clang format

.clang-format

@@ -0,0 +1,4 @@
+---
+BasedOnStyle: Google
+
+...

examples/event_writer/write_events.cpp

@@ -1,10 +1,10 @@
 #include <tensorflow/core/util/events_writer.h>
-#include <string>
 #include <iostream>
+#include <string>
 
-
-void write_scalar(tensorflow::EventsWriter* writer, double wall_time, tensorflow::int64 step,
-                  const std::string& tag, float simple_value) {
+void write_scalar(tensorflow::EventsWriter* writer, double wall_time,
+                  tensorflow::int64 step, const std::string& tag,
+                  float simple_value) {
   tensorflow::Event event;
   event.set_wall_time(wall_time);
   event.set_step(step);
@@ -14,9 +14,7 @@ void write_scalar(tensorflow::EventsWriter* writer, double wall_time, tensorflow
   writer->WriteEvent(event);
 }
 
-
-int main(int argc, char const *argv[]) {
-
+int main(int argc, char const* argv[]) {
   std::string envent_file = "./events";
   tensorflow::EventsWriter writer(envent_file);
   for (int i = 0; i < 150; ++i)

examples/keras/inference.cpp

@@ -1,13 +1,12 @@
 // 2018, Patrick Wieschollek <mail@patwie.com>
+#include <tensorflow/core/protobuf/meta_graph.pb.h>
 #include <tensorflow/core/public/session.h>
 #include <tensorflow/core/public/session_options.h>
-#include <tensorflow/core/protobuf/meta_graph.pb.h>
-#include <string>
 #include <iostream>
+#include <string>
 
 typedef std::vector<std::pair<std::string, tensorflow::Tensor>> tensor_dict;
 
-
 /**
  * @brief load a previous store model
  * @details [long description]
@@ -18,53 +17,55 @@ typedef std::vector<std::pair<std::string, tensorflow::Tensor>> tensor_dict;
  *    saver.save(sess, './exported/my_model')
  *    tf.train.write_graph(sess.graph, '.', './exported/graph.pb, as_text=False)
  *
- * this relies on a graph which has an operation called `init` responsible to initialize all variables, eg.
+ * this relies on a graph which has an operation called `init` responsible to
+ * initialize all variables, eg.
  *
- *    sess.run(tf.global_variables_initializer())  # somewhere in the python file
+ *    sess.run(tf.global_variables_initializer())  # somewhere in the python
+ * file
  *
  * @param sess active tensorflow session
  * @param graph_fn path to graph file (eg. "./exported/graph.pb")
- * @param checkpoint_fn path to checkpoint file (eg. "./exported/my_model", optional)
+ * @param checkpoint_fn path to checkpoint file (eg. "./exported/my_model",
+ * optional)
  * @return status of reloading
  */
-tensorflow::Status LoadModel(tensorflow::Session *sess, std::string graph_fn, std::string checkpoint_fn = "") {
+tensorflow::Status LoadModel(tensorflow::Session *sess, std::string graph_fn,
+                             std::string checkpoint_fn = "") {
   tensorflow::Status status;
 
   // Read in the protobuf graph we exported
   tensorflow::MetaGraphDef graph_def;
   status = ReadBinaryProto(tensorflow::Env::Default(), graph_fn, &graph_def);
-  if (status != tensorflow::Status::OK())
-    return status;
+  if (status != tensorflow::Status::OK()) return status;
 
   // create the graph
   status = sess->Create(graph_def.graph_def());
-  if (status != tensorflow::Status::OK())
-    return status;
+  if (status != tensorflow::Status::OK()) return status;
 
   // restore model from checkpoint, iff checkpoint is given
   if (checkpoint_fn != "") {
-    tensorflow::Tensor checkpointPathTensor(tensorflow::DT_STRING, tensorflow::TensorShape());
+    tensorflow::Tensor checkpointPathTensor(tensorflow::DT_STRING,
+                                            tensorflow::TensorShape());
     checkpointPathTensor.scalar<std::string>()() = checkpoint_fn;
 
