feat - add 1_Introduction/basic-io example

Author: ksachdeva

data/hello.txt

@@ -0,0 +1,5 @@
+Hello world from Tensorflow
+This is new line
+
+a line after space
+the last line

examples/1_Introduction/CMakeLists.txt

@@ -16,5 +16,5 @@ endmacro()
 add_introduction(hello-world ${INTRODUCTION_SRC_PATH} hello-world)
 add_introduction(basic-operations ${INTRODUCTION_SRC_PATH} basic-operations)
 add_introduction(basic-tensors ${INTRODUCTION_SRC_PATH} basic-tensors)
-
+add_introduction(basic-io ${INTRODUCTION_SRC_PATH} basic-io)
 

examples/1_Introduction/src/basic-io.cc

@@ -0,0 +1,102 @@
+#include <tensorflow/core/framework/tensor.h>
+#include <tensorflow/core/lib/io/path.h>
+#include <tensorflow/core/platform/env.h>
+
+// Tensorflow provides support to perform I/O operations
+// as well. Normally you should be able to perform these types
+// of operations using the library/framework of your choice.
+// I believe reason tensorflow provides the support is to provide
+// an abstraction layer for various platforms it supports.
+//
+// In this example we are going to look at few I/O related
+// apis.
+//
+// Run this example as:
+// <path_to>/bin/intro-basic-io <path_to>/data/hello.txt
+
+int main(int argc, char** argv) {
+  if (argc != 2) {
+    std::cerr << "Provide the path to the input file .." << std::endl;
+    std::cout << "Usage: intro-basic-io data/hello.txt" << std::endl;
+    return -1;
+  }
+
+  auto filePath = std::string(argv[1]);
+
+  // The first thing to do is to get a handle on the underlying
+  // platform / OS on which this app is running.
+  auto env = tensorflow::Env::Default();
+
+  // check if a file provided as the input argument exists or not
+  auto status = env->FileExists(filePath);
+  if (status.ok() == false) {
+    std::cerr << "Invalid path to the file .." << std::endl;
+    return -1;
+  }
+
+  // get the size of the file
+  tensorflow::uint64 file_size;
+  status = env->GetFileSize(filePath, &file_size);
+
+  if (status.ok() == false) {
+    std::cerr << "Unable to get the size of the file .." << std::endl;
+    return -1;
+  }
+
+  std::cout << "File size is - " << file_size << std::endl;
+
+  // In order to read the content of the file
+  // we first get a file handle
+
+  std::unique_ptr<tensorflow::RandomAccessFile> theFile;
+  status = env->NewRandomAccessFile(filePath, &theFile);
+
+  if (status.ok() == false) {
+    std::cerr << "Unable to open the file .." << std::endl;
+    return -1;
+  }
+
+  // now using this handle we read it out
+  //
+  // it would be required to specify from which offset in the file
+  // we want to read and how many bytes we want to read
+  //
+  // it should be evident that trying to read more bytes than the ones
+  // in the file will lead to error very much like it happens in any I/O
+  // based API. In other words, it is up to the caller to make sure that the
+  // arguments of Read api are sane.
+  //
+  // Read api takes 4 arguments - offset, number of bytes to read, StringPiece
+  // and pointer to address where the api will store the result
+  //
+
+  // allocate the data where we will store the output of the Read api
+  std::string content;
+  content.resize(file_size);
+
+  // create StringPiece which is nothing but a view of any external data
+  // location
+  //
+  // Note that it is the API that is setting StringPiece to the content
+  tensorflow::StringPiece sp;
+
+  status = theFile->Read(0, file_size, &sp, &(content)[0]);
+
+  if (status.ok() == false) {
+    std::cerr << "Failed to read the file .." << std::endl;
+    return -1;
+  }
+
+  std::cout << content << std::endl;
+
+  // You can now get the data by StringPiece or
+  // the original location and convert it into the tensor
+  // (in this particular example it would be Scalar)
+  tensorflow::Tensor result(tensorflow::DT_STRING, tensorflow::TensorShape());
+  result.scalar<std::string>()() = sp.ToString();
+
+  // As should be clear from this example one could easily use the
+  // I/O api that are not necessarily part of tensorflow
+
+  return 0;
+}