Improvements to the C++ graph building API.

TESTED:
- passed opensource_build: http://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/2780/
Change: 127585603

1 files changed, 25 insertions, 30 deletions

diff --git a/tensorflow/examples/label_image/main.cc b/tensorflow/examples/label_image/main.cc
index 9251d06684..e4cc11dbe0 100644
--- a/tensorflow/examples/label_image/main.cc
+++ b/tensorflow/examples/label_image/main.cc
@@ -87,50 +87,44 @@ Status ReadTensorFromImageFile(string file_name, const int input_height,
                                const int input_width, const float input_mean,
                                const float input_std,
                                std::vector<Tensor>* out_tensors) {
-  tensorflow::GraphDefBuilder b;
+  auto root = tensorflow::Scope::NewRootScope();
+  using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
   string input_name = "file_reader";
   string output_name = "normalized";
-  tensorflow::Node* file_reader =
-      tensorflow::ops::ReadFile(tensorflow::ops::Const(file_name, b.opts()),
-                                b.opts().WithName(input_name));
+  auto file_reader = ReadFile(root.WithOpName(input_name), file_name);
   // Now try to figure out what kind of file it is and decode it.
   const int wanted_channels = 3;
-  tensorflow::Node* image_reader;
+  Output image_reader;
   if (tensorflow::StringPiece(file_name).ends_with(".png")) {
-    image_reader = tensorflow::ops::DecodePng(
-        file_reader,
-        b.opts().WithAttr("channels", wanted_channels).WithName("png_reader"));
+    image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,
+                             DecodePng::Channels(wanted_channels));
   } else {
     // Assume if it's not a PNG then it must be a JPEG.
-    image_reader = tensorflow::ops::DecodeJpeg(
-        file_reader,
-        b.opts().WithAttr("channels", wanted_channels).WithName("jpeg_reader"));
+    image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,
+                              DecodeJpeg::Channels(wanted_channels));
   }
   // Now cast the image data to float so we can do normal math on it.
-  tensorflow::Node* float_caster = tensorflow::ops::Cast(
-      image_reader, tensorflow::DT_FLOAT, b.opts().WithName("float_caster"));
+  auto float_caster =
+      Cast(root.WithOpName("float_caster"), image_reader, tensorflow::DT_FLOAT);
   // The convention for image ops in TensorFlow is that all images are expected
   // to be in batches, so that they're four-dimensional arrays with indices of
   // [batch, height, width, channel]. Because we only have a single image, we
   // have to add a batch dimension of 1 to the start with ExpandDims().
-  tensorflow::Node* dims_expander = tensorflow::ops::ExpandDims(
-      float_caster, tensorflow::ops::Const(0, b.opts()), b.opts());
+  auto dims_expander = ExpandDims(root, float_caster, 0);
   // Bilinearly resize the image to fit the required dimensions.
-  tensorflow::Node* resized = tensorflow::ops::ResizeBilinear(
-      dims_expander, tensorflow::ops::Const({input_height, input_width},
-                                            b.opts().WithName("size")),
-      b.opts());
+  auto resized = ResizeBilinear(
+      root, dims_expander,
+      Const(root.WithOpName("size"), {input_height, input_width}));
   // Subtract the mean and divide by the scale.
-  tensorflow::ops::Div(
-      tensorflow::ops::Sub(
-          resized, tensorflow::ops::Const({input_mean}, b.opts()), b.opts()),
-      tensorflow::ops::Const({input_std}, b.opts()),
-      b.opts().WithName(output_name));
+  Div(root.WithOpName(output_name), Sub(root, resized, {input_mean}),
+      {input_std});
 
   // This runs the GraphDef network definition that we've just constructed, and
   // returns the results in the output tensor.
   tensorflow::GraphDef graph;
-  TF_RETURN_IF_ERROR(b.ToGraphDef(&graph));
+  TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));
+
   std::unique_ptr<tensorflow::Session> session(
       tensorflow::NewSession(tensorflow::SessionOptions()));
   TF_RETURN_IF_ERROR(session->Create(graph));
@@ -161,15 +155,16 @@ Status LoadGraph(string graph_file_name,
 // their positions in the tensor, which correspond to categories.
 Status GetTopLabels(const std::vector<Tensor>& outputs, int how_many_labels,
                     Tensor* indices, Tensor* scores) {
-  tensorflow::GraphDefBuilder b;
+  auto root = tensorflow::Scope::NewRootScope();
+  using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
+
   string output_name = "top_k";
-  tensorflow::ops::TopKV2(tensorflow::ops::Const(outputs[0], b.opts()),
-                          tensorflow::ops::Const(how_many_labels, b.opts()),
-                          b.opts().WithName(output_name));
+  TopKV2(root.WithOpName(output_name), outputs[0], how_many_labels);
   // This runs the GraphDef network definition that we've just constructed, and
   // returns the results in the output tensors.
   tensorflow::GraphDef graph;
-  TF_RETURN_IF_ERROR(b.ToGraphDef(&graph));
+  TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));
+
   std::unique_ptr<tensorflow::Session> session(
       tensorflow::NewSession(tensorflow::SessionOptions()));
   TF_RETURN_IF_ERROR(session->Create(graph));