-    tensor_dict feed_dict = {{graph_def.saver_def().filename_tensor_name(), checkpointPathTensor}};
-    status = sess->Run(feed_dict, {}, {graph_def.saver_def().restore_op_name()}, nullptr);
-    if (status != tensorflow::Status::OK())
-      return status;
+    tensor_dict feed_dict = {
+        {graph_def.saver_def().filename_tensor_name(), checkpointPathTensor}};
+    status = sess->Run(feed_dict, {}, {graph_def.saver_def().restore_op_name()},
+                       nullptr);
+    if (status != tensorflow::Status::OK()) return status;
   } else {
     // virtual Status Run(const std::vector<std::pair<string, Tensor> >& inputs,
     //                  const std::vector<string>& output_tensor_names,
     //                  const std::vector<string>& target_node_names,
     //                  std::vector<Tensor>* outputs) = 0;
     status = sess->Run({}, {}, {"init"}, nullptr);
-    if (status != tensorflow::Status::OK())
-      return status;
+    if (status != tensorflow::Status::OK()) return status;
   }
 
   return tensorflow::Status::OK();
 }
 
 int main(int argc, char const *argv[]) {
-
   const std::string graph_fn = "./exported/my_model.meta";
   const std::string checkpoint_fn = "./exported/my_model";
 
@@ -84,15 +85,15 @@ int main(int argc, char const *argv[]) {
   data_[1] = 43;
 
   tensor_dict feed_dict = {
-    { "input_plhdr", data },
+      {"input_plhdr", data},
   };
 
   std::vector<tensorflow::Tensor> outputs;
-  TF_CHECK_OK(sess->Run(feed_dict, {"sequential/Output_1/Softmax:0"}, {}, &outputs));
+  TF_CHECK_OK(
+      sess->Run(feed_dict, {"sequential/Output_1/Softmax:0"}, {}, &outputs));
 
   std::cout << "input           " << data.DebugString() << std::endl;
   std::cout << "output          " << outputs[0].DebugString() << std::endl;
 
-
   return 0;
 }

examples/resize/opencv_version.cc

@@ -2,8 +2,8 @@
 // Patrick Wieschollek, <mail@patwie.com>
 
 /*
-This example loads an image using OpenCV and resizes this image by factor 2 using TensorFlow
-before writing it again into a file.
+This example loads an image using OpenCV and resizes this image by factor 2
+using TensorFlow before writing it again into a file.
 */
 
 #include "tensorflow/cc/client/client_session.h"
@@ -14,39 +14,43 @@ before writing it again into a file.
 #include <opencv2/highgui/highgui.hpp>
 
 int main() {
-
   // read image in OpenCV
   std::string fn = "Grace_Hopper.png";
   cv::Mat image;
   image = cv::imread(fn);
-  if (!image.data ) {
-    std::cerr <<  "Could not open or find the image" << std::endl ;
+  if (!image.data) {
+    std::cerr << "Could not open or find the image" << std::endl;
     return -1;
   }
-  std::cout << "read image " << fn << " with shape " << image.size() << ", "<< image.channels() << std::endl;
+  std::cout << "read image " << fn << " with shape " << image.size() << ", "
+            << image.channels() << std::endl;
 
   // convert byte to float image
   cv::Mat image_float;
   image.convertTo(image_float, CV_32FC3);
-  float *image_float_data = (float*)image_float.data;
+  float *image_float_data = (float *)image_float.data;
 
   // create input shape
-  tensorflow::TensorShape image_shape = tensorflow::TensorShape{1, image.rows, image.cols, image.channels()};
-  std::cout << "Input TensorShape ["
-            << image_shape.dim_size(0) << ", "
-            << image_shape.dim_size(1) << ", "
-            << image_shape.dim_size(2) << ", "
-            << image_shape.dim_size(3) << "]" << std::endl;
+  tensorflow::TensorShape image_shape =
+      tensorflow::TensorShape{1, image.rows, image.cols, image.channels()};
+  std::cout << "Input TensorShape [" << image_shape.dim_size(0) << ", "
+            << image_shape.dim_size(1) << ", " << image_shape.dim_size(2)
+            << ", " << image_shape.dim_size(3) << "]" << std::endl;
 
   // create input tensor
-  tensorflow::Tensor image_tensor = tensorflow::Tensor(tensorflow::DT_FLOAT, image_shape);
+  tensorflow::Tensor image_tensor =
+      tensorflow::Tensor(tensorflow::DT_FLOAT, image_shape);
   // copy data from OpenCv to TensorFlow Tensor
-  std::copy_n((char*) image_float_data, image_shape.num_elements() * sizeof(float),
-                  const_cast<char*>(image_tensor.tensor_data().data()));
+  std::copy_n((char *)image_float_data,
+              image_shape.num_elements() * sizeof(float),
+              const_cast<char *>(image_tensor.tensor_data().data()));
 
