Automated g4 rollback of changelist 179260538

PiperOrigin-RevId: 179263865

8 files changed, 326 insertions, 76 deletions

diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle
index 48f566f825..f7bdf8b816 100644
--- a/tensorflow/examples/android/build.gradle
+++ b/tensorflow/examples/android/build.gradle
@@ -28,8 +28,8 @@ buildscript {
     }
 
     dependencies {
-        classpath 'com.android.tools.build:gradle:2.3.0'
-        classpath 'org.apache.httpcomponents:httpclient:4.5.2'
+        classpath 'com.android.tools.build:gradle:3.0.1'
+        classpath 'org.apache.httpcomponents:httpclient:4.5.4'
     }
 }
 
@@ -75,7 +75,7 @@ apply plugin: 'com.android.application'
 
 android {
     compileSdkVersion 23
-    buildToolsVersion "25.0.2"
+    buildToolsVersion '26.0.2'
 
     if (nativeBuildSystem == 'cmake') {
         defaultConfig {
diff --git a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar
new file mode 100644
index 0000000000..13372aef5e
--- /dev/null
+++ b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar
diff --git a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties
new file mode 100644
index 0000000000..bd9ee87db3
--- /dev/null
+++ b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties
@@ -0,0 +1,6 @@
+#Sat Nov 18 15:06:47 CET 2017
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-4.1-all.zip
diff --git a/tensorflow/examples/android/gradlew b/tensorflow/examples/android/gradlew
new file mode 100644
index 0000000000..9d82f78915
--- /dev/null
+++ b/tensorflow/examples/android/gradlew
@@ -0,0 +1,160 @@
+#!/usr/bin/env bash
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn ( ) {
+    echo "$*"
+}
+
+die ( ) {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+esac
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
+function splitJvmOpts() {
+    JVM_OPTS=("$@")
+}
+eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
+JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
+
+exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"
diff --git a/tensorflow/examples/android/gradlew.bat b/tensorflow/examples/android/gradlew.bat
new file mode 100644
index 0000000000..8a0b282aa6
--- /dev/null
+++ b/tensorflow/examples/android/gradlew.bat
@@ -0,0 +1,90 @@
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windowz variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+if "%@eval[2+2]" == "4" goto 4NT_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+goto execute
+
+:4NT_args
+@rem Get arguments from the 4NT Shell from JP Software
+set CMD_LINE_ARGS=%$
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
index 4e45f42d0c..8bd4abb154 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
@@ -333,8 +333,12 @@ public abstract class CameraActivity extends Activity
           continue;
         }
 
-        useCamera2API = isHardwareLevelSupported(characteristics,
-            CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_FULL);
+        // Fallback to camera1 API for internal cameras that don't have full support.
+        // This should help with legacy situations where using the camera2 API causes
+        // distorted or otherwise broken previews.
+        useCamera2API = (facing == CameraCharacteristics.LENS_FACING_EXTERNAL)
+            || isHardwareLevelSupported(characteristics, 
+                                        CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_FULL);
         LOGGER.i("Camera API lv2?: %s", useCamera2API);
         return cameraId;
       }
diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
index a9ed02dd1a..9db8835d92 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -45,9 +45,7 @@ TRAIN_FILE = 'train.tfrecords'
 VALIDATION_FILE = 'validation.tfrecords'
 
 
-def read_and_decode(filename_queue):
-  reader = tf.TFRecordReader()
-  _, serialized_example = reader.read(filename_queue)
+def decode(serialized_example):
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -60,22 +58,26 @@ def read_and_decode(filename_queue):
   # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
   # [mnist.IMAGE_PIXELS].
   image = tf.decode_raw(features['image_raw'], tf.uint8)
-  image.set_shape([mnist.IMAGE_PIXELS])
+  image.set_shape((mnist.IMAGE_PIXELS))
 
+  # Convert label from a scalar uint8 tensor to an int32 scalar.
+  label = tf.cast(features['label'], tf.int32)
+  
+  return image, label
+
+def augment(image, label):
   # OPTIONAL: Could reshape into a 28x28 image and apply distortions
   # here.  Since we are not applying any distortions in this
   # example, and the next step expects the image to be flattened
   # into a vector, we don't bother.
+  return image, label
 
+def normalize(image, label):
   # Convert from [0, 255] -> [-0.5, 0.5] floats.
   image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
 
-  # Convert label from a scalar uint8 tensor to an int32 scalar.
-  label = tf.cast(features['label'], tf.int32)
-
   return image, label
 
-
 def inputs(train, batch_size, num_epochs):
   """Reads input data num_epochs times.
 
@@ -91,31 +93,32 @@ def inputs(train, batch_size, num_epochs):
       in the range [-0.5, 0.5].
     * labels is an int32 tensor with shape [batch_size] with the true label,
       a number in the range [0, mnist.NUM_CLASSES).
-    Note that an tf.train.QueueRunner is added to the graph, which
-    must be run using e.g. tf.train.start_queue_runners().
+
+    This function creates a one_shot_iterator, meaning that it will only iterate
+    over the dataset once. On the other hand there is no special initialization
+    required.
   """
   if not num_epochs: num_epochs = None
   filename = os.path.join(FLAGS.train_dir,
                           TRAIN_FILE if train else VALIDATION_FILE)
 
   with tf.name_scope('input'):
-    filename_queue = tf.train.string_input_producer(
-        [filename], num_epochs=num_epochs)
+    # TFRecordDataset opens a protobuf and reads entries line by line
+    # could also be [list, of, filenames]
+    dataset = tf.data.TFRecordDataset(filename)
+    dataset = dataset.repeat(num_epochs)
 
