diff options
Diffstat (limited to 'tensorflow/examples')
109 files changed, 1663 insertions, 2419 deletions
diff --git a/tensorflow/examples/adding_an_op/BUILD b/tensorflow/examples/adding_an_op/BUILD index b3ed6589ed..cf8054be6a 100644 --- a/tensorflow/examples/adding_an_op/BUILD +++ b/tensorflow/examples/adding_an_op/BUILD @@ -139,15 +139,3 @@ tf_cc_binary( "//tensorflow/core:framework", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/android/AndroidManifest.xml b/tensorflow/examples/android/AndroidManifest.xml index bb75431a1f..5c47ce6b67 100644 --- a/tensorflow/examples/android/AndroidManifest.xml +++ b/tensorflow/examples/android/AndroidManifest.xml @@ -40,6 +40,7 @@ <intent-filter> <action android:name="android.intent.action.MAIN" /> <category android:name="android.intent.category.LAUNCHER" /> + <category android:name="android.intent.category.LEANBACK_LAUNCHER" /> </intent-filter> </activity> @@ -49,6 +50,7 @@ <intent-filter> <action android:name="android.intent.action.MAIN" /> <category android:name="android.intent.category.LAUNCHER" /> + <category android:name="android.intent.category.LEANBACK_LAUNCHER" /> </intent-filter> </activity> @@ -58,6 +60,7 @@ <intent-filter> <action android:name="android.intent.action.MAIN" /> <category android:name="android.intent.category.LAUNCHER" /> + <category android:name="android.intent.category.LEANBACK_LAUNCHER" /> </intent-filter> </activity> @@ -67,6 +70,7 @@ <intent-filter> <action android:name="android.intent.action.MAIN" /> <category android:name="android.intent.category.LAUNCHER" /> + <category android:name="android.intent.category.LEANBACK_LAUNCHER" /> </intent-filter> </activity> </application> diff --git a/tensorflow/examples/android/BUILD b/tensorflow/examples/android/BUILD index 46df5973e8..f327b645f5 100644 --- a/tensorflow/examples/android/BUILD +++ b/tensorflow/examples/android/BUILD @@ -1,6 +1,8 @@ # Description: # TensorFlow camera demo app for Android. +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 @@ -36,7 +38,7 @@ cc_binary( "-z defs", "-s", "-Wl,--version-script", # This line must be directly followed by LINKER_SCRIPT. - LINKER_SCRIPT, + "$(location {})".format(LINKER_SCRIPT), ], linkshared = 1, linkstatic = 1, @@ -76,7 +78,6 @@ android_binary( custom_package = "org.tensorflow.demo", inline_constants = 1, manifest = "AndroidManifest.xml", - manifest_merger = "legacy", resource_files = glob(["res/**"]), tags = [ "manual", @@ -92,7 +93,7 @@ android_binary( filegroup( name = "external_assets", srcs = [ - "@inception5h//:model_files", + "@inception_v1//:model_files", "@mobile_ssd//:model_files", "@speech_commands//:model_files", "@stylize//:model_files", @@ -101,22 +102,6 @@ filegroup( # LINT.ThenChange(//tensorflow/examples/android/download-models.gradle) filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - "gradleBuild/**", - "libs/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) - -filegroup( name = "java_files", srcs = glob(["src/**/*.java"]), ) diff --git a/tensorflow/examples/android/README.md b/tensorflow/examples/android/README.md index 79202a38d7..dac9b7ab82 100644 --- a/tensorflow/examples/android/README.md +++ b/tensorflow/examples/android/README.md @@ -45,11 +45,7 @@ on API >= 14 devices. ## Prebuilt Components: -If you just want the fastest path to trying the demo, you may download the -nightly build -[here](https://ci.tensorflow.org/view/Nightly/job/nightly-android/). Expand the -"View" and then the "out" folders under "Last Successful Artifacts" to find -tensorflow_demo.apk. +The fastest path to trying the demo is to download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). Also available are precompiled native libraries, and a jcenter package that you may simply drop into your own applications. See @@ -113,8 +109,7 @@ protobuf compilation. NOTE: Bazel does not currently support building for Android on Windows. Full support for gradle/cmake builds is coming soon, but in the meantime we suggest -that Windows users download the [prebuilt -binaries](https://ci.tensorflow.org/view/Nightly/job/nightly-android/) instead. +that Windows users download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) instead. ##### Install Bazel and Android Prerequisites @@ -126,6 +121,10 @@ the Android NDK and SDK must be installed on your system. 2. The Android NDK is required to build the native (C/C++) TensorFlow code. The current recommended version is 14b, which may be found [here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-14b-downloads). + + * NDK 16, the revision released in November 2017, is **incompatible** with + Bazel. See [here](https://github.com/tensorflow/tensorflow/issues/14918). + 3. The Android SDK and build tools may be obtained [here](https://developer.android.com/tools/revisions/build-tools.html), or alternatively as part of [Android @@ -133,8 +132,16 @@ the Android NDK and SDK must be installed on your system. 23 is required to build the TF Android demo (though it will run on API >= 21 devices). + - The Android Studio SDK Manager's NDK installer will install the latest + revision of the NDK, which is **incompatible** with Bazel. You'll need + to download an older version manually, as (2) suggests. + ##### Edit WORKSPACE +NOTE: As long as you have the SDK and NDK installed, the `./configure` script +will create these rules for you. Answer "Yes" when the script asks to +automatically configure the `./WORKSPACE`. + The Android entries in [`<workspace_root>/WORKSPACE`](../../../WORKSPACE#L19-L36) must be uncommented with the paths filled in appropriately depending on where you installed the NDK @@ -156,7 +163,7 @@ download-models.gradle. **Optional**: If you wish to place the models in your assets manually, remove all of the `model_files` entries from the `assets` list in `tensorflow_demo` -found in the `[BUILD](BUILD)` file. Then download and extract the archives +found in the [`BUILD`](BUILD#L92) file. Then download and extract the archives yourself to the `assets` directory in the source tree: ```bash diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle index 48f566f825..0767726aa9 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' } } @@ -56,10 +56,12 @@ def nativeOutDir = 'libs/' + cpuType def nativeBuildRule = 'buildNativeBazel' def demoLibPath = '../../../bazel-bin/tensorflow/examples/android/libtensorflow_demo.so' def inferenceLibPath = '../../../bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so' + +// Override for Makefile builds. if (nativeBuildSystem == 'makefile') { nativeBuildRule = 'buildNativeMake' - demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_demo.so' - inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_inference.so' + demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_demo.so' + inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_inference.so' } // If building with Bazel, this is the location of the bazel binary. @@ -75,7 +77,7 @@ apply plugin: 'com.android.application' android { compileSdkVersion 23 - buildToolsVersion "25.0.2" + buildToolsVersion '26.0.2' if (nativeBuildSystem == 'cmake') { defaultConfig { @@ -154,7 +156,8 @@ task buildNativeMake(type: Exec) { '-s', \ 'tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in', \ '-t', \ - 'libtensorflow_inference.so libtensorflow_demo.so' \ + 'libtensorflow_inference.so libtensorflow_demo.so all' \ + , '-a', cpuType \ //, '-T' // Uncomment to skip protobuf and speed up subsequent builds. } diff --git a/tensorflow/examples/android/download-models.gradle b/tensorflow/examples/android/download-models.gradle index 0e2cf65f53..d3b67eab52 100644 --- a/tensorflow/examples/android/download-models.gradle +++ b/tensorflow/examples/android/download-models.gradle @@ -9,7 +9,7 @@ */ // hard coded model files // LINT.IfChange -def models = ['inception5h.zip', +def models = ['inception_v1.zip', 'object_detection/ssd_mobilenet_v1_android_export.zip', 'stylize_v1.zip', 'speech_commands_conv_actions.zip'] diff --git a/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar b/tensorflow/examples/android/gradle/wrapper/gradle-wrapper.jar Binary files differnew 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/jni/object_tracking/config.h b/tensorflow/examples/android/jni/object_tracking/config.h index 86e9fc71b6..47de2d2c15 100644 --- a/tensorflow/examples/android/jni/object_tracking/config.h +++ b/tensorflow/examples/android/jni/object_tracking/config.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ #include <math.h> @@ -297,4 +297,4 @@ struct TrackerConfig { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_CONFIG_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/flow_cache.h b/tensorflow/examples/android/jni/object_tracking/flow_cache.h index 8813ab6d71..b62e334ecd 100644 --- a/tensorflow/examples/android/jni/object_tracking/flow_cache.h +++ b/tensorflow/examples/android/jni/object_tracking/flow_cache.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ #include "tensorflow/examples/android/jni/object_tracking/geom.h" #include "tensorflow/examples/android/jni/object_tracking/utils.h" @@ -303,4 +303,4 @@ class FlowCache { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FLOW_CACHE_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/frame_pair.h b/tensorflow/examples/android/jni/object_tracking/frame_pair.h index 8f409fe806..6c8ac9be98 100644 --- a/tensorflow/examples/android/jni/object_tracking/frame_pair.h +++ b/tensorflow/examples/android/jni/object_tracking/frame_pair.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ #include "tensorflow/examples/android/jni/object_tracking/keypoint.h" @@ -100,4 +100,4 @@ class FramePair { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_FRAME_PAIR_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/geom.h b/tensorflow/examples/android/jni/object_tracking/geom.h index 2819063616..c975e40144 100644 --- a/tensorflow/examples/android/jni/object_tracking/geom.h +++ b/tensorflow/examples/android/jni/object_tracking/geom.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ #include "tensorflow/examples/android/jni/object_tracking/logging.h" #include "tensorflow/examples/android/jni/object_tracking/utils.h" @@ -316,4 +316,4 @@ inline BoundingSquare GetCenteredSquare(const BoundingBox& original_box) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GEOM_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/gl_utils.h b/tensorflow/examples/android/jni/object_tracking/gl_utils.h index bd5c233f4f..a29e677d3c 100755 --- a/tensorflow/examples/android/jni/object_tracking/gl_utils.h +++ b/tensorflow/examples/android/jni/object_tracking/gl_utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ #include <GLES/gl.h> #include <GLES/glext.h> @@ -52,4 +52,4 @@ inline static void MapWorldSquareToUnitSquare(const BoundingSquare& square) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_GL_UTILS_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/image-inl.h b/tensorflow/examples/android/jni/object_tracking/image-inl.h index 9c4c389aa7..61d69908b5 100644 --- a/tensorflow/examples/android/jni/object_tracking/image-inl.h +++ b/tensorflow/examples/android/jni/object_tracking/image-inl.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ #include <stdint.h> @@ -641,4 +641,4 @@ inline void Image<T>::FromArray(const T* const pixels, const int stride, } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_INL_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/image.h b/tensorflow/examples/android/jni/object_tracking/image.h index b7a2301f5e..a436f0e0a1 100644 --- a/tensorflow/examples/android/jni/object_tracking/image.h +++ b/tensorflow/examples/android/jni/object_tracking/image.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ #include <stdint.h> @@ -338,4 +338,4 @@ inline std::ostream& operator<<(std::ostream& stream, const Image<t>& image) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/image_data.h b/tensorflow/examples/android/jni/object_tracking/image_data.h index 445cdb57a3..c4f91d8cbd 100644 --- a/tensorflow/examples/android/jni/object_tracking/image_data.h +++ b/tensorflow/examples/android/jni/object_tracking/image_data.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ #include <stdint.h> #include <memory> @@ -261,4 +261,4 @@ class ImageData { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_DATA_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/image_utils.h b/tensorflow/examples/android/jni/object_tracking/image_utils.h index ac9ffd90f8..b4ad7000b3 100644 --- a/tensorflow/examples/android/jni/object_tracking/image_utils.h +++ b/tensorflow/examples/android/jni/object_tracking/image_utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ #include <stdint.h> @@ -295,4 +295,4 @@ inline void NormalizeImage(Image<float>* const image) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_IMAGE_UTILS_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/integral_image.h b/tensorflow/examples/android/jni/object_tracking/integral_image.h index 8e82334abf..caf9b7d2ab 100755 --- a/tensorflow/examples/android/jni/object_tracking/integral_image.h +++ b/tensorflow/examples/android/jni/object_tracking/integral_image.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ #include "tensorflow/examples/android/jni/object_tracking/geom.h" #include "tensorflow/examples/android/jni/object_tracking/image-inl.h" @@ -184,4 +184,4 @@ class IntegralImage : public Image<uint32_t> { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_INTEGRAL_IMAGE_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/jni_utils.h b/tensorflow/examples/android/jni/object_tracking/jni_utils.h index 21fbabb521..b81d9e0c12 100644 --- a/tensorflow/examples/android/jni/object_tracking/jni_utils.h +++ b/tensorflow/examples/android/jni/object_tracking/jni_utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_JNI_UTILS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_JNI_UTILS_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_JNI_UTILS_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_JNI_UTILS_H_ #include <stdint.h> diff --git a/tensorflow/examples/android/jni/object_tracking/keypoint.h b/tensorflow/examples/android/jni/object_tracking/keypoint.h index 719f9aff3f..93405a5b2a 100644 --- a/tensorflow/examples/android/jni/object_tracking/keypoint.h +++ b/tensorflow/examples/android/jni/object_tracking/keypoint.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ #include "tensorflow/examples/android/jni/object_tracking/geom.h" #include "tensorflow/examples/android/jni/object_tracking/image-inl.h" @@ -45,4 +45,4 @@ inline std::ostream& operator<<(std::ostream& stream, const Keypoint keypoint) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/keypoint_detector.h b/tensorflow/examples/android/jni/object_tracking/keypoint_detector.h index 33d228128d..2e85b835a7 100644 --- a/tensorflow/examples/android/jni/object_tracking/keypoint_detector.h +++ b/tensorflow/examples/android/jni/object_tracking/keypoint_detector.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ #include <stdint.h> #include <vector> @@ -125,4 +125,4 @@ class KeypointDetector { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_KEYPOINT_DETECTOR_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/logging.h b/tensorflow/examples/android/jni/object_tracking/logging.h index dbc89af2f7..852a749399 100644 --- a/tensorflow/examples/android/jni/object_tracking/logging.h +++ b/tensorflow/examples/android/jni/object_tracking/logging.