   // create graph for resizing images
   tensorflow::Scope root = tensorflow::Scope::NewRootScope();
-  auto resized = tensorflow::ops::ResizeBicubic(root, image_tensor, tensorflow::ops::Const(root.WithOpName("size"), {2 * image.rows, 2 * image.cols}));
+  auto resized = tensorflow::ops::ResizeBicubic(
+      root, image_tensor,
+      tensorflow::ops::Const(root.WithOpName("size"),
+                             {2 * image.rows, 2 * image.cols}));
 
   // run graph and fetch outputs
   std::vector<tensorflow::Tensor> outputs;
@@ -57,21 +61,20 @@ int main() {
   tensorflow::Tensor output = outputs[0];
   float *result_float_data = output.flat<float>().data();
 
-  std::cout << "Output TensorShape ["
-            << output.shape().dim_size(0) << ", "
-            << output.shape().dim_size(1) << ", "
-            << output.shape().dim_size(2) << ", "
-            << output.shape().dim_size(3) << "]" << std::endl;
+  std::cout << "Output TensorShape [" << output.shape().dim_size(0) << ", "
+            << output.shape().dim_size(1) << ", " << output.shape().dim_size(2)
+            << ", " << output.shape().dim_size(3) << "]" << std::endl;
 
   cv::Mat resized_image;
   resized_image.create(output.dim_size(1), output.dim_size(2), CV_32FC3);
-  std::copy_n((char*) result_float_data , output.shape().num_elements() * sizeof(float), (char*) resized_image.data);
+  std::copy_n((char *)result_float_data,
+              output.shape().num_elements() * sizeof(float),
+              (char *)resized_image.data);
 
-  //cv::imwrite("Grace_Hopper_resized.png", resized_image);
+  // cv::imwrite("Grace_Hopper_resized.png", resized_image);
   cv::Mat resized_image_uint8;
   resized_image.convertTo(resized_image_uint8, CV_8UC3);
   cv::imwrite("Grace_Hopper_resized.png", resized_image_uint8);
 
-
   return 0;
 }

examples/resize/tensorflow_version.cc

@@ -1,15 +1,15 @@
 // tensorflow/cc/example/example.cc
 
 #include "tensorflow/cc/client/client_session.h"
-#include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/cc/ops/image_ops.h"
+#include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/tensor.h"
 
-#include <string>
 #include <fstream>
 #include <iostream>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
+#include <string>
 
 /*
 Loads, resizes and saves an image using TensorFlow only.
@@ -24,8 +24,10 @@ int main() {
   auto net2 = tensorflow::ops::DecodePng(root, net1);
   auto net3 = tensorflow::ops::Cast(root, net2, tensorflow::DT_FLOAT);
   auto net4 = tensorflow::ops::ExpandDims(root, net3, 0);
-  auto net5 = tensorflow::ops::ResizeBilinear(root, net4, tensorflow::ops::Const(root, {2 * 606, 2 * 517}));
-  auto net6 = tensorflow::ops::Reshape(root, net5, tensorflow::ops::Const(root, {2 * 606, 2 * 517, 3}));
+  auto net5 = tensorflow::ops::ResizeBilinear(
+      root, net4, tensorflow::ops::Const(root, {2 * 606, 2 * 517}));
+  auto net6 = tensorflow::ops::Reshape(
+      root, net5, tensorflow::ops::Const(root, {2 * 606, 2 * 517, 3}));
   auto net7 = tensorflow::ops::Cast(root, net6, tensorflow::DT_UINT8);
   auto net8 = tensorflow::ops::EncodeJpeg(root, net7);
 
@@ -34,7 +36,8 @@ int main() {
 
   // Run and fetch v
   TF_CHECK_OK(session.Run({net8}, &outputs));
-  std::ofstream("output.jpg", std::ios::binary) << outputs[0].scalar<std::string>()();
+  std::ofstream("output.jpg", std::ios::binary)
+      << outputs[0].scalar<std::string>()();
 
   return 0;
 }

examples/simple/example.cc

@@ -8,9 +8,9 @@ int main() {
   using namespace tensorflow::ops;
   Scope root = Scope::NewRootScope();
   // Matrix A = [3 2; -1 0]
-  auto A = Const(root, { {3.f, 2.f}, { -1.f, 0.f} });
+  auto A = Const(root, {{3.f, 2.f}, {-1.f, 0.f}});
   // Vector b = [3 5]
-  auto b = Const(root, { {3.f, 5.f} });
+  auto b = Const(root, {{3.f, 5.f}});
   // v = Ab^T
   auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));
   std::vector<Tensor> outputs;