Automated g4 rollback of changelist 179258973

PiperOrigin-RevId: 179260538

8 files changed, 76 insertions, 326 deletions

diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle
index f7bdf8b816..48f566f825 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:3.0.1'
-        classpath 'org.apache.httpcomponents:httpclient:4.5.4'
+        classpath 'com.android.tools.build:gradle:2.3.0'
+        classpath 'org.apache.httpcomponents:httpclient:4.5.2'
     }
 }
 
@@ -75,7 +75,7 @@ apply plugin: 'com.android.application'
 
 android {
     compileSdkVersion 23
-    buildToolsVersion '26.0.2'
+    buildToolsVersion "25.0.2"
 
     if (nativeBuildSystem == 'cmake') {
         defaultConfig {
diff --git a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar
deleted file mode 100644
index 13372aef5e..0000000000
--- a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar
+++ /dev/null
diff --git a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties
deleted file mode 100644
index bd9ee87db3..0000000000
--- a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.properties
+++ /dev/null
@@ -1,6 +0,0 @@
-#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
deleted file mode 100644
index 9d82f78915..0000000000
--- a/tensorflow/examples/android/gradlew
+++ /dev/null
@@ -1,160 +0,0 @@
-#!/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 "$@"
diff --git a/tensorflow/examples/android/gradlew.bat b/tensorflow/examples/android/gradlew.bat
deleted file mode 100644
index 8a0b282aa6..0000000000
--- a/tensorflow/examples/android/gradlew.bat
+++ /dev/null
@@ -1,90 +0,0 @@
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  Gradle startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto init
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto init
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:init
-@rem Get command-line arguments, handling Windowz variants
-
-if not "%OS%" == "Windows_NT" goto win9xME_args
-if "%@eval[2+2]" == "4" goto 4NT_args
-
-:win9xME_args
-@rem Slurp the command line arguments.
-set CMD_LINE_ARGS=
-set _SKIP=2
-
-:win9xME_args_slurp
-if "x%~1" == "x" goto execute
-
-set CMD_LINE_ARGS=%*
-goto execute
-
-:4NT_args
-@rem Get arguments from the 4NT Shell from JP Software
-set CMD_LINE_ARGS=%$
-
-: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 8bd4abb154..4e45f42d0c 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java
@@ -333,12 +333,8 @@ public abstract class CameraActivity extends Activity
           continue;
         }
 
-        // 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);
+        useCamera2API = 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 9db8835d92..a9ed02dd1a 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -45,7 +45,9 @@ TRAIN_FILE = 'train.tfrecords'
 VALIDATION_FILE = 'validation.tfrecords'
 
 
-def decode(serialized_example):
+def read_and_decode(filename_queue):
+  reader = tf.TFRecordReader()
+  _, serialized_example = reader.read(filename_queue)
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -58,26 +60,22 @@ def decode(serialized_example):
   # 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.
 
@@ -93,32 +91,31 @@ 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).
-
-    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.
+    Note that an tf.train.QueueRunner is added to the graph, which
+    must be run using e.g. tf.train.start_queue_runners().
   """
   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'):
-    # 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)
+    filename_queue = tf.train.string_input_producer(
+        [filename], num_epochs=num_epochs)
 
-    # map takes a python function and applies it to every sample
-    dataset = dataset.map(decode)
-    dataset = dataset.map(augment)
-    dataset = dataset.map(normalize)
+    # Even when reading in multiple threads, share the filename
+    # queue.
+    image, label = read_and_decode(filename_queue)
 
-    #the parameter is the queue size
-    dataset = dataset.shuffle(1000 + 3 * batch_size)
-    dataset = dataset.batch(batch_size)
+    # 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)
 
-    iterator = dataset.make_one_shot_iterator()
-  return iterator.get_next()
+    return images, sparse_labels
 
 
 def run_training():
@@ -127,16 +124,16 @@ def run_training():
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
     # Input images and labels.
-    image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size,
-                               num_epochs=FLAGS.num_epochs)
+    images, labels = 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(image_batch,
+    logits = mnist.inference(images,
                              FLAGS.hidden1,
                              FLAGS.hidden2)
 
     # Add to the Graph the loss calculation.
-    loss = mnist.loss(logits, label_batch)
+    loss = mnist.loss(logits, labels)
 
     # Add to the Graph operations that train the model.
     train_op = mnist.training(loss, FLAGS.learning_rate)
@@ -146,33 +143,47 @@ def run_training():
                        tf.local_variables_initializer())
 
     # Create a session for running operations in the Graph.
-    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,
+    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,
                                                      duration))
-          step += 1
-      except tf.errors.OutOfRangeError:
-        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
-      
+        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()
+
+
 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 4a429837b7..1e375ed48e 100644
--- a/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc
+++ b/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.cc
@@ -53,8 +53,7 @@ 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 =
-      tensorflow::ops::ReadFile(root.WithOpName("input_wav"), input_wav);
+  Output file_reader = ReadFile(root.WithOpName("input_wav"), input_wav);
   DecodeWav wav_decoder =
       DecodeWav(root.WithOpName("wav_decoder"), file_reader);
   Output spectrogram = AudioSpectrogram(root.WithOpName("spectrogram"),
@@ -72,8 +71,8 @@ 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);
-  tensorflow::ops::WriteFile file_writer = tensorflow::ops::WriteFile(
-      root.WithOpName("output_image"), output_image, png_encoder);
+  WriteFile file_writer =
+      WriteFile(root.WithOpName("output_image"), output_image, png_encoder);
   tensorflow::GraphDef graph;
   TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));