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ #include <android/log.h> #include <string.h> @@ -118,4 +118,4 @@ void LogPrintF(const int severity, const char* format, ...); #endif -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_LOG_STREAMING_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/object_detector.h b/tensorflow/examples/android/jni/object_tracking/object_detector.h index 2525567678..a65c7b0db7 100644 --- a/tensorflow/examples/android/jni/object_tracking/object_detector.h +++ b/tensorflow/examples/android/jni/object_tracking/object_detector.h @@ -20,8 +20,8 @@ limitations under the License. // Defines the ObjectDetector class that is the main interface for detecting // ObjectModelBases in frames. -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ #include <float.h> #include <map> @@ -227,4 +227,4 @@ class ObjectDetector : public ObjectDetectorBase { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_DETECTOR_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/object_model.h b/tensorflow/examples/android/jni/object_tracking/object_model.h index be33aea638..5e81c49080 100644 --- a/tensorflow/examples/android/jni/object_tracking/object_model.h +++ b/tensorflow/examples/android/jni/object_tracking/object_model.h @@ -19,8 +19,8 @@ limitations under the License. // Contains ObjectModelBase declaration. -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ #ifdef __RENDER_OPENGL__ #include <GLES/gl.h> @@ -99,4 +99,4 @@ class ObjectModel : public ObjectModelBase { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_DETECTION_OBJECT_MODEL_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/object_tracker.h b/tensorflow/examples/android/jni/object_tracking/object_tracker.h index eb281fad37..20c7627fc5 100644 --- a/tensorflow/examples/android/jni/object_tracking/object_tracker.h +++ b/tensorflow/examples/android/jni/object_tracking/object_tracker.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ #include <map> #include <string> @@ -267,4 +267,4 @@ inline std::ostream& operator<<(std::ostream& stream, } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OBJECT_TRACKER_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/optical_flow.h b/tensorflow/examples/android/jni/object_tracking/optical_flow.h index 2206375beb..f98ae22bd6 100644 --- a/tensorflow/examples/android/jni/object_tracking/optical_flow.h +++ b/tensorflow/examples/android/jni/object_tracking/optical_flow.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ #include "tensorflow/examples/android/jni/object_tracking/geom.h" #include "tensorflow/examples/android/jni/object_tracking/image-inl.h" @@ -97,4 +97,4 @@ class OpticalFlow { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_OPTICAL_FLOW_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/sprite.h b/tensorflow/examples/android/jni/object_tracking/sprite.h index 05a13fea11..b54a68458f 100755 --- a/tensorflow/examples/android/jni/object_tracking/sprite.h +++ b/tensorflow/examples/android/jni/object_tracking/sprite.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ #include <GLES/gl.h> #include <GLES/glext.h> @@ -199,4 +199,4 @@ class Sprite { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_SPRITE_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/time_log.h b/tensorflow/examples/android/jni/object_tracking/time_log.h index 60911da396..0073e11596 100644 --- a/tensorflow/examples/android/jni/object_tracking/time_log.h +++ b/tensorflow/examples/android/jni/object_tracking/time_log.h @@ -15,8 +15,8 @@ limitations under the License. // Utility functions for performance profiling. -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ #include <stdint.h> @@ -134,4 +134,4 @@ inline static void TimeLog(const char* const str) { inline static void PrintTimeLog() {} #endif -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TIME_LOG_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/tracked_object.h b/tensorflow/examples/android/jni/object_tracking/tracked_object.h index cda14e19d2..d7f1a7019b 100644 --- a/tensorflow/examples/android/jni/object_tracking/tracked_object.h +++ b/tensorflow/examples/android/jni/object_tracking/tracked_object.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ #ifdef __RENDER_OPENGL__ #include "tensorflow/examples/android/jni/object_tracking/gl_utils.h" @@ -183,4 +183,4 @@ inline std::ostream& operator<<(std::ostream& stream, } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_TRACKED_OBJECT_H_ diff --git a/tensorflow/examples/android/jni/object_tracking/utils.h b/tensorflow/examples/android/jni/object_tracking/utils.h index 51cdfcdcfb..2e98734ec4 100644 --- a/tensorflow/examples/android/jni/object_tracking/utils.h +++ b/tensorflow/examples/android/jni/object_tracking/utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ +#ifndef TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ +#define TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ #include <math.h> #include <stdint.h> @@ -378,4 +378,4 @@ inline bool Invert2x2(const T* const a, float* const a_inv) { } // namespace tf_tracking -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ +#endif // TENSORFLOW_EXAMPLES_ANDROID_JNI_OBJECT_TRACKING_UTILS_H_ diff --git a/tensorflow/examples/android/res/animator/color_animation.xml b/tensorflow/examples/android/res/animator/color_animation.xml new file mode 100644 index 0000000000..891d8cc1d4 --- /dev/null +++ b/tensorflow/examples/android/res/animator/color_animation.xml @@ -0,0 +1,30 @@ +<?xml version="1.0" encoding="utf-8"?><!-- + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +--> +<set xmlns:android="http://schemas.android.com/apk/res/android" + android:ordering="sequentially"> + <objectAnimator + android:propertyName="backgroundColor" + android:duration="375" + android:valueFrom="0x00b3ccff" + android:valueTo="0xffb3ccff" + android:valueType="colorType"/> + <objectAnimator + android:propertyName="backgroundColor" + android:duration="375" + android:valueFrom="0xffb3ccff" + android:valueTo="0x00b3ccff" + android:valueType="colorType"/> +</set> diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/CameraActivity.java index 4e45f42d0c..429138abe5 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; } @@ -347,6 +351,10 @@ public abstract class CameraActivity extends Activity protected void setFragment() { String cameraId = chooseCamera(); + if (cameraId == null) { + Toast.makeText(this, "No Camera Detected", Toast.LENGTH_SHORT).show(); + finish(); + } Fragment fragment; if (useCamera2API) { @@ -412,7 +420,8 @@ public abstract class CameraActivity extends Activity @Override public boolean onKeyDown(final int keyCode, final KeyEvent event) { - if (keyCode == KeyEvent.KEYCODE_VOLUME_DOWN || keyCode == KeyEvent.KEYCODE_VOLUME_UP) { + if (keyCode == KeyEvent.KEYCODE_VOLUME_DOWN || keyCode == KeyEvent.KEYCODE_VOLUME_UP + || keyCode == KeyEvent.KEYCODE_BUTTON_L1 || keyCode == KeyEvent.KEYCODE_DPAD_CENTER) { debug = !debug; requestRender(); onSetDebug(debug); diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java b/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java index eabc724f7f..07995febaf 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java @@ -100,7 +100,7 @@ public interface Classifier { List<Recognition> recognizeImage(Bitmap bitmap); void enableStatLogging(final boolean debug); - + String getStatString(); void close(); diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java index a317273acd..068c7b0d94 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java @@ -81,8 +81,11 @@ public class LegacyCameraConnectionFragment extends Fragment { try { Camera.Parameters parameters = camera.getParameters(); - parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); - + List<String> focusModes = parameters.getSupportedFocusModes(); + if (focusModes != null + && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) { + parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); + } List<Camera.Size> cameraSizes = parameters.getSupportedPreviewSizes(); Size[] sizes = new Size[cameraSizes.size()]; int i = 0; diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java index 184df1bdb4..1cddf3dc55 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java @@ -31,7 +31,8 @@ the RecognizeCommands helper class. package org.tensorflow.demo; -import android.animation.ValueAnimator; +import android.animation.AnimatorInflater; +import android.animation.AnimatorSet; import android.app.Activity; import android.content.pm.PackageManager; import android.media.AudioFormat; @@ -329,17 +330,13 @@ public class SpeechActivity extends Activity { labelIndex = i; } } - final View labelView = (View) labelsListView.getChildAt(labelIndex - 2); - ValueAnimator colorAnimation = - ValueAnimator.ofArgb(0x00b3ccff, 0xffb3ccff, 0x00b3ccff); - colorAnimation.setDuration(750); - colorAnimation.addUpdateListener( - new ValueAnimator.AnimatorUpdateListener() { - @Override - public void onAnimationUpdate(ValueAnimator animator) { - labelView.setBackgroundColor((int) animator.getAnimatedValue()); - } - }); + final View labelView = labelsListView.getChildAt(labelIndex - 2); + + AnimatorSet colorAnimation = + (AnimatorSet) + AnimatorInflater.loadAnimator( + SpeechActivity.this, R.animator.color_animation); + colorAnimation.setTarget(labelView); colorAnimation.start(); } } diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java index 6a66ec3927..33ec65e9f7 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/StylizeActivity.java @@ -16,8 +16,10 @@ package org.tensorflow.demo; +import android.app.UiModeManager; import android.content.Context; import android.content.res.AssetManager; +import android.content.res.Configuration; import android.graphics.Bitmap; import android.graphics.Bitmap.Config; import android.graphics.BitmapFactory; @@ -31,9 +33,11 @@ import android.graphics.Typeface; import android.media.ImageReader.OnImageAvailableListener; import android.os.Bundle; import android.os.SystemClock; +import android.util.DisplayMetrics; import android.util.Size; import android.util.TypedValue; import android.view.Display; +import android.view.KeyEvent; import android.view.MotionEvent; import android.view.View; import android.view.View.OnClickListener; @@ -43,6 +47,7 @@ import android.widget.BaseAdapter; import android.widget.Button; import android.widget.GridView; import android.widget.ImageView; +import android.widget.RelativeLayout; import android.widget.Toast; import java.io.IOException; import java.io.InputStream; @@ -381,6 +386,27 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL grid = (GridView) findViewById(R.id.grid_layout); grid.setAdapter(adapter); grid.setOnTouchListener(gridTouchAdapter); + + // Change UI on Android TV + UiModeManager uiModeManager = (UiModeManager) getSystemService(UI_MODE_SERVICE); + if (uiModeManager.getCurrentModeType() == Configuration.UI_MODE_TYPE_TELEVISION) { + DisplayMetrics displayMetrics = new DisplayMetrics(); + getWindowManager().getDefaultDisplay().getMetrics(displayMetrics); + int styleSelectorHeight = displayMetrics.heightPixels; + int styleSelectorWidth = displayMetrics.widthPixels - styleSelectorHeight; + RelativeLayout.LayoutParams layoutParams = new RelativeLayout.LayoutParams(styleSelectorWidth, ViewGroup.LayoutParams.MATCH_PARENT); + + // Calculate number of style in a row, so all the style can show up without scrolling + int numOfStylePerRow = 3; + while (styleSelectorWidth / numOfStylePerRow * Math.ceil((float) (adapter.getCount() - 2) / numOfStylePerRow) > styleSelectorHeight) { + numOfStylePerRow++; + } + grid.setNumColumns(numOfStylePerRow); + layoutParams.addRule(RelativeLayout.ALIGN_PARENT_RIGHT); + grid.setLayoutParams(layoutParams); + adapter.buttons.clear(); + } + setStyle(adapter.items[0], 1.0f); } @@ -602,4 +628,38 @@ public class StylizeActivity extends CameraActivity implements OnImageAvailableL borderedText.drawLines(canvas, 10, canvas.getHeight() - 10, lines); } + + @Override + public boolean onKeyDown(int keyCode, KeyEvent event) { + int moveOffset = 0; + switch (keyCode) { + case KeyEvent.KEYCODE_DPAD_LEFT: + moveOffset = -1; + break; + case KeyEvent.KEYCODE_DPAD_RIGHT: + moveOffset = 1; + break; + case KeyEvent.KEYCODE_DPAD_UP: + moveOffset = -1 * grid.getNumColumns(); + break; + case KeyEvent.KEYCODE_DPAD_DOWN: + moveOffset = grid.getNumColumns(); + break; + default: + return super.onKeyDown(keyCode, event); + } + + // get the highest selected style + int currentSelect = 0; + float highestValue = 0; + for (int i = 0; i < adapter.getCount(); i++) { + if (adapter.items[i].value > highestValue) { + currentSelect = i; + highestValue = adapter.items[i].value; + } + } + setStyle(adapter.items[(currentSelect + moveOffset + adapter.getCount()) % adapter.getCount()], 1); + + return true; + } } diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java b/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java index 614d3c7dd7..9739e58018 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java @@ -137,7 +137,7 @@ public class TensorFlowObjectDetectionAPIModel implements Classifier { Trace.beginSection("recognizeImage"); Trace.beginSection("preprocessBitmap"); - // Preprocess the image data from 0-255 int to normalized float based + // Preprocess the image data to extract R, G and B bytes from int of form 0x00RRGGBB // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java index 2fe2ba539e..af6af2bc8f 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java @@ -199,7 +199,7 @@ public class MultiBoxTracker { final int w, final int h, final int rowStride, - final int sensorOrienation, + final int sensorOrientation, final byte[] frame, final long timestamp) { if (objectTracker == null && !initialized) { @@ -209,7 +209,7 @@ public class MultiBoxTracker { objectTracker = ObjectTracker.getInstance(w, h, rowStride, true); frameWidth = w; frameHeight = h; - this.sensorOrientation = sensorOrienation; + this.sensorOrientation = sensorOrientation; initialized = true; if (objectTracker == null) { diff --git a/tensorflow/examples/benchmark/BUILD b/tensorflow/examples/benchmark/BUILD index c4bb0a5bd9..98611a9aad 100644 --- a/tensorflow/examples/benchmark/BUILD +++ b/tensorflow/examples/benchmark/BUILD @@ -23,9 +23,3 @@ tf_py_logged_benchmark( name = "sample_logged_benchmark", target = "//tensorflow/examples/benchmark:sample_benchmark", ) - -filegroup( - name = "all_files", - srcs = glob(["**/*"]), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/get_started/regression/BUILD b/tensorflow/examples/get_started/regression/BUILD index 577b970c90..bee94d7d90 100644 --- a/tensorflow/examples/get_started/regression/BUILD +++ b/tensorflow/examples/get_started/regression/BUILD @@ -2,18 +2,6 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) - py_test( name = "test", size = "medium", diff --git a/tensorflow/examples/get_started/regression/imports85.py b/tensorflow/examples/get_started/regression/imports85.py index 6bee556eb8..4fdaceea9a 100644 --- a/tensorflow/examples/get_started/regression/imports85.py +++ b/tensorflow/examples/get_started/regression/imports85.py @@ -131,11 +131,12 @@ def dataset(y_name="price", train_fraction=0.7): # booleans but we are dealing with symbolic tensors. return ~in_training_set(line) - base_dataset = (tf.contrib.data - # Get the lines from the file. - .TextLineDataset(path) - # drop lines with question marks. - .filter(has_no_question_marks)) + base_dataset = ( + tf.data + # Get the lines from the file. + .TextLineDataset(path) + # drop lines with question marks. + .filter(has_no_question_marks)) train = (base_dataset # Take only the training-set lines. diff --git a/tensorflow/examples/how_tos/reading_data/BUILD b/tensorflow/examples/how_tos/reading_data/BUILD index 4a43585d53..64a054d371 100644 --- a/tensorflow/examples/how_tos/reading_data/BUILD +++ b/tensorflow/examples/how_tos/reading_data/BUILD @@ -54,15 +54,3 @@ py_binary( "//tensorflow/examples/tutorials/mnist:input_data", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/how_tos/reading_data/convert_to_records.py b/tensorflow/examples/how_tos/reading_data/convert_to_records.py index a402eac053..c89e839563 100644 --- a/tensorflow/examples/how_tos/reading_data/convert_to_records.py +++ b/tensorflow/examples/how_tos/reading_data/convert_to_records.py @@ -55,12 +55,15 @@ def convert_to(data_set, name): with tf.python_io.TFRecordWriter(filename) as writer: for index in range(num_examples): image_raw = images[index].tostring() - example = tf.train.Example(features=tf.train.Features(feature={ - 'height': _int64_feature(rows), - 'width': _int64_feature(cols), - 'depth': _int64_feature(depth), - 'label': _int64_feature(int(labels[index])), - 'image_raw': _bytes_feature(image_raw)})) + example = tf.train.Example( + features=tf.train.Features( + feature={ + 'height': _int64_feature(rows), + 'width': _int64_feature(cols), + 'depth': _int64_feature(depth), + 'label': _int64_feature(int(labels[index])), + 'image_raw': _bytes_feature(image_raw) + })) writer.write(example.SerializeToString()) 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..7402247448 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,13 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Train and Eval the MNIST network. This version is like fully_connected_feed.py but uses data converted to a TFRecords file containing tf.train.Example protocol buffers. See: -https://www.tensorflow.org/programmers_guide/reading_data#reading_from_files +https://www.tensorflow.org/guide/reading_data#reading_from_files for context. YOU MUST run convert_to_records before running this (but you only need to @@ -45,9 +44,8 @@ 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): + """Parses an image and label from the given `serialized_example`.""" features = tf.parse_single_example( serialized_example, # Defaults are not specified since both keys are required. @@ -60,19 +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): + """Placeholder for data augmentation.""" # 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 - # 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) +def normalize(image, label): + """Convert `image` from [0, 255] -> [-0.5, 0.5] floats.""" + image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 return image, label @@ -91,31 +96,38 @@ 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) + 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 binary file and reads one record at a time. + # `filename` could also be a list of filenames, which will be read in order. + dataset = tf.data.TFRecordDataset(filename) + + # The map transformation takes a function and applies it to every element + # of the dataset. + 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 shuffle transformation uses a finite-sized buffer to shuffle elements + # in memory. The parameter is the number of elements in the buffer. For + # completely uniform shuffling, set the parameter to be the same as the + # number of elements in the dataset. + dataset = dataset.shuffle(1000 + 3 * 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) + dataset = dataset.repeat(num_epochs) + dataset = dataset.batch(batch_size) - return images, sparse_labels + iterator = dataset.make_one_shot_iterator() + return iterator.get_next() def run_training(): @@ -124,16 +136,14 @@ 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, - FLAGS.hidden1, - FLAGS.hidden2) + 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) @@ -143,45 +153,33 @@ 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, - 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() + 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)) def main(_): @@ -194,37 +192,27 @@ if __name__ == '__main__': '--learning_rate', type=float, default=0.01, - help='Initial learning rate.' - ) + help='Initial learning rate.') parser.add_argument( '--num_epochs', type=int, default=2, - help='Number of epochs to run trainer.' - ) + help='Number of epochs to run trainer.') parser.add_argument( '--hidden1', type=int, default=128, - help='Number of units in hidden layer 1.' - ) + help='Number of units in hidden layer 1.') parser.add_argument( '--hidden2', type=int, default=32, - help='Number of units in hidden layer 2.' - ) - parser.add_argument( - '--batch_size', - type=int, - default=100, - help='Batch size.' - ) + help='Number of units in hidden layer 2.') + parser.add_argument('--batch_size', type=int, default=100, help='Batch size.') parser.add_argument( '--train_dir', type=str, default='/tmp/data', - help='Directory with the training data.' - ) + help='Directory with the training data.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD deleted file mode 100644 index 9f9244a74c..0000000000 --- a/tensorflow/examples/image_retraining/BUILD +++ /dev/null @@ -1,63 +0,0 @@ -# Description: -# Transfer learning example for TensorFlow. - -licenses(["notice"]) # Apache 2.0 - -exports_files(["LICENSE"]) - -load("//tensorflow:tensorflow.bzl", "py_test") - -py_binary( - name = "retrain", - srcs = [ - "retrain.py", - ], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:__subpackages__"], - deps = [ - "//tensorflow:tensorflow_py", - "//tensorflow/python:framework", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:graph_util", - "//tensorflow/python:platform", - "//tensorflow/python:util", - "//third_party/py/numpy", - ], -) - -py_test( - name = "retrain_test", - size = "small", - srcs = [ - "retrain.py", - "retrain_test.py", - ], - data = [ - ":data/labels.txt", - "//tensorflow/examples/label_image:data/grace_hopper.jpg", - ], - srcs_version = "PY2AND3", - deps = [ - ":retrain", - "//tensorflow:tensorflow_py", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:graph_util", - "//tensorflow/python:platform", - "//tensorflow/python:platform_test", - "//tensorflow/python:tensor_shape", - "//tensorflow/python:util", - "//third_party/py/numpy", - ], -) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/image_retraining/README.md b/tensorflow/examples/image_retraining/README.md index 8a49525c6e..3f0b3d1268 100644 --- a/tensorflow/examples/image_retraining/README.md +++ b/tensorflow/examples/image_retraining/README.md @@ -1,12 +1,15 @@ -retrain.py is an example script that shows how one can adapt a pretrained -network for other classification problems. A detailed overview of this script -can be found at: -https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0 +**NOTE: This code has moved to** +https://github.com/tensorflow/hub/tree/master/examples/image_retraining -The script also shows how one can train layers -with quantized weights and activations instead of taking a pre-trained floating -point model and then quantizing weights and activations. -The output graphdef produced by this script is compatible with the TensorFlow -Lite Optimizing Converter and can be converted to TFLite format. +retrain.py is an example script that shows how one can adapt a pretrained +network for other classification problems (including use with TFLite and +quantization). +As of TensorFlow 1.7, it is recommended to use a pretrained network from +TensorFlow Hub, using the new version of this example found in the location +above, as explained in TensorFlow's revised [image retraining +tutorial](https://www.tensorflow.org/tutorials/image_retraining). +Older versions of this example (using frozen GraphDefs instead of +TensorFlow Hub modules) are available in the release branches of +TensorFlow versions up to and including 1.7. diff --git a/tensorflow/examples/image_retraining/data/labels.txt b/tensorflow/examples/image_retraining/data/labels.txt deleted file mode 100644 index bc1131ac45..0000000000 --- a/tensorflow/examples/image_retraining/data/labels.txt +++ /dev/null @@ -1,3 +0,0 @@ -Runner-up -Winner -Loser diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py deleted file mode 100644 index ebddfb20f4..0000000000 --- a/tensorflow/examples/image_retraining/retrain.py +++ /dev/null @@ -1,1376 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -r"""Simple transfer learning with Inception v3 or Mobilenet models. - -With support for TensorBoard. - -This example shows how to take a Inception v3 or Mobilenet model trained on -ImageNet images, and train a new top layer that can recognize other classes of -images. - -The top layer receives as input a 2048-dimensional vector (1001-dimensional for -Mobilenet) for each image. We train a softmax layer on top of this -representation. Assuming the softmax layer contains N labels, this corresponds -to learning N + 2048*N (or 1001*N) model parameters corresponding to the -learned biases and weights. - -Here's an example, which assumes you have a folder containing class-named -subfolders, each full of images for each label. The example folder flower_photos -should have a structure like this: - -~/flower_photos/daisy/photo1.jpg -~/flower_photos/daisy/photo2.jpg -... -~/flower_photos/rose/anotherphoto77.jpg -... -~/flower_photos/sunflower/somepicture.jpg - -The subfolder names are important, since they define what label is applied to -each image, but the filenames themselves don't matter. Once your images are -prepared, you can run the training with a command like this: - - -```bash -bazel build tensorflow/examples/image_retraining:retrain && \ -bazel-bin/tensorflow/examples/image_retraining/retrain \ - --image_dir ~/flower_photos -``` - -Or, if you have a pip installation of tensorflow, `retrain.py` can be run -without bazel: - -```bash -python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos -``` - -You can replace the image_dir argument with any folder containing subfolders of -images. The label for each image is taken from the name of the subfolder it's -in. - -This produces a new model file that can be loaded and run by any TensorFlow -program, for example the label_image sample code. - -By default this script will use the high accuracy, but comparatively large and -slow Inception v3 model architecture. It's recommended that you start with this -to validate that you have gathered good training data, but if you want to deploy -on resource-limited platforms, you can try the `--architecture` flag with a -Mobilenet model. For example: - -Run floating-point version of mobilenet: -```bash -python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos --architecture mobilenet_1.0_224 -``` - -Run quantized version of mobilenet: -```bash -python tensorflow/examples/image_retraining/retrain.py \ - --image_dir ~/flower_photos/ --architecture mobilenet_1.0_224_quantized -``` - -There are 32 different Mobilenet models to choose from, with a variety of file -size and latency options. The first number can be '1.0', '0.75', '0.50', or -'0.25' to control the size, and the second controls the input image size, either -'224', '192', '160', or '128', with smaller sizes running faster. See -https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html -for more information on Mobilenet. - -To use with TensorBoard: - -By default, this script will log summaries to /tmp/retrain_logs directory - -Visualize the summaries with this command: - -tensorboard --logdir /tmp/retrain_logs - -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -from datetime import datetime -import hashlib -import os.path -import random -import re -import sys -import tarfile - -import numpy as np -from six.moves import urllib -import tensorflow as tf - -from tensorflow.contrib.quantize.python import quant_ops -from tensorflow.python.framework import graph_util -from tensorflow.python.framework import tensor_shape -from tensorflow.python.platform import gfile -from tensorflow.python.util import compat - -FLAGS = None - -# These are all parameters that are tied to the particular model architecture -# we're using for Inception v3. These include things like tensor names and their -# sizes. If you want to adapt this script to work with another model, you will -# need to update these to reflect the values in the network you're using. -MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M - - -def create_image_lists(image_dir, testing_percentage, validation_percentage): - """Builds a list of training images from the file system. - - Analyzes the sub folders in the image directory, splits them into stable - training, testing, and validation sets, and returns a data structure - describing the lists of images for each label and their paths. - - Args: - image_dir: String path to a folder containing subfolders of images. - testing_percentage: Integer percentage of the images to reserve for tests. - validation_percentage: Integer percentage of images reserved for validation. - - Returns: - A dictionary containing an entry for each label subfolder, with images split - into training, testing, and validation sets within each label. - """ - if not gfile.Exists(image_dir): - tf.logging.error("Image directory '" + image_dir + "' not found.") - return None - result = {} - sub_dirs = [x[0] for x in gfile.Walk(image_dir)] - # The root directory comes first, so skip it. - is_root_dir = True - for sub_dir in sub_dirs: - if is_root_dir: - is_root_dir = False - continue - extensions = ['jpg', 'jpeg', 'JPG', 'JPEG'] - file_list = [] - dir_name = os.path.basename(sub_dir) - if dir_name == image_dir: - continue - tf.logging.info("Looking for images in '" + dir_name + "'") - for extension in extensions: - file_glob = os.path.join(image_dir, dir_name, '*.' + extension) - file_list.extend(gfile.Glob(file_glob)) - if not file_list: - tf.logging.warning('No files found') - continue - if len(file_list) < 20: - tf.logging.warning( - 'WARNING: Folder has less than 20 images, which may cause issues.') - elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS: - tf.logging.warning( - 'WARNING: Folder {} has more than {} images. Some images will ' - 'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS)) - label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower()) - training_images = [] - testing_images = [] - validation_images = [] - for file_name in file_list: - base_name = os.path.basename(file_name) - # We want to ignore anything after '_nohash_' in the file name when - # deciding which set to put an image in, the data set creator has a way of - # grouping photos that are close variations of each other. For example - # this is used in the plant disease data set to group multiple pictures of - # the same leaf. - hash_name = re.sub(r'_nohash_.*$', '', file_name) - # This looks a bit magical, but we need to decide whether this file should - # go into the training, testing, or validation sets, and we want to keep - # existing files in the same set even if more files are subsequently - # added. - # To do that, we need a stable way of deciding based on just the file name - # itself, so we do a hash of that and then use that to generate a - # probability value that we use to assign it. - hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest() - percentage_hash = ((int(hash_name_hashed, 16) % - (MAX_NUM_IMAGES_PER_CLASS + 1)) * - (100.0 / MAX_NUM_IMAGES_PER_CLASS)) - if percentage_hash < validation_percentage: - validation_images.append(base_name) - elif percentage_hash < (testing_percentage + validation_percentage): - testing_images.append(base_name) - else: - training_images.append(base_name) - result[label_name] = { - 'dir': dir_name, - 'training': training_images, - 'testing': testing_images, - 'validation': validation_images, - } - return result - - -def get_image_path(image_lists, label_name, index, image_dir, category): - """"Returns a path to an image for a label at the given index. - - Args: - image_lists: Dictionary of training images for each label. - label_name: Label string we want to get an image for. - index: Int offset of the image we want. This will be moduloed by the - available number of images for the label, so it can be arbitrarily large. - image_dir: Root folder string of the subfolders containing the training - images. - category: Name string of set to pull images from - training, testing, or - validation. - - Returns: - File system path string to an image that meets the requested parameters. - - """ - if label_name not in image_lists: - tf.logging.fatal('Label does not exist %s.', label_name) - label_lists = image_lists[label_name] - if category not in label_lists: - tf.logging.fatal('Category does not exist %s.', category) - category_list = label_lists[category] - if not category_list: - tf.logging.fatal('Label %s has no images in the category %s.', - label_name, category) - mod_index = index % len(category_list) - base_name = category_list[mod_index] - sub_dir = label_lists['dir'] - full_path = os.path.join(image_dir, sub_dir, base_name) - return full_path - - -def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, - category, architecture): - """"Returns a path to a bottleneck file for a label