPtx(ptx);
     }
     return decompressed_ptx_iter->second.c_str();
@@ -136,7 +136,7 @@ const char *CudaPtxInMemory::text(int compute_capability_major,
   if (decompressed_ptx_iter != decompressed_ptx_.end()) {
     // If the decompressed string is empty, which means the ptx hasn't been
     // decompressed, decompress it here.
-    if (decompressed_ptx_iter->second.size() == 0) {
+    if (decompressed_ptx_iter->second.empty()) {
       decompressed_ptx_iter->second = DecompressPtx(ptx_iter->second);
     }
     return decompressed_ptx_iter->second.c_str();

tensorflow/tools/graph_transforms/freeze_requantization_ranges.cc

@@ -108,7 +108,7 @@ Status FreezeRequantizationRanges(const GraphDef& input_graph_def,
   string min_max_log_file;
   TF_RETURN_IF_ERROR(
       context.GetOneStringParameter("min_max_log_file", "", &min_max_log_file));
-  if (min_max_log_file == "") {
+  if (min_max_log_file.empty()) {
     return errors::InvalidArgument(
         "You must pass a file name to min_max_log_file");
   }

tensorflow/tools/graph_transforms/quantize_nodes.cc

@@ -149,7 +149,7 @@ string UniqueNodeNameFromInput(const string& input_name) {
     result += "__hat__";
   }
   result += node_name;
-  if (suffix != "") {
+  if (!suffix.empty()) {
     result += "__port__" + suffix.substr(1, suffix.size() - 1);
   }
   return result;

tensorflow/tools/graph_transforms/set_device.cc

@@ -32,7 +32,7 @@ Status SetDevice(const GraphDef& input_graph_def,
   for (const NodeDef& node : input_graph_def.node()) {
     NodeDef* new_node = output_graph_def->mutable_node()->Add();
     new_node->CopyFrom(node);
-    if (!if_default || (node.device() == "")) {
+    if (!if_default || (node.device().empty())) {
       new_node->set_device(new_device);
     }
   }

tensorflow/tools/graph_transforms/strip_unused_nodes.cc

@@ -76,7 +76,7 @@ Status TypeForPlaceholder(const TransformFuncContext& context,
 
 // Takes a comma-separated string of numbers and parses them into a shape.
 bool TensorShapeFromString(const string& shape_string, TensorShape* result) {
-  if (shape_string == "") {
+  if (shape_string.empty()) {
     return false;
   }
   std::vector<int64> dims;

tensorflow/tools/graph_transforms/summarize_graph_main.cc

@@ -184,7 +184,7 @@ Status SummarizeGraph(const GraphDef& graph, const string& graph_path,
         ++control_edge_count;
       }
     }
-    if (node.device() != "") {
+    if (!node.device().empty()) {
       ++device_counts[node.device()];
     }
     if ((node.op() == "Const") || (node.op() == "Variable") ||

tensorflow/tools/graph_transforms/transform_graph.cc

@@ -252,7 +252,7 @@ Status TransformGraph(const std::vector<string>& inputs,
   TransformRegistry* transform_registry = GetTransformRegistry();
   for (const auto& transform_info : transform_params) {
     const string& transform_name = transform_info.first;
-    if (transform_name == "") {
+    if (transform_name.empty()) {
       continue;
     }
     if (!transform_registry->count(transform_name)) {

tensorflow/tools/graph_transforms/transform_utils.cc

@@ -110,7 +110,7 @@ string CanonicalInputName(const string& input_name) {
   string node_name;
   string suffix;
   NodeNamePartsFromInput(input_name, &prefix, &node_name, &suffix);
-  if (suffix == "") {
+  if (suffix.empty()) {
     suffix = ":0";
   }
   return prefix + node_name + suffix;