-    # Even when reading in multiple threads, share the filename
-    # queue.
-    image, label = read_and_decode(filename_queue)
+    # map takes a python function and applies it to every sample
+    dataset = dataset.map(decode)
+    dataset = dataset.map(augment)
+    dataset = dataset.map(normalize)
 
-    # Shuffle the examples and collect them into batch_size batches.
-    # (Internally uses a RandomShuffleQueue.)
-    # We run this in two threads to avoid being a bottleneck.
-    images, sparse_labels = tf.train.shuffle_batch(
-        [image, label], batch_size=batch_size, num_threads=2,
-        capacity=1000 + 3 * batch_size,
-        # Ensures a minimum amount of shuffling of examples.
-        min_after_dequeue=1000)
+    #the parameter is the queue size
+    dataset = dataset.shuffle(1000 + 3 * batch_size)
+    dataset = dataset.batch(batch_size)
 
-    return images, sparse_labels
+    iterator = dataset.make_one_shot_iterator()
+  return iterator.get_next()
 
 
 def run_training():
@@ -124,16 +127,16 @@ def run_training():
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
     # Input images and labels.
-    images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
-                            num_epochs=FLAGS.num_epochs)
+    image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size,
+                               num_epochs=FLAGS.num_epochs)
 
     # Build a Graph that computes predictions from the inference model.
-    logits = mnist.inference(images,
+    logits = mnist.inference(image_batch,
                              FLAGS.hidden1,
                              FLAGS.hidden2)
 
     # Add to the Graph the loss calculation.
-    loss = mnist.loss(logits, labels)
+    loss = mnist.loss(logits, label_batch)
 
     # Add to the Graph operations that train the model.
     train_op = mnist.training(loss, FLAGS.learning_rate)
@@ -143,47 +146,33 @@ def run_training():
                        tf.local_variables_initializer())
 
     # Create a session for running operations in the Graph.
-    sess = tf.Session()
-
-    # Initialize the variables (the trained variables and the
-    # epoch counter).
-    sess.run(init_op)
-
-    # Start input enqueue threads.
-    coord = tf.train.Coordinator()
-    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-
-    try:
-      step = 0
-      while not coord.should_stop():
-        start_time = time.time()
-
-        # Run one step of the model.  The return values are
-        # the activations from the `train_op` (which is
-        # discarded) and the `loss` op.  To inspect the values
-        # of your ops or variables, you may include them in
-        # the list passed to sess.run() and the value tensors
-        # will be returned in the tuple from the call.
-        _, loss_value = sess.run([train_op, loss])
-
-        duration = time.time() - start_time
-
-        # Print an overview fairly often.
-        if step % 100 == 0:
-          print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
+    with tf.Session() as sess:
+      # Initialize the variables (the trained variables and the
+      # epoch counter).
+      sess.run(init_op)
+      try:
+        step = 0
+        while True: #train until OutOfRangeError
+          start_time = time.time()
+
+          # Run one step of the model.  The return values are
+          # the activations from the `train_op` (which is
+          # discarded) and the `loss` op.  To inspect the values
+          # of your ops or variables, you may include them in
+          # the list passed to sess.run() and the value tensors
+          # will be returned in the tuple from the call.
+          _, loss_value = sess.run([train_op, loss])
+
+          duration = time.time() - start_time
+
+          # Print an overview fairly often.
+          if step % 100 == 0:
+            print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
                                                      duration))
-        step += 1
-    except tf.errors.OutOfRangeError:
-      print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
-    finally:
-      # When done, ask the threads to stop.
-      coord.request_stop()
-
-    # Wait for threads to finish.
-    coord.join(threads)
-    sess.close()
-
-
+          step += 1
+      except tf.errors.OutOfRangeError:
+        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
+      
 def main(_):
   run_training()
 
diff --git a/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc b/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc
index 1e375ed48e..4a429837b7 100644
--- a/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc
+++ b/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc
@@ -53,7 +53,8 @@ tensorflow::Status WavToSpectrogram(const tensorflow::string& input_wav,
   //  - Scales, clamps, and converts that spectrogram to 0 to 255 uint8's.
   //  - Reshapes the tensor so that it's [height, width, 1] for imaging.
   //  - Encodes it as a PNG stream and saves it out to a file.
-  Output file_reader = ReadFile(root.WithOpName("input_wav"), input_wav);
+  Output file_reader =
+      tensorflow::ops::ReadFile(root.WithOpName("input_wav"), input_wav);
   DecodeWav wav_decoder =
       DecodeWav(root.WithOpName("wav_decoder"), file_reader);
   Output spectrogram = AudioSpectrogram(root.WithOpName("spectrogram"),
@@ -71,8 +72,8 @@ tensorflow::Status WavToSpectrogram(const tensorflow::string& input_wav,
   Output squeeze = Squeeze(root.WithOpName("squeeze"), expand_dims,
                            Squeeze::Attrs().Axis({0}));
   Output png_encoder = EncodePng(root.WithOpName("png_encoder"), squeeze);
-  WriteFile file_writer =
-      WriteFile(root.WithOpName("output_image"), output_image, png_encoder);
+  tensorflow::ops::WriteFile file_writer = tensorflow::ops::WriteFile(
+      root.WithOpName("output_image"), output_image, png_encoder);
   tensorflow::GraphDef graph;
   TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));