at the given index. - - Args: - image_lists: Dictionary of training images for each label. - label_name: Label string we want to get an image for. - index: Integer offset of the image we want. This will be moduloed by the - available number of images for the label, so it can be arbitrarily large. - bottleneck_dir: Folder string holding cached files of bottleneck values. - category: Name string of set to pull images from - training, testing, or - validation. - architecture: The name of the model architecture. - - Returns: - File system path string to an image that meets the requested parameters. - """ - return get_image_path(image_lists, label_name, index, bottleneck_dir, - category) + '_' + architecture + '.txt' - - -def create_model_graph(model_info): - """"Creates a graph from saved GraphDef file and returns a Graph object. - - Args: - model_info: Dictionary containing information about the model architecture. - - Returns: - Graph holding the trained Inception network, and various tensors we'll be - manipulating. - """ - with tf.Graph().as_default() as graph: - model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name']) - print('Model path: ', model_path) - with gfile.FastGFile(model_path, 'rb') as f: - graph_def = tf.GraphDef() - graph_def.ParseFromString(f.read()) - bottleneck_tensor, resized_input_tensor = (tf.import_graph_def( - graph_def, - name='', - return_elements=[ - model_info['bottleneck_tensor_name'], - model_info['resized_input_tensor_name'], - ])) - return graph, bottleneck_tensor, resized_input_tensor - - -def run_bottleneck_on_image(sess, image_data, image_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor): - """Runs inference on an image to extract the 'bottleneck' summary layer. - - Args: - sess: Current active TensorFlow Session. - image_data: String of raw JPEG data. - image_data_tensor: Input data layer in the graph. - decoded_image_tensor: Output of initial image resizing and preprocessing. - resized_input_tensor: The input node of the recognition graph. - bottleneck_tensor: Layer before the final softmax. - - Returns: - Numpy array of bottleneck values. - """ - # First decode the JPEG image, resize it, and rescale the pixel values. - resized_input_values = sess.run(decoded_image_tensor, - {image_data_tensor: image_data}) - # Then run it through the recognition network. - bottleneck_values = sess.run(bottleneck_tensor, - {resized_input_tensor: resized_input_values}) - bottleneck_values = np.squeeze(bottleneck_values) - return bottleneck_values - - -def maybe_download_and_extract(data_url): - """Download and extract model tar file. - - If the pretrained model we're using doesn't already exist, this function - downloads it from the TensorFlow.org website and unpacks it into a directory. - - Args: - data_url: Web location of the tar file containing the pretrained model. - """ - dest_directory = FLAGS.model_dir - if not os.path.exists(dest_directory): - os.makedirs(dest_directory) - filename = data_url.split('/')[-1] - filepath = os.path.join(dest_directory, filename) - if not os.path.exists(filepath): - - def _progress(count, block_size, total_size): - sys.stdout.write('\r>> Downloading %s %.1f%%' % - (filename, - float(count * block_size) / float(total_size) * 100.0)) - sys.stdout.flush() - - filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress) - print() - statinfo = os.stat(filepath) - tf.logging.info('Successfully downloaded', filename, statinfo.st_size, - 'bytes.') - print('Extracting file from ', filepath) - tarfile.open(filepath, 'r:gz').extractall(dest_directory) - else: - print('Not extracting or downloading files, model already present in disk') - - -def ensure_dir_exists(dir_name): - """Makes sure the folder exists on disk. - - Args: - dir_name: Path string to the folder we want to create. - """ - if not os.path.exists(dir_name): - os.makedirs(dir_name) - - -bottleneck_path_2_bottleneck_values = {} - - -def create_bottleneck_file(bottleneck_path, image_lists, label_name, index, - image_dir, category, sess, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor): - """Create a single bottleneck file.""" - tf.logging.info('Creating bottleneck at ' + bottleneck_path) - image_path = get_image_path(image_lists, label_name, index, - image_dir, category) - if not gfile.Exists(image_path): - tf.logging.fatal('File does not exist %s', image_path) - image_data = gfile.FastGFile(image_path, 'rb').read() - try: - bottleneck_values = run_bottleneck_on_image( - sess, image_data, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor) - except Exception as e: - raise RuntimeError('Error during processing file %s (%s)' % (image_path, - str(e))) - bottleneck_string = ','.join(str(x) for x in bottleneck_values) - with open(bottleneck_path, 'w') as bottleneck_file: - bottleneck_file.write(bottleneck_string) - - -def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, - category, bottleneck_dir, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor, architecture): - """Retrieves or calculates bottleneck values for an image. - - If a cached version of the bottleneck data exists on-disk, return that, - otherwise calculate the data and save it to disk for future use. - - Args: - sess: The current active TensorFlow Session. - image_lists: Dictionary of training images for each label. - label_name: Label string we want to get an image for. - index: Integer offset of the image we want. This will be modulo-ed by the - available number of images for the label, so it can be arbitrarily large. - image_dir: Root folder string of the subfolders containing the training - images. - category: Name string of which set to pull images from - training, testing, - or validation. - bottleneck_dir: Folder string holding cached files of bottleneck values. - jpeg_data_tensor: The tensor to feed loaded jpeg data into. - decoded_image_tensor: The output of decoding and resizing the image. - resized_input_tensor: The input node of the recognition graph. - bottleneck_tensor: The output tensor for the bottleneck values. - architecture: The name of the model architecture. - - Returns: - Numpy array of values produced by the bottleneck layer for the image. - """ - label_lists = image_lists[label_name] - sub_dir = label_lists['dir'] - sub_dir_path = os.path.join(bottleneck_dir, sub_dir) - ensure_dir_exists(sub_dir_path) - bottleneck_path = get_bottleneck_path(image_lists, label_name, index, - bottleneck_dir, category, architecture) - if not os.path.exists(bottleneck_path): - create_bottleneck_file(bottleneck_path, image_lists, label_name, index, - image_dir, category, sess, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor) - with open(bottleneck_path, 'r') as bottleneck_file: - bottleneck_string = bottleneck_file.read() - did_hit_error = False - try: - bottleneck_values = [float(x) for x in bottleneck_string.split(',')] - except ValueError: - tf.logging.warning('Invalid float found, recreating bottleneck') - did_hit_error = True - if did_hit_error: - create_bottleneck_file(bottleneck_path, image_lists, label_name, index, - image_dir, category, sess, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor) - with open(bottleneck_path, 'r') as bottleneck_file: - bottleneck_string = bottleneck_file.read() - # Allow exceptions to propagate here, since they shouldn't happen after a - # fresh creation - bottleneck_values = [float(x) for x in bottleneck_string.split(',')] - return bottleneck_values - - -def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir, - jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, architecture): - """Ensures all the training, testing, and validation bottlenecks are cached. - - Because we're likely to read the same image multiple times (if there are no - distortions applied during training) it can speed things up a lot if we - calculate the bottleneck layer values once for each image during - preprocessing, and then just read those cached values repeatedly during - training. Here we go through all the images we've found, calculate those - values, and save them off. - - Args: - sess: The current active TensorFlow Session. - image_lists: Dictionary of training images for each label. - image_dir: Root folder string of the subfolders containing the training - images. - bottleneck_dir: Folder string holding cached files of bottleneck values. - jpeg_data_tensor: Input tensor for jpeg data from file. - decoded_image_tensor: The output of decoding and resizing the image. - resized_input_tensor: The input node of the recognition graph. - bottleneck_tensor: The penultimate output layer of the graph. - architecture: The name of the model architecture. - - Returns: - Nothing. - """ - how_many_bottlenecks = 0 - ensure_dir_exists(bottleneck_dir) - for label_name, label_lists in image_lists.items(): - for category in ['training', 'testing', 'validation']: - category_list = label_lists[category] - for index, unused_base_name in enumerate(category_list): - get_or_create_bottleneck( - sess, image_lists, label_name, index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, architecture) - - how_many_bottlenecks += 1 - if how_many_bottlenecks % 100 == 0: - tf.logging.info( - str(how_many_bottlenecks) + ' bottleneck files created.') - - -def get_random_cached_bottlenecks(sess, image_lists, how_many, category, - bottleneck_dir, image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_input_tensor, - bottleneck_tensor, architecture): - """Retrieves bottleneck values for cached images. - - If no distortions are being applied, this function can retrieve the cached - bottleneck values directly from disk for images. It picks a random set of - images from the specified category. - - Args: - sess: Current TensorFlow Session. - image_lists: Dictionary of training images for each label. - how_many: If positive, a random sample of this size will be chosen. - If negative, all bottlenecks will be retrieved. - category: Name string of which set to pull from - training, testing, or - validation. - bottleneck_dir: Folder string holding cached files of bottleneck values. - image_dir: Root folder string of the subfolders containing the training - images. - jpeg_data_tensor: The layer to feed jpeg image data into. - decoded_image_tensor: The output of decoding and resizing the image. - resized_input_tensor: The input node of the recognition graph. - bottleneck_tensor: The bottleneck output layer of the CNN graph. - architecture: The name of the model architecture. - - Returns: - List of bottleneck arrays, their corresponding ground truths, and the - relevant filenames. - """ - class_count = len(image_lists.keys()) - bottlenecks = [] - ground_truths = [] - filenames = [] - if how_many >= 0: - # Retrieve a random sample of bottlenecks. - for unused_i in range(how_many): - label_index = random.randrange(class_count) - label_name = list(image_lists.keys())[label_index] - image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1) - image_name = get_image_path(image_lists, label_name, image_index, - image_dir, category) - bottleneck = get_or_create_bottleneck( - sess, image_lists, label_name, image_index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, architecture) - ground_truth = np.zeros(class_count, dtype=np.float32) - ground_truth[label_index] = 1.0 - bottlenecks.append(bottleneck) - ground_truths.append(ground_truth) - filenames.append(image_name) - else: - # Retrieve all bottlenecks. - for label_index, label_name in enumerate(image_lists.keys()): - for image_index, image_name in enumerate( - image_lists[label_name][category]): - image_name = get_image_path(image_lists, label_name, image_index, - image_dir, category) - bottleneck = get_or_create_bottleneck( - sess, image_lists, label_name, image_index, image_dir, category, - bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, - resized_input_tensor, bottleneck_tensor, architecture) - ground_truth = np.zeros(class_count, dtype=np.float32) - ground_truth[label_index] = 1.0 - bottlenecks.append(bottleneck) - ground_truths.append(ground_truth) - filenames.append(image_name) - return bottlenecks, ground_truths, filenames - - -def get_random_distorted_bottlenecks( - sess, image_lists, how_many, category, image_dir, input_jpeg_tensor, - distorted_image, resized_input_tensor, bottleneck_tensor): - """Retrieves bottleneck values for training images, after distortions. - - If we're training with distortions like crops, scales, or flips, we have to - recalculate the full model for every image, and so we can't use cached - bottleneck values. Instead we find random images for the requested category, - run them through the distortion graph, and then the full graph to get the - bottleneck results for each. - - Args: - sess: Current TensorFlow Session. - image_lists: Dictionary of training images for each label. - how_many: The integer number of bottleneck values to return. - category: Name string of which set of images to fetch - training, testing, - or validation. - image_dir: Root folder string of the subfolders containing the training - images. - input_jpeg_tensor: The input layer we feed the image data to. - distorted_image: The output node of the distortion graph. - resized_input_tensor: The input node of the recognition graph. - bottleneck_tensor: The bottleneck output layer of the CNN graph. - - Returns: - List of bottleneck arrays and their corresponding ground truths. - """ - class_count = len(image_lists.keys()) - bottlenecks = [] - ground_truths = [] - for unused_i in range(how_many): - label_index = random.randrange(class_count) - label_name = list(image_lists.keys())[label_index] - image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1) - image_path = get_image_path(image_lists, label_name, image_index, image_dir, - category) - if not gfile.Exists(image_path): - tf.logging.fatal('File does not exist %s', image_path) - jpeg_data = gfile.FastGFile(image_path, 'rb').read() - # Note that we materialize the distorted_image_data as a numpy array before - # sending running inference on the image. This involves 2 memory copies and - # might be optimized in other implementations. - distorted_image_data = sess.run(distorted_image, - {input_jpeg_tensor: jpeg_data}) - bottleneck_values = sess.run(bottleneck_tensor, - {resized_input_tensor: distorted_image_data}) - bottleneck_values = np.squeeze(bottleneck_values) - ground_truth = np.zeros(class_count, dtype=np.float32) - ground_truth[label_index] = 1.0 - bottlenecks.append(bottleneck_values) - ground_truths.append(ground_truth) - return bottlenecks, ground_truths - - -def should_distort_images(flip_left_right, random_crop, random_scale, - random_brightness): - """Whether any distortions are enabled, from the input flags. - - Args: - flip_left_right: Boolean whether to randomly mirror images horizontally. - random_crop: Integer percentage setting the total margin used around the - crop box. - random_scale: Integer percentage of how much to vary the scale by. - random_brightness: Integer range to randomly multiply the pixel values by. - - Returns: - Boolean value indicating whether any distortions should be applied. - """ - return (flip_left_right or (random_crop != 0) or (random_scale != 0) or - (random_brightness != 0)) - - -def add_input_distortions(flip_left_right, random_crop, random_scale, - random_brightness, input_width, input_height, - input_depth, input_mean, input_std): - """Creates the operations to apply the specified distortions. - - During training it can help to improve the results if we run the images - through simple distortions like crops, scales, and flips. These reflect the - kind of variations we expect in the real world, and so can help train the - model to cope with natural data more effectively. Here we take the supplied - parameters and construct a network of operations to apply them to an image. - - Cropping - ~~~~~~~~ - - Cropping is done by placing a bounding box at a random position in the full - image. The cropping parameter controls the size of that box relative to the - input image. If it's zero, then the box is the same size as the input and no - cropping is performed. If the value is 50%, then the crop box will be half the - width and height of the input. In a diagram it looks like this: - - < width > - +---------------------+ - | | - | width - crop% | - | < > | - | +------+ | - | | | | - | | | | - | | | | - | +------+ | - | | - | | - +---------------------+ - - Scaling - ~~~~~~~ - - Scaling is a lot like cropping, except that the bounding box is always - centered and its size varies randomly within the given range. For example if - the scale percentage is zero, then the bounding box is the same size as the - input and no scaling is applied. If it's 50%, then the bounding box will be in - a random range between half the width and height and full size. - - Args: - flip_left_right: Boolean whether to randomly mirror images horizontally. - random_crop: Integer percentage setting the total margin used around the - crop box. - random_scale: Integer percentage of how much to vary the scale by. - random_brightness: Integer range to randomly multiply the pixel values by. - graph. - input_width: Horizontal size of expected input image to model. - input_height: Vertical size of expected input image to model. - input_depth: How many channels the expected input image should have. - input_mean: Pixel value that should be zero in the image for the graph. - input_std: How much to divide the pixel values by before recognition. - - Returns: - The jpeg input layer and the distorted result tensor. - """ - - jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput') - decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth) - decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32) - decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0) - margin_scale = 1.0 + (random_crop / 100.0) - resize_scale = 1.0 + (random_scale / 100.0) - margin_scale_value = tf.constant(margin_scale) - resize_scale_value = tf.random_uniform(tensor_shape.scalar(), - minval=1.0, - maxval=resize_scale) - scale_value = tf.multiply(margin_scale_value, resize_scale_value) - precrop_width = tf.multiply(scale_value, input_width) - precrop_height = tf.multiply(scale_value, input_height) - precrop_shape = tf.stack([precrop_height, precrop_width]) - precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32) - precropped_image = tf.image.resize_bilinear(decoded_image_4d, - precrop_shape_as_int) - precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0]) - cropped_image = tf.random_crop(precropped_image_3d, - [input_height, input_width, input_depth]) - if flip_left_right: - flipped_image = tf.image.random_flip_left_right(cropped_image) - else: - flipped_image = cropped_image - brightness_min = 1.0 - (random_brightness / 100.0) - brightness_max = 1.0 + (random_brightness / 100.0) - brightness_value = tf.random_uniform(tensor_shape.scalar(), - minval=brightness_min, - maxval=brightness_max) - brightened_image = tf.multiply(flipped_image, brightness_value) - offset_image = tf.subtract(brightened_image, input_mean) - mul_image = tf.multiply(offset_image, 1.0 / input_std) - distort_result = tf.expand_dims(mul_image, 0, name='DistortResult') - return jpeg_data, distort_result - - -def variable_summaries(var): - """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" - with tf.name_scope('summaries'): - mean = tf.reduce_mean(var) - tf.summary.scalar('mean', mean) - with tf.name_scope('stddev'): - stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) - tf.summary.scalar('stddev', stddev) - tf.summary.scalar('max', tf.reduce_max(var)) - tf.summary.scalar('min', tf.reduce_min(var)) - tf.summary.histogram('histogram', var) - - -def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, - bottleneck_tensor_size, quantize_layer): - """Adds a new softmax and fully-connected layer for training. - - We need to retrain the top layer to identify our new classes, so this function - adds the right operations to the graph, along with some variables to hold the - weights, and then sets up all the gradients for the backward pass. - - The set up for the softmax and fully-connected layers is based on: - https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html - - Args: - class_count: Integer of how many categories of things we're trying to - recognize. - final_tensor_name: Name string for the new final node that produces results. - bottleneck_tensor: The output of the main CNN graph. - bottleneck_tensor_size: How many entries in the bottleneck vector. - quantize_layer: Boolean, specifying whether the newly added layer should be - quantized. - - Returns: - The tensors for the training and cross entropy results, and tensors for the - bottleneck input and ground truth input. - """ - with tf.name_scope('input'): - bottleneck_input = tf.placeholder_with_default( - bottleneck_tensor, - shape=[None, bottleneck_tensor_size], - name='BottleneckInputPlaceholder') - - ground_truth_input = tf.placeholder(tf.float32, - [None, class_count], - name='GroundTruthInput') - - # Organizing the following ops as `final_training_ops` so they're easier - # to see in TensorBoard - layer_name = 'final_training_ops' - with tf.name_scope(layer_name): - with tf.name_scope('weights'): - initial_value = tf.truncated_normal( - [bottleneck_tensor_size, class_count], stddev=0.001) - layer_weights = tf.Variable(initial_value, name='final_weights') - if quantize_layer: - quantized_layer_weights = quant_ops.MovingAvgQuantize( - layer_weights, is_training=True) - variable_summaries(quantized_layer_weights) - - variable_summaries(layer_weights) - with tf.name_scope('biases'): - layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') - if quantize_layer: - quantized_layer_biases = quant_ops.MovingAvgQuantize( - layer_biases, is_training=True) - variable_summaries(quantized_layer_biases) - - variable_summaries(layer_biases) - - with tf.name_scope('Wx_plus_b'): - if quantize_layer: - logits = tf.matmul(bottleneck_input, - quantized_layer_weights) + quantized_layer_biases - logits = quant_ops.MovingAvgQuantize( - logits, - init_min=-32.0, - init_max=32.0, - is_training=True, - num_bits=8, - narrow_range=False, - ema_decay=0.5) - tf.summary.histogram('pre_activations', logits) - else: - logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases - tf.summary.histogram('pre_activations', logits) - - final_tensor = tf.nn.softmax(logits, name=final_tensor_name) - - tf.summary.histogram('activations', final_tensor) - - with tf.name_scope('cross_entropy'): - cross_entropy = tf.nn.softmax_cross_entropy_with_logits( - labels=ground_truth_input, logits=logits) - with tf.name_scope('total'): - cross_entropy_mean = tf.reduce_mean(cross_entropy) - - tf.summary.scalar('cross_entropy', cross_entropy_mean) - - with tf.name_scope('train'): - optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate) - train_step = optimizer.minimize(cross_entropy_mean) - - return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input, - final_tensor) - - -def add_evaluation_step(result_tensor, ground_truth_tensor): - """Inserts the operations we need to evaluate the accuracy of our results. - - Args: - result_tensor: The new final node that produces results. - ground_truth_tensor: The node we feed ground truth data - into. - - Returns: - Tuple of (evaluation step, prediction). - """ - with tf.name_scope('accuracy'): - with tf.name_scope('correct_prediction'): - prediction = tf.argmax(result_tensor, 1) - correct_prediction = tf.equal( - prediction, tf.argmax(ground_truth_tensor, 1)) - with tf.name_scope('accuracy'): - evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - tf.summary.scalar('accuracy', evaluation_step) - return evaluation_step, prediction - - -def save_graph_to_file(sess, graph, graph_file_name): - output_graph_def = graph_util.convert_variables_to_constants( - sess, graph.as_graph_def(), [FLAGS.final_tensor_name]) - - with gfile.FastGFile(graph_file_name, 'wb') as f: - f.write(output_graph_def.SerializeToString()) - return - - -def prepare_file_system(): - # Setup the directory we'll write summaries to for TensorBoard - if tf.gfile.Exists(FLAGS.summaries_dir): - tf.gfile.DeleteRecursively(FLAGS.summaries_dir) - tf.gfile.MakeDirs(FLAGS.summaries_dir) - if FLAGS.intermediate_store_frequency > 0: - ensure_dir_exists(FLAGS.intermediate_output_graphs_dir) - return - - -def create_model_info(architecture): - """Given the name of a model architecture, returns information about it. - - There are different base image recognition pretrained models that can be - retrained using transfer learning, and this function translates from the name - of a model to the attributes that are needed to download and train with it. - - Args: - architecture: Name of a model architecture. - - Returns: - Dictionary of information about the model, or None if the name isn't - recognized - - Raises: - ValueError: If architecture name is unknown. - """ - architecture = architecture.lower() - is_quantized = False - if architecture == 'inception_v3': - # pylint: disable=line-too-long - data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' - # pylint: enable=line-too-long - bottleneck_tensor_name = 'pool_3/_reshape:0' - bottleneck_tensor_size = 2048 - input_width = 299 - input_height = 299 - input_depth = 3 - resized_input_tensor_name = 'Mul:0' - model_file_name = 'classify_image_graph_def.pb' - input_mean = 128 - input_std = 128 - elif architecture.startswith('mobilenet_'): - parts = architecture.split('_') - if len(parts) != 3 and len(parts) != 4: - tf.logging.error("Couldn't understand architecture name '%s'", - architecture) - return None - version_string = parts[1] - if (version_string != '1.0' and version_string != '0.75' and - version_string != '0.50' and version_string != '0.25'): - tf.logging.error( - """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', - but found '%s' for architecture '%s'""", - version_string, architecture) - return None - size_string = parts[2] - if (size_string != '224' and size_string != '192' and - size_string != '160' and size_string != '128'): - tf.logging.error( - """The Mobilenet input size should be '224', '192', '160', or '128', - but found '%s' for architecture '%s'""", - size_string, architecture) - return None - if len(parts) == 3: - is_quantized = False - else: - if parts[3] != 'quantized': - tf.logging.error( - "Couldn't understand architecture suffix '%s' for '%s'", parts[3], - architecture) - return None - is_quantized = True - - if is_quantized: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_quantized_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'Placeholder:0' - model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string + - '_quantized_frozen') - model_base_name = 'quantized_frozen_graph.pb' - - else: - data_url = 'http://download.tensorflow.org/models/mobilenet_v1_' - data_url += version_string + '_' + size_string + '_frozen.tgz' - bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0' - resized_input_tensor_name = 'input:0' - model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string - model_base_name = 'frozen_graph.pb' - - bottleneck_tensor_size = 1001 - input_width = int(size_string) - input_height = int(size_string) - input_depth = 3 - model_file_name = os.path.join(model_dir_name, model_base_name) - input_mean = 127.5 - input_std = 127.5 - else: - tf.logging.error("Couldn't understand architecture name '%s'", architecture) - raise ValueError('Unknown architecture', architecture) - - return { - 'data_url': data_url, - 'bottleneck_tensor_name': bottleneck_tensor_name, - 'bottleneck_tensor_size': bottleneck_tensor_size, - 'input_width': input_width, - 'input_height': input_height, - 'input_depth': input_depth, - 'resized_input_tensor_name': resized_input_tensor_name, - 'model_file_name': model_file_name, - 'input_mean': input_mean, - 'input_std': input_std, - 'quantize_layer': is_quantized, - } - - -def add_jpeg_decoding(input_width, input_height, input_depth, input_mean, - input_std): - """Adds operations that perform JPEG decoding and resizing to the graph.. - - Args: - input_width: Desired width of the image fed into the recognizer graph. - input_height: Desired width of the image fed into the recognizer graph. - input_depth: Desired channels of the image fed into the recognizer graph. - input_mean: Pixel value that should be zero in the image for the graph. - input_std: How much to divide the pixel values by before recognition. - - Returns: - Tensors for the node to feed JPEG data into, and the output of the - preprocessing steps. - """ - jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput') - decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth) - decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32) - decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0) - resize_shape = tf.stack([input_height, input_width]) - resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32) - resized_image = tf.image.resize_bilinear(decoded_image_4d, - resize_shape_as_int) - offset_image = tf.subtract(resized_image, input_mean) - mul_image = tf.multiply(offset_image, 1.0 / input_std) - return jpeg_data, mul_image - - -def main(_): - # Needed to make sure the logging output is visible. - # See https://github.com/tensorflow/tensorflow/issues/3047 - tf.logging.set_verbosity(tf.logging.INFO) - - # Prepare necessary directories that can be used during training - prepare_file_system() - - # Gather information about the model architecture we'll be using. - model_info = create_model_info(FLAGS.architecture) - if not model_info: - tf.logging.error('Did not recognize architecture flag') - return -1 - - # Set up the pre-trained graph. - maybe_download_and_extract(model_info['data_url']) - graph, bottleneck_tensor, resized_image_tensor = ( - create_model_graph(model_info)) - - # Look at the folder structure, and create lists of all the images. - image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage, - FLAGS.validation_percentage) - class_count = len(image_lists.keys()) - if class_count == 0: - tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir) - return -1 - if class_count == 1: - tf.logging.error('Only one valid folder of images found at ' + - FLAGS.image_dir + - ' - multiple classes are needed for classification.') - return -1 - - # See if the command-line flags mean we're applying any distortions. - do_distort_images = should_distort_images( - FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, - FLAGS.random_brightness) - - with tf.Session(graph=graph) as sess: - # Set up the image decoding sub-graph. - jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding( - model_info['input_width'], model_info['input_height'], - model_info['input_depth'], model_info['input_mean'], - model_info['input_std']) - - if do_distort_images: - # We will be applying distortions, so setup the operations we'll need. - (distorted_jpeg_data_tensor, - distorted_image_tensor) = add_input_distortions( - FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale, - FLAGS.random_brightness, model_info['input_width'], - model_info['input_height'], model_info['input_depth'], - model_info['input_mean'], model_info['input_std']) - else: - # We'll make sure we've calculated the 'bottleneck' image summaries and - # cached them on disk. - cache_bottlenecks(sess, image_lists, FLAGS.image_dir, - FLAGS.bottleneck_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, - bottleneck_tensor, FLAGS.architecture) - - # Add the new layer that we'll be training. - (train_step, cross_entropy, bottleneck_input, ground_truth_input, - final_tensor) = add_final_training_ops( - len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor, - model_info['bottleneck_tensor_size'], model_info['quantize_layer']) - - # Create the operations we need to evaluate the accuracy of our new layer. - evaluation_step, prediction = add_evaluation_step( - final_tensor, ground_truth_input) - - # Merge all the summaries and write them out to the summaries_dir - merged = tf.summary.merge_all() - train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', - sess.graph) - - validation_writer = tf.summary.FileWriter( - FLAGS.summaries_dir + '/validation') - - # Set up all our weights to their initial default values. - init = tf.global_variables_initializer() - sess.run(init) - - # Run the training for as many cycles as requested on the command line. - for i in range(FLAGS.how_many_training_steps): - # Get a batch of input bottleneck values, either calculated fresh every - # time with distortions applied, or from the cache stored on disk. - if do_distort_images: - (train_bottlenecks, - train_ground_truth) = get_random_distorted_bottlenecks( - sess, image_lists, FLAGS.train_batch_size, 'training', - FLAGS.image_dir, distorted_jpeg_data_tensor, - distorted_image_tensor, resized_image_tensor, bottleneck_tensor) - else: - (train_bottlenecks, - train_ground_truth, _) = get_random_cached_bottlenecks( - sess, image_lists, FLAGS.train_batch_size, 'training', - FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - FLAGS.architecture) - # Feed the bottlenecks and ground truth into the graph, and run a training - # step. Capture training summaries for TensorBoard with the `merged` op. - train_summary, _ = sess.run( - [merged, train_step], - feed_dict={bottleneck_input: train_bottlenecks, - ground_truth_input: train_ground_truth}) - train_writer.add_summary(train_summary, i) - - # Every so often, print out how well the graph is training. - is_last_step = (i + 1 == FLAGS.how_many_training_steps) - if (i % FLAGS.eval_step_interval) == 0 or is_last_step: - train_accuracy, cross_entropy_value = sess.run( - [evaluation_step, cross_entropy], - feed_dict={bottleneck_input: train_bottlenecks, - ground_truth_input: train_ground_truth}) - tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' % - (datetime.now(), i, train_accuracy * 100)) - tf.logging.info('%s: Step %d: Cross entropy = %f' % - (datetime.now(), i, cross_entropy_value)) - validation_bottlenecks, validation_ground_truth, _ = ( - get_random_cached_bottlenecks( - sess, image_lists, FLAGS.validation_batch_size, 'validation', - FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - FLAGS.architecture)) - # Run a validation step and capture training summaries for TensorBoard - # with the `merged` op. - validation_summary, validation_accuracy = sess.run( - [merged, evaluation_step], - feed_dict={bottleneck_input: validation_bottlenecks, - ground_truth_input: validation_ground_truth}) - validation_writer.add_summary(validation_summary, i) - tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' % - (datetime.now(), i, validation_accuracy * 100, - len(validation_bottlenecks))) - - # Store intermediate results - intermediate_frequency = FLAGS.intermediate_store_frequency - - if (intermediate_frequency > 0 and (i % intermediate_frequency == 0) - and i > 0): - intermediate_file_name = (FLAGS.intermediate_output_graphs_dir + - 'intermediate_' + str(i) + '.pb') - tf.logging.info('Save intermediate result to : ' + - intermediate_file_name) - save_graph_to_file(sess, graph, intermediate_file_name) - - # We've completed all our training, so run a final test evaluation on - # some new images we haven't used before. - test_bottlenecks, test_ground_truth, test_filenames = ( - get_random_cached_bottlenecks( - sess, image_lists, FLAGS.test_batch_size, 'testing', - FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor, - decoded_image_tensor, resized_image_tensor, bottleneck_tensor, - FLAGS.architecture)) - test_accuracy, predictions = sess.run( - [evaluation_step, prediction], - feed_dict={bottleneck_input: test_bottlenecks, - ground_truth_input: test_ground_truth}) - tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % - (test_accuracy * 100, len(test_bottlenecks))) - - if FLAGS.print_misclassified_test_images: - tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===') - for i, test_filename in enumerate(test_filenames): - if predictions[i] != test_ground_truth[i].argmax(): - tf.logging.info('%70s %s' % - (test_filename, - list(image_lists.keys())[predictions[i]])) - - # Write out the trained graph and labels with the weights stored as - # constants. - save_graph_to_file(sess, graph, FLAGS.output_graph) - with gfile.FastGFile(FLAGS.output_labels, 'w') as f: - f.write('\n'.join(image_lists.keys()) + '\n') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument( - '--image_dir', - type=str, - default='', - help='Path to folders of labeled images.' - ) - parser.add_argument( - '--output_graph', - type=str, - default='/tmp/output_graph.pb', - help='Where to save the trained graph.' - ) - parser.add_argument( - '--intermediate_output_graphs_dir', - type=str, - default='/tmp/intermediate_graph/', - help='Where to save the intermediate graphs.' - ) - parser.add_argument( - '--intermediate_store_frequency', - type=int, - default=0, - help="""\ - How many steps to store intermediate graph. If "0" then will not - store.\ - """ - ) - parser.add_argument( - '--output_labels', - type=str, - default='/tmp/output_labels.txt', - help='Where to save the trained graph\'s labels.' - ) - parser.add_argument( - '--summaries_dir', - type=str, - default='/tmp/retrain_logs', - help='Where to save summary logs for TensorBoard.' - ) - parser.add_argument( - '--how_many_training_steps', - type=int, - default=4000, - help='How many training steps to run before ending.' - ) - parser.add_argument( - '--learning_rate', - type=float, - default=0.01, - help='How large a learning rate to use when training.' - ) - parser.add_argument( - '--testing_percentage', - type=int, - default=10, - help='What percentage of images to use as a test set.' - ) - parser.add_argument( - '--validation_percentage', - type=int, - default=10, - help='What percentage of images to use as a validation set.' - ) - parser.add_argument( - '--eval_step_interval', - type=int, - default=10, - help='How often to evaluate the training results.' - ) - parser.add_argument( - '--train_batch_size', - type=int, - default=100, - help='How many images to train on at a time.' - ) - parser.add_argument( - '--test_batch_size', - type=int, - default=-1, - help="""\ - How many images to test on. This test set is only used once, to evaluate - the final accuracy of the model after training completes. - A value of -1 causes the entire test set to be used, which leads to more - stable results across runs.\ - """ - ) - parser.add_argument( - '--validation_batch_size', - type=int, - default=100, - help="""\ - How many images to use in an evaluation batch. This validation set is - used much more often than the test set, and is an early indicator of how - accurate the model is during training. - A value of -1 causes the entire validation set to be used, which leads to - more stable results across training iterations, but may be slower on large - training sets.\ - """ - ) - parser.add_argument( - '--print_misclassified_test_images', - default=False, - help="""\ - Whether to print out a list of all misclassified test images.\ - """, - action='store_true' - ) - parser.add_argument( - '--model_dir', - type=str, - default='/tmp/imagenet', - help="""\ - Path to classify_image_graph_def.pb, - imagenet_synset_to_human_label_map.txt, and - imagenet_2012_challenge_label_map_proto.pbtxt.\ - """ - ) - parser.add_argument( - '--bottleneck_dir', - type=str, - default='/tmp/bottleneck', - help='Path to cache bottleneck layer values as files.' - ) - parser.add_argument( - '--final_tensor_name', - type=str, - default='final_result', - help="""\ - The name of the output classification layer in the retrained graph.\ - """ - ) - parser.add_argument( - '--flip_left_right', - default=False, - help="""\ - Whether to randomly flip half of the training images horizontally.\ - """, - action='store_true' - ) - parser.add_argument( - '--random_crop', - type=int, - default=0, - help="""\ - A percentage determining how much of a margin to randomly crop off the - training images.\ - """ - ) - parser.add_argument( - '--random_scale', - type=int, - default=0, - help="""\ - A percentage determining how much to randomly scale up the size of the - training images by.\ - """ - ) - parser.add_argument( - '--random_brightness', - type=int, - default=0, - help="""\ - A percentage determining how much to randomly multiply the training image - input pixels up or down by.\ - """ - ) - parser.add_argument( - '--architecture', - type=str, - default='inception_v3', - help="""\ - Which model architecture to use. 'inception_v3' is the most accurate, but - also the slowest. For faster or smaller models, chose a MobileNet with the - form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example, - 'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224 - pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much - less accurate, but smaller and faster network that's 920 KB on disk and - takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html - for more information on Mobilenet.\ - """) - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py deleted file mode 100644 index 2de4c4ec99..0000000000 --- a/tensorflow/examples/image_retraining/retrain_test.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -# pylint: disable=g-bad-import-order,unused-import -"""Tests the graph freezing tool.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf -import os - -from tensorflow.examples.image_retraining import retrain -from tensorflow.python.framework import test_util - - -class ImageRetrainingTest(test_util.TensorFlowTestCase): - - def dummyImageLists(self): - return {'label_one': {'dir': 'somedir', 'training': ['image_one.jpg', - 'image_two.jpg'], - 'testing': ['image_three.jpg', 'image_four.jpg'], - 'validation': ['image_five.jpg', 'image_six.jpg']}, - 'label_two': {'dir': 'otherdir', 'training': ['image_one.jpg', - 'image_two.jpg'], - 'testing': ['image_three.jpg', 'image_four.jpg'], - 'validation': ['image_five.jpg', 'image_six.jpg']}} - - def testGetImagePath(self): - image_lists = self.dummyImageLists() - self.assertEqual('image_dir/somedir/image_one.jpg', retrain.get_image_path( - image_lists, 'label_one', 0, 'image_dir', 'training')) - self.assertEqual('image_dir/otherdir/image_four.jpg', - retrain.get_image_path(image_lists, 'label_two', 1, - 'image_dir', 'testing')) - - def testGetBottleneckPath(self): - image_lists = self.dummyImageLists() - self.assertEqual('bottleneck_dir/somedir/image_five.jpg_imagenet_v3.txt', - retrain.get_bottleneck_path( - image_lists, 'label_one', 0, 'bottleneck_dir', - 'validation', 'imagenet_v3')) - - def testShouldDistortImage(self): - self.assertEqual(False, retrain.should_distort_images(False, 0, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(True, 0, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 10, 0, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 0, 1, 0)) - self.assertEqual(True, retrain.should_distort_images(False, 0, 0, 50)) - - def testAddInputDistortions(self): - with tf.Graph().as_default(): - with tf.Session() as sess: - retrain.add_input_distortions(True, 10, 10, 10, 299, 299, 3, 128, 128) - self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortJPGInput:0')) - self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0')) - - @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOps(self, flags_mock): - with tf.Graph().as_default(): - with tf.Session() as sess: - bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, False) - self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) - - @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01) - def testAddFinalTrainingOpsQuantized(self, flags_mock): - with tf.Graph().as_default(): - with tf.Session() as sess: - bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck') - # Test creating final training op with quantization - retrain.add_final_training_ops(5, 'final', bottleneck, 1024, True) - self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0')) - - def testAddEvaluationStep(self): - with tf.Graph().as_default(): - final = tf.placeholder(tf.float32, [1], name='final') - gt = tf.placeholder(tf.float32, [1], name='gt') - self.assertIsNotNone(retrain.add_evaluation_step(final, gt)) - - def testAddJpegDecoding(self): - with tf.Graph().as_default(): - jpeg_data, mul_image = retrain.add_jpeg_decoding(10, 10, 3, 0, 255) - self.assertIsNotNone(jpeg_data) - self.assertIsNotNone(mul_image) - - def testCreateModelInfo(self): - did_raise_value_error = False - try: - retrain.create_model_info('no_such_model_name') - except ValueError: - did_raise_value_error = True - self.assertTrue(did_raise_value_error) - model_info = retrain.create_model_info('inception_v3') - self.assertIsNotNone(model_info) - self.assertEqual(299, model_info['input_width']) - - def testCreateModelInfoQuantized(self): - # Test for mobilenet_quantized - model_info = retrain.create_model_info('mobilenet_1.0_224') - self.assertIsNotNone(model_info) - self.assertEqual(224, model_info['input_width']) - - -if __name__ == '__main__': - tf.test.main() diff --git a/tensorflow/examples/ios/.gitignore b/tensorflow/examples/ios/.gitignore index e572b3012c..dbabfb33bf 100644 --- a/tensorflow/examples/ios/.gitignore +++ b/tensorflow/examples/ios/.gitignore @@ -2,3 +2,6 @@ project.xcworkspace xcuserdata imagenet_comp_graph_label_strings.txt tensorflow_inception_graph.pb +simple/data/LICENSE +camera/data/LICENSE +benchmark/data/LICENSE diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md index 7d2eb870be..5d7bd36837 100644 --- a/tensorflow/examples/ios/README.md +++ b/tensorflow/examples/ios/README.md @@ -6,7 +6,7 @@ This folder contains examples of how to build applications for iOS devices using - You'll need Xcode 7.3 or later. - There are currently three examples: simple, benchmark, and camera. For now, - you can download the sample code by cloning the main tensorflow repository + you can download the sample code by cloning the main tensorflow repository (we are planning to make the samples available as a separate repository later). @@ -48,8 +48,8 @@ open tf_simple_example.xcworkspace # obs, not the .xcodeproj directory ### Troubleshooting - Make sure you use the TensorFlow-experimental pod (and not TensorFlow). - - - The TensorFlow-experimental pod is current about ~450MB. The reason it is + + - The TensorFlow-experimental pod is current about ~450MB. The reason it is so big is because we are bundling multiple platforms, and the pod includes all TensorFlow functionality (e.g. operations). The final app size after build is substantially smaller though (~25MB). Working with the complete @@ -91,7 +91,7 @@ target 'YourProjectName' open up the Xcode project in the `camera` subfolder. Once you build and run that, you should get a live camera view that you can point at objects to get real-time recognition results. - + ### Troubleshooting If you're hitting problems, here's a checklist of common things to investigate: @@ -119,11 +119,13 @@ rundown: `tensorflow/contrib/makefile/gen/lib` to the Library Search Paths setting. - You'll also need to add `libprotobuf.a` and `libprotobuf-lite.a` from - `tensorflow/contrib/makefile/gen/protobuf_ios/lib` to your _Build Stages_ and - _Library Search Paths_. + `tensorflow/contrib/makefile/gen/protobuf_ios/lib` + and `nsync.a` from `tensorflow/contrib/makefile/downloads/nsync/builds/lipo.ios.c++11` + to your _Build Stages_ and _Library Search Paths_. - The _Header Search_ paths needs to contain: - the root folder of tensorflow, + - `tensorflow/contrib/makefile/downloads/nsync/public` - `tensorflow/contrib/makefile/downloads/protobuf/src` - `tensorflow/contrib/makefile/downloads`, - `tensorflow/contrib/makefile/downloads/eigen`, and diff --git a/tensorflow/examples/label_image/BUILD b/tensorflow/examples/label_image/BUILD index 9207fc6332..c50fd93d03 100644 --- a/tensorflow/examples/label_image/BUILD +++ b/tensorflow/examples/label_image/BUILD @@ -9,6 +9,8 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) +exports_files(["data/grace_hopper.jpg"]) + load("//tensorflow:tensorflow.bzl", "tf_cc_binary") tf_cc_binary( @@ -51,16 +53,12 @@ tf_cc_binary( }), ) -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], +py_binary( + name = "label_image_py", + srcs = ["label_image.py"], + main = "label_image.py", + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], ) diff --git a/tensorflow/examples/label_image/README.md b/tensorflow/examples/label_image/README.md index a9e44745e5..cfd0132a7a 100644 --- a/tensorflow/examples/label_image/README.md +++ b/tensorflow/examples/label_image/README.md @@ -73,10 +73,23 @@ Python than the Python code mentioned in the [Inception tutorial](https://www.tensorflow.org/tutorials/image_recognition/). and could be easier to add visualization or debug code. -With tensorflow python package installed, you can run it like: + +`bazel-bin/tensorflow/examples/label_image/label_image_py` should be there after +```bash +$ bazel build tensorflow/examples/label_image/... +``` + +Run + +```bash +$ bazel-bin/tensorflow/examples/label_image/label_image_py +``` + +Or, with tensorflow python package installed, you can run it like: ```bash $ python3 tensorflow/examples/label_image/label_image.py ``` + And get result similar to this: ``` military uniform 0.834305 diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py index 39d0981337..fe5e0fc684 100644 --- a/tensorflow/examples/label_image/label_image.py +++ b/tensorflow/examples/label_image/label_image.py @@ -18,11 +18,11 @@ from __future__ import division from __future__ import print_function import argparse -import sys import numpy as np import tensorflow as tf + def load_graph(model_file): graph = tf.Graph() graph_def = tf.GraphDef() @@ -34,24 +34,28 @@ def load_graph(model_file): return graph -def read_tensor_from_image_file(file_name, input_height=299, input_width=299, - input_mean=0, input_std=255): + +def read_tensor_from_image_file(file_name, + input_height=299, + input_width=299, + input_mean=0, + input_std=255): input_name = "file_reader" output_name = "normalized" file_reader = tf.read_file(file_name, input_name) if file_name.endswith(".png"): - image_reader = tf.image.decode_png(file_reader, channels = 3, - name='png_reader') + image_reader = tf.image.decode_png( + file_reader, channels=3, name="png_reader") elif file_name.endswith(".gif"): - image_reader = tf.squeeze(tf.image.decode_gif(file_reader, - name='gif_reader')) + image_reader = tf.squeeze( + tf.image.decode_gif(file_reader, name="gif_reader")) elif file_name.endswith(".bmp"): - image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader') + image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader") else: - image_reader = tf.image.decode_jpeg(file_reader, channels = 3, - name='jpeg_reader') + image_reader = tf.image.decode_jpeg( + file_reader, channels=3, name="jpeg_reader") float_caster = tf.cast(image_reader, tf.float32) - dims_expander = tf.expand_dims(float_caster, 0); + dims_expander = tf.expand_dims(float_caster, 0) resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width]) normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std]) sess = tf.Session() @@ -59,6 +63,7 @@ def read_tensor_from_image_file(file_name, input_height=299, input_width=299, return result + def load_labels(label_file): label = [] proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines() @@ -66,6 +71,7 @@ def load_labels(label_file): label.append(l.rstrip()) return label + if __name__ == "__main__": file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg" model_file = \ @@ -110,20 +116,22 @@ if __name__ == "__main__": output_layer = args.output_layer graph = load_graph(model_file) - t = read_tensor_from_image_file(file_name, - input_height=input_height, - input_width=input_width, - input_mean=input_mean, - input_std=input_std) + t = read_tensor_from_image_file( + file_name, + input_height=input_height, + input_width=input_width, + input_mean=input_mean, + input_std=input_std) input_name = "import/" + input_layer output_name = "import/" + output_layer - input_operation = graph.get_operation_by_name(input_name); - output_operation = graph.get_operation_by_name(output_name); + input_operation = graph.get_operation_by_name(input_name) + output_operation = graph.get_operation_by_name(output_name) with tf.Session(graph=graph) as sess: - results = sess.run(output_operation.outputs[0], - {input_operation.outputs[0]: t}) + results = sess.run(output_operation.outputs[0], { + input_operation.outputs[0]: t + }) results = np.squeeze(results) top_k = results.argsort()[-5:][::-1] diff --git a/tensorflow/examples/label_image/main.cc b/tensorflow/examples/label_image/main.cc index 63bc39de6c..baa65d3243 100644 --- a/tensorflow/examples/label_image/main.cc +++ b/tensorflow/examples/label_image/main.cc @@ -49,6 +49,7 @@ limitations under the License. #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/init_main.h" @@ -137,15 +138,15 @@ Status ReadTensorFromImageFile(const string& file_name, const int input_height, // Now try to figure out what kind of file it is and decode it. const int wanted_channels = 3; tensorflow::Output image_reader; - if (tensorflow::StringPiece(file_name).ends_with(".png")) { + if (tensorflow::str_util::EndsWith(file_name, ".png")) { image_reader = DecodePng(root.WithOpName("png_reader"), file_reader, DecodePng::Channels(wanted_channels)); - } else if (tensorflow::StringPiece(file_name).ends_with(".gif")) { + } else if (tensorflow::str_util::EndsWith(file_name, ".gif")) { // gif decoder returns 4-D tensor, remove the first dim image_reader = Squeeze(root.WithOpName("squeeze_first_dim"), DecodeGif(root.WithOpName("gif_reader"), file_reader)); - } else if (tensorflow::StringPiece(file_name).ends_with(".bmp")) { + } else if (tensorflow::str_util::EndsWith(file_name, ".bmp")) { image_reader = DecodeBmp(root.WithOpName("bmp_reader"), file_reader); } else { // Assume if it's neither a PNG nor a GIF then it must be a JPEG. diff --git a/tensorflow/examples/learn/BUILD b/tensorflow/examples/learn/BUILD index aba7f600b5..bdbcb0b163 100644 --- a/tensorflow/examples/learn/BUILD +++ b/tensorflow/examples/learn/BUILD @@ -152,15 +152,3 @@ sh_test( "notap", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py index 03e60972aa..86f5204ec3 100644 --- a/tensorflow/examples/learn/iris.py +++ b/tensorflow/examples/learn/iris.py @@ -21,7 +21,8 @@ from __future__ import division from __future__ import print_function import os -import urllib + +from six.moves.urllib.request import urlretrieve import tensorflow as tf @@ -38,9 +39,7 @@ FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] def maybe_download_iris_data(file_name, download_url): """Downloads the file and returns the number of data.""" if not os.path.exists(file_name): - raw = urllib.urlopen(download_url).read() - with open(file_name, 'w') as f: - f.write(raw) + urlretrieve(download_url, file_name) # The first line is a comma-separated string. The first one is the number of # total data in the file. diff --git a/tensorflow/examples/learn/iris_custom_decay_dnn.py b/tensorflow/examples/learn/iris_custom_decay_dnn.py index 072357e51c..4a219694d1 100644 --- a/tensorflow/examples/learn/iris_custom_decay_dnn.py +++ b/tensorflow/examples/learn/iris_custom_decay_dnn.py @@ -46,12 +46,8 @@ def my_model(features, labels, mode): } return tf.estimator.EstimatorSpec(mode, predictions=predictions) - # Convert the labels to a one-hot tensor of shape (length of features, 3) and - # with a on-value of 1 for each one-hot vector of length 3. - onehot_labels = tf.one_hot(labels, 3, 1, 0) # Compute loss. - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Create training op with exponentially decaying learning rate. if mode == tf.estimator.ModeKeys.TRAIN: diff --git a/tensorflow/examples/learn/iris_custom_model.py b/tensorflow/examples/learn/iris_custom_model.py index 471a99ba76..c6bdb86ba5 100644 --- a/tensorflow/examples/learn/iris_custom_model.py +++ b/tensorflow/examples/learn/iris_custom_model.py @@ -47,12 +47,8 @@ def my_model(features, labels, mode): } return tf.estimator.EstimatorSpec(mode, predictions=predictions) - # Convert the labels to a one-hot tensor of shape (length of features, 3) and - # with a on-value of 1 for each one-hot vector of length 3. - onehot_labels = tf.one_hot(labels, 3, 1, 0) # Compute loss. - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Create training op. if mode == tf.estimator.ModeKeys.TRAIN: diff --git a/tensorflow/examples/learn/mnist.py b/tensorflow/examples/learn/mnist.py index 88425ea0d0..3ead8614b6 100644 --- a/tensorflow/examples/learn/mnist.py +++ b/tensorflow/examples/learn/mnist.py @@ -61,8 +61,10 @@ def conv_model(features, labels, mode): # Densely connected layer with 1024 neurons. h_fc1 = tf.layers.dense(h_pool2_flat, 1024, activation=tf.nn.relu) - if mode == tf.estimator.ModeKeys.TRAIN: - h_fc1 = tf.layers.dropout(h_fc1, rate=0.5) + h_fc1 = tf.layers.dropout( + h_fc1, + rate=0.5, + training=(mode == tf.estimator.ModeKeys.TRAIN)) # Compute logits (1 per class) and compute loss. logits = tf.layers.dense(h_fc1, N_DIGITS, activation=None) @@ -77,9 +79,7 @@ def conv_model(features, labels, mode): return tf.estimator.EstimatorSpec(mode, predictions=predictions) # Compute loss. - onehot_labels = tf.one_hot(tf.cast(labels, tf.int32), N_DIGITS, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Create training op. if mode == tf.estimator.ModeKeys.TRAIN: diff --git a/tensorflow/examples/learn/multiple_gpu.py b/tensorflow/examples/learn/multiple_gpu.py index a294950a38..3bad22ddf6 100644 --- a/tensorflow/examples/learn/multiple_gpu.py +++ b/tensorflow/examples/learn/multiple_gpu.py @@ -65,12 +65,8 @@ def my_model(features, labels, mode): } return tf.estimator.EstimatorSpec(mode, predictions=predictions) - # Convert the labels to a one-hot tensor of shape (length of features, 3) - # and with a on-value of 1 for each one-hot vector of length 3. - onehot_labels = tf.one_hot(labels, 3, 1, 0) # Compute loss. - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Create training op. if mode == tf.estimator.ModeKeys.TRAIN: diff --git a/tensorflow/examples/learn/resnet.py b/tensorflow/examples/learn/resnet.py index 1e0966475b..c00de932a8 100755 --- a/tensorflow/examples/learn/resnet.py +++ b/tensorflow/examples/learn/resnet.py @@ -53,6 +53,8 @@ def res_net_model(features, labels, mode): ndim = int(sqrt(input_shape[1])) x = tf.reshape(x, [-1, ndim, ndim, 1]) + training = (mode == tf.estimator.ModeKeys.TRAIN) + # First convolution expands to 64 channels with tf.variable_scope('conv_layer1'): net = tf.layers.conv2d( @@ -60,7 +62,7 @@ def res_net_model(features, labels, mode): filters=64, kernel_size=7, activation=tf.nn.relu) - net = tf.layers.batch_normalization(net) + net = tf.layers.batch_normalization(net, training=training) # Max pool net = tf.layers.max_pooling2d( @@ -88,7 +90,7 @@ def res_net_model(features, labels, mode): kernel_size=1, padding='valid', activation=tf.nn.relu) - conv = tf.layers.batch_normalization(conv) + conv = tf.layers.batch_normalization(conv, training=training) with tf.variable_scope(name + '/conv_bottleneck'): conv = tf.layers.conv2d( @@ -97,7 +99,7 @@ def res_net_model(features, labels, mode): kernel_size=3, padding='same', activation=tf.nn.relu) - conv = tf.layers.batch_normalization(conv) + conv = tf.layers.batch_normalization(conv, training=training) # 1x1 convolution responsible for restoring dimension with tf.variable_scope(name + '/conv_out'): @@ -108,7 +110,7 @@ def res_net_model(features, labels, mode): kernel_size=1, padding='valid', activation=tf.nn.relu) - conv = tf.layers.batch_normalization(conv) + conv = tf.layers.batch_normalization(conv, training=training) # shortcut connections that turn the network into its counterpart # residual function (identity shortcut) @@ -151,12 +153,10 @@ def res_net_model(features, labels, mode): return tf.estimator.EstimatorSpec(mode, predictions=predictions) # Compute loss. - onehot_labels = tf.one_hot(tf.cast(labels, tf.int32), N_DIGITS, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Create training op. - if mode == tf.estimator.ModeKeys.TRAIN: + if training: optimizer = tf.train.AdagradOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py index ba89c532be..e4e61862b0 100644 --- a/tensorflow/examples/learn/text_classification.py +++ b/tensorflow/examples/learn/text_classification.py @@ -34,8 +34,7 @@ MAX_LABEL = 15 WORDS_FEATURE = 'words' # Name of the input words feature. -def estimator_spec_for_softmax_classification( - logits, labels, mode): +def estimator_spec_for_softmax_classification(logits, labels, mode): """Returns EstimatorSpec instance for softmax classification.""" predicted_classes = tf.argmax(logits, 1) if mode == tf.estimator.ModeKeys.PREDICT: @@ -46,17 +45,15 @@ def estimator_spec_for_softmax_classification( 'prob': tf.nn.softmax(logits) }) - onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) eval_metric_ops = { - 'accuracy': tf.metrics.accuracy( - labels=labels, predictions=predicted_classes) + 'accuracy': + tf.metrics.accuracy(labels=labels, predictions=predicted_classes) } return tf.estimator.EstimatorSpec( mode=mode, loss=loss, eval_metric_ops=eval_metric_ops) @@ -69,8 +66,7 @@ def bag_of_words_model(features, labels, mode): bow_embedding_column = tf.feature_column.embedding_column( bow_column, dimension=EMBEDDING_SIZE) bow = tf.feature_column.input_layer( - features, - feature_columns=[bow_embedding_column]) + features, feature_columns=[bow_embedding_column]) logits = tf.layers.dense(bow, MAX_LABEL, activation=None) return estimator_spec_for_softmax_classification( @@ -112,9 +108,9 @@ def main(unused_argv): # Prepare training and testing data dbpedia = tf.contrib.learn.datasets.load_dataset( 'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data) - x_train = pandas.Series(dbpedia.train.data[:,1]) + x_train = pandas.Series(dbpedia.train.data[:, 1]) y_train = pandas.Series(dbpedia.train.target) - x_test = pandas.Series(dbpedia.test.data[:,1]) + x_test = pandas.Series(dbpedia.test.data[:, 1]) y_test = pandas.Series(dbpedia.test.target) # Process vocabulary @@ -154,10 +150,7 @@ def main(unused_argv): # Predict. test_input_fn = tf.estimator.inputs.numpy_input_fn( - x={WORDS_FEATURE: x_test}, - y=y_test, - num_epochs=1, - shuffle=False) + x={WORDS_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False) predictions = classifier.predict(input_fn=test_input_fn) y_predicted = np.array(list(p['class'] for p in predictions)) y_predicted = y_predicted.reshape(np.array(y_test).shape) diff --git a/tensorflow/examples/learn/text_classification_character_cnn.py b/tensorflow/examples/learn/text_classification_character_cnn.py index 363ff00362..afda170e2a 100644 --- a/tensorflow/examples/learn/text_classification_character_cnn.py +++ b/tensorflow/examples/learn/text_classification_character_cnn.py @@ -88,9 +88,7 @@ def char_cnn_model(features, labels, mode): 'prob': tf.nn.softmax(logits) }) - onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) diff --git a/tensorflow/examples/learn/text_classification_character_rnn.py b/tensorflow/examples/learn/text_classification_character_rnn.py index 86adc056ad..15733821fb 100644 --- a/tensorflow/examples/learn/text_classification_character_rnn.py +++ b/tensorflow/examples/learn/text_classification_character_rnn.py @@ -59,9 +59,7 @@ def char_rnn_model(features, labels, mode): 'prob': tf.nn.softmax(logits) }) - onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) diff --git a/tensorflow/examples/learn/text_classification_cnn.py b/tensorflow/examples/learn/text_classification_cnn.py index be262285a3..a40a9eaecb 100644 --- a/tensorflow/examples/learn/text_classification_cnn.py +++ b/tensorflow/examples/learn/text_classification_cnn.py @@ -73,7 +73,7 @@ def cnn_model(features, labels, mode): kernel_size=FILTER_SHAPE2, padding='VALID') # Max across each filter to get useful features for classification. - pool2 = tf.squeeze(tf.reduce_max(conv2, 1), squeeze_dims=[1]) + pool2 = tf.squeeze(tf.reduce_max(conv2, 1), axis=[1]) # Apply regular WX + B and classification. logits = tf.layers.dense(pool2, MAX_LABEL, activation=None) @@ -87,9 +87,7 @@ def cnn_model(features, labels, mode): 'prob': tf.nn.softmax(logits) }) - onehot_labels = tf.one_hot(labels, MAX_LABEL, 1, 0) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) diff --git a/tensorflow/examples/multibox_detector/BUILD b/tensorflow/examples/multibox_detector/BUILD index 91a5bfa51c..4f9908cd52 100644 --- a/tensorflow/examples/multibox_detector/BUILD +++ b/tensorflow/examples/multibox_detector/BUILD @@ -27,17 +27,3 @@ tf_cc_binary( "//tensorflow/core:tensorflow", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/multibox_detector/main.cc b/tensorflow/examples/multibox_detector/main.cc index e38704fd98..96ea525a4e 100644 --- a/tensorflow/examples/multibox_detector/main.cc +++ b/tensorflow/examples/multibox_detector/main.cc @@ -30,6 +30,7 @@ limitations under the License. #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/platform/logging.h" @@ -84,10 +85,10 @@ Status ReadTensorFromImageFile(const string& file_name, const int input_height, // Now try to figure out what kind of file it is and decode it. const int wanted_channels = 3; tensorflow::Output image_reader; - if (tensorflow::StringPiece(file_name).ends_with(".png")) { + if (tensorflow::str_util::EndsWith(file_name, ".png")) { image_reader = DecodePng(root.WithOpName("png_reader"), file_reader, DecodePng::Channels(wanted_channels)); - } else if (tensorflow::StringPiece(file_name).ends_with(".gif")) { + } else if (tensorflow::str_util::EndsWith(file_name, ".gif")) { image_reader = DecodeGif(root.WithOpName("gif_reader"), file_reader); } else { // Assume if it's neither a PNG nor a GIF then it must be a JPEG. @@ -131,7 +132,7 @@ Status ReadTensorFromImageFile(const string& file_name, const int input_height, Status SaveImage(const Tensor& tensor, const string& file_path) { LOG(INFO) << "Saving image to " << file_path; - CHECK(tensorflow::StringPiece(file_path).ends_with(".png")) + CHECK(tensorflow::str_util::EndsWith(file_path, ".png")) << "Only saving of png files is supported."; auto root = tensorflow::Scope::NewRootScope(); diff --git a/tensorflow/examples/saved_model/BUILD b/tensorflow/examples/saved_model/BUILD index 1cdf5ec6e1..ebefc6576d 100644 --- a/tensorflow/examples/saved_model/BUILD +++ b/tensorflow/examples/saved_model/BUILD @@ -8,19 +8,6 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "g3doc/sitemap.md", - ], - ), - visibility = ["//visibility:public"], -) - py_binary( name = "saved_model_half_plus_two", srcs = [ diff --git a/tensorflow/examples/saved_model/saved_model_half_plus_two.py b/tensorflow/examples/saved_model/saved_model_half_plus_two.py index 0d6f1ef655..2d1e0c6f6d 100644 --- a/tensorflow/examples/saved_model/saved_model_half_plus_two.py +++ b/tensorflow/examples/saved_model/saved_model_half_plus_two.py @@ -33,6 +33,13 @@ where `a`, `b` and `c` are variables with `a=0.5` and `b=2` and `c=3`. Output from this program is typically used to exercise SavedModel load and execution code. + +To create a CPU model: + bazel run -c opt saved_half_plus_two -- --device=cpu + +To create GPU model: + bazel run --config=cuda -c opt saved_half_plus_two -- \ + --device=gpu """ from __future__ import absolute_import @@ -105,42 +112,52 @@ def _build_classification_signature(input_tensor, scores_tensor): def _generate_saved_model_for_half_plus_two(export_dir, as_text=False, - use_main_op=False): + use_main_op=False, + device_type="cpu"): """Generates SavedModel for half plus two. Args: export_dir: The directory to which the SavedModel should be written. as_text: Writes the SavedModel protocol buffer in text format to disk. use_main_op: Whether to supply a main op during SavedModel build time. + device_name: Device to force ops to run on. """ builder = tf.saved_model.builder.SavedModelBuilder(export_dir) - with tf.Session(graph=tf.Graph()) as sess: - # Set up the model parameters as variables to exercise variable loading - # functionality upon restore. - a = tf.Variable(0.5, name="a") - b = tf.Variable(2.0, name="b") - c = tf.Variable(3.0, name="c") - - # Create a placeholder for serialized tensorflow.Example messages to be fed. - serialized_tf_example = tf.placeholder(tf.string, name="tf_example") - - # Parse the tensorflow.Example looking for a feature named "x" with a single - # floating point value. - feature_configs = { - "x": tf.FixedLenFeature( - [1], dtype=tf.float32), - "x2": tf.FixedLenFeature( - [1], dtype=tf.float32, default_value=[0.0]) - } - tf_example = tf.parse_example(serialized_tf_example, feature_configs) - # Use tf.identity() to assign name - x = tf.identity(tf_example["x"], name="x") - y = tf.add(tf.multiply(a, x), b, name="y") - y2 = tf.add(tf.multiply(a, x), c, name="y2") - - x2 = tf.identity(tf_example["x2"], name="x2") - y3 = tf.add(tf.multiply(a, x2), c, name="y3") + device_name = "/cpu:0" + if device_type == "gpu": + device_name = "/gpu:0" + + with tf.Session( + graph=tf.Graph(), + config=tf.ConfigProto(log_device_placement=True)) as sess: + with tf.device(device_name): + # Set up the model parameters as variables to exercise variable loading + # functionality upon restore. + a = tf.Variable(0.5, name="a") + b = tf.Variable(2.0, name="b") + c = tf.Variable(3.0, name="c") + + # Create a placeholder for serialized tensorflow.Example messages to be + # fed. + serialized_tf_example = tf.placeholder(tf.string, name="tf_example") + + # Parse the tensorflow.Example looking for a feature named "x" with a + # single floating point value. + feature_configs = { + "x": tf.FixedLenFeature([1], dtype=tf.float32), + "x2": tf.FixedLenFeature([1], dtype=tf.float32, default_value=[0.0]) + } + # parse_example only works on CPU + with tf.device("/cpu:0"): + tf_example = tf.parse_example(serialized_tf_example, feature_configs) + # Use tf.identity() to assign name + x = tf.identity(tf_example["x"], name="x") + y = tf.add(tf.multiply(a, x), b, name="y") + y2 = tf.add(tf.multiply(a, x), c, name="y2") + + x2 = tf.identity(tf_example["x2"], name="x2") + y3 = tf.add(tf.multiply(a, x2), c, name="y3") # Create an assets file that can be saved and restored as part of the # SavedModel. @@ -185,20 +202,7 @@ def _generate_saved_model_for_half_plus_two(export_dir, } # Initialize all variables and then save the SavedModel. sess.run(tf.global_variables_initializer()) - signature_def_map = { - "regress_x_to_y": - _build_regression_signature(serialized_tf_example, y), - "regress_x_to_y2": - _build_regression_signature(serialized_tf_example, y2), - "regress_x2_to_y3": - _build_regression_signature(x2, y3), - "classify_x_to_y": - _build_classification_signature(serialized_tf_example, y), - "classify_x2_to_y3": - _build_classification_signature(x2, y3), - tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: - predict_signature_def - } + if use_main_op: builder.add_meta_graph_and_variables( sess, [tf.saved_model.tag_constants.SERVING], @@ -212,19 +216,30 @@ def _generate_saved_model_for_half_plus_two(export_dir, signature_def_map=signature_def_map, assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS), legacy_init_op=tf.group(assign_filename_op)) - builder.save(as_text) + builder.save(as_text) def main(_): - _generate_saved_model_for_half_plus_two(FLAGS.output_dir) - print("SavedModel generated at: %s" % FLAGS.output_dir) + _generate_saved_model_for_half_plus_two( + FLAGS.output_dir, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s" % { + "device": FLAGS.device, + "dir": FLAGS.output_dir + }) - _generate_saved_model_for_half_plus_two(FLAGS.output_dir_pbtxt, as_text=True) - print("SavedModel generated at: %s" % FLAGS.output_dir_pbtxt) + _generate_saved_model_for_half_plus_two( + FLAGS.output_dir_pbtxt, as_text=True, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s" % { + "device": FLAGS.device, + "dir": FLAGS.output_dir_pbtxt + }) _generate_saved_model_for_half_plus_two( - FLAGS.output_dir_main_op, use_main_op=True) - print("SavedModel generated at: %s" % FLAGS.output_dir_main_op) + FLAGS.output_dir_main_op, use_main_op=True, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s " % { + "device": FLAGS.device, + "dir": FLAGS.output_dir_main_op + }) if __name__ == "__main__": @@ -244,5 +259,10 @@ if __name__ == "__main__": type=str, default="/tmp/saved_model_half_plus_two_main_op", help="Directory where to output the SavedModel with a main op.") + parser.add_argument( + "--device", + type=str, + default="cpu", + help="Force model to run on 'cpu' or 'gpu'") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/BUILD b/tensorflow/examples/speech_commands/BUILD index 12479211c3..7a44e2ee4f 100644 --- a/tensorflow/examples/speech_commands/BUILD +++ b/tensorflow/examples/speech_commands/BUILD @@ -56,6 +56,7 @@ tf_py_test( srcs = ["input_data_test.py"], additional_deps = [ ":input_data", + ":models", "//tensorflow/python:client_testlib", ], ) @@ -245,15 +246,3 @@ tf_cc_binary( "//tensorflow/core:protos_all_cc", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/speech_commands/accuracy_utils.h b/tensorflow/examples/speech_commands/accuracy_utils.h index 8d918cb64b..eea048365b 100644 --- a/tensorflow/examples/speech_commands/accuracy_utils.h +++ b/tensorflow/examples/speech_commands/accuracy_utils.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ +#ifndef TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ +#define TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ #include <vector> @@ -57,4 +57,4 @@ void PrintAccuracyStats(const StreamingAccuracyStats& stats); } // namespace tensorflow -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ +#endif // TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_ACCURACY_UTILS_H_ diff --git a/tensorflow/examples/speech_commands/freeze.py b/tensorflow/examples/speech_commands/freeze.py index c8671d9c41..89e790d4e4 100644 --- a/tensorflow/examples/speech_commands/freeze.py +++ b/tensorflow/examples/speech_commands/freeze.py @@ -54,7 +54,7 @@ FLAGS = None def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, clip_stride_ms, window_size_ms, window_stride_ms, - dct_coefficient_count, model_architecture): + feature_bin_count, model_architecture, preprocess): """Creates an audio model with the nodes needed for inference. Uses the supplied arguments to create a model, and inserts the input and @@ -67,14 +67,19 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, clip_stride_ms: How often to run recognition. Useful for models with cache. window_size_ms: Time slice duration to estimate frequencies from. window_stride_ms: How far apart time slices should be. - dct_coefficient_count: Number of frequency bands to analyze. + feature_bin_count: Number of frequency bands to analyze. model_architecture: Name of the kind of model to generate. + preprocess: How the spectrogram is processed to produce features, for + example 'mfcc' or 'average'. + + Raises: + Exception: If the preprocessing mode isn't recognized. """ words_list = input_data.prepare_words_list(wanted_words.split(',')) model_settings = models.prepare_model_settings( len(words_list), sample_rate, clip_duration_ms, window_size_ms, - window_stride_ms, dct_coefficient_count) + window_stride_ms, feature_bin_count, preprocess) runtime_settings = {'clip_stride_ms': clip_stride_ms} wav_data_placeholder = tf.placeholder(tf.string, [], name='wav_data') @@ -88,15 +93,25 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, window_size=model_settings['window_size_samples'], stride=model_settings['window_stride_samples'], magnitude_squared=True) - fingerprint_input = contrib_audio.mfcc( - spectrogram, - decoded_sample_data.sample_rate, - dct_coefficient_count=dct_coefficient_count) - fingerprint_frequency_size = model_settings['dct_coefficient_count'] - fingerprint_time_size = model_settings['spectrogram_length'] - reshaped_input = tf.reshape(fingerprint_input, [ - -1, fingerprint_time_size * fingerprint_frequency_size - ]) + + if preprocess == 'average': + fingerprint_input = tf.nn.pool( + tf.expand_dims(spectrogram, -1), + window_shape=[1, model_settings['average_window_width']], + strides=[1, model_settings['average_window_width']], + pooling_type='AVG', + padding='SAME') + elif preprocess == 'mfcc': + fingerprint_input = contrib_audio.mfcc( + spectrogram, + sample_rate, + dct_coefficient_count=model_settings['fingerprint_width']) + else: + raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (preprocess)) + + fingerprint_size = model_settings['fingerprint_size'] + reshaped_input = tf.reshape(fingerprint_input, [-1, fingerprint_size]) logits = models.create_model( reshaped_input, model_settings, model_architecture, is_training=False, @@ -110,10 +125,12 @@ def main(_): # Create the model and load its weights. sess = tf.InteractiveSession() - create_inference_graph(FLAGS.wanted_words, FLAGS.sample_rate, - FLAGS.clip_duration_ms, FLAGS.clip_stride_ms, - FLAGS.window_size_ms, FLAGS.window_stride_ms, - FLAGS.dct_coefficient_count, FLAGS.model_architecture) + create_inference_graph( + FLAGS.wanted_words, FLAGS.sample_rate, FLAGS.clip_duration_ms, + FLAGS.clip_stride_ms, FLAGS.window_size_ms, FLAGS.window_stride_ms, + FLAGS.feature_bin_count, FLAGS.model_architecture, FLAGS.preprocess) + if FLAGS.quantize: + tf.contrib.quantize.create_eval_graph() models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint) # Turn all the variables into inline constants inside the graph and save it. @@ -155,10 +172,11 @@ if __name__ == '__main__': default=10.0, help='How long the stride is between spectrogram timeslices',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--start_checkpoint', type=str, @@ -176,5 +194,15 @@ if __name__ == '__main__': help='Words to use (others will be added to an unknown label)',) parser.add_argument( '--output_file', type=str, help='Where to save the frozen graph.') + parser.add_argument( + '--quantize', + type=bool, + default=False, + help='Whether to train the model for eight-bit deployment') + parser.add_argument( + '--preprocess', + type=str, + default='mfcc', + help='Spectrogram processing mode. Can be "mfcc" or "average"') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/freeze_test.py b/tensorflow/examples/speech_commands/freeze_test.py index 97c6eac675..c8de6c2152 100644 --- a/tensorflow/examples/speech_commands/freeze_test.py +++ b/tensorflow/examples/speech_commands/freeze_test.py @@ -24,14 +24,62 @@ from tensorflow.python.platform import test class FreezeTest(test.TestCase): - def testCreateInferenceGraph(self): + def testCreateInferenceGraphWithMfcc(self): with self.test_session() as sess: - freeze.create_inference_graph('a,b,c,d', 16000, 1000.0, 30.0, 30.0, 10.0, - 40, 'conv') + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=40, + model_architecture='conv', + preprocess='mfcc') self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) self.assertIsNotNone( sess.graph.get_tensor_by_name('decoded_sample_data:0')) self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(1, ops.count('Mfcc')) + + def testCreateInferenceGraphWithoutMfcc(self): + with self.test_session() as sess: + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=40, + model_architecture='conv', + preprocess='average') + self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) + self.assertIsNotNone( + sess.graph.get_tensor_by_name('decoded_sample_data:0')) + self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(0, ops.count('Mfcc')) + + def testFeatureBinCount(self): + with self.test_session() as sess: + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=80, + model_architecture='conv', + preprocess='average') + self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) + self.assertIsNotNone( + sess.graph.get_tensor_by_name('decoded_sample_data:0')) + self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(0, ops.count('Mfcc')) if __name__ == '__main__': diff --git a/tensorflow/examples/speech_commands/generate_streaming_test_wav.py b/tensorflow/examples/speech_commands/generate_streaming_test_wav.py index 053206ae2f..9858906927 100644 --- a/tensorflow/examples/speech_commands/generate_streaming_test_wav.py +++ b/tensorflow/examples/speech_commands/generate_streaming_test_wav.py @@ -87,11 +87,12 @@ def main(_): words_list = input_data.prepare_words_list(FLAGS.wanted_words.split(',')) model_settings = models.prepare_model_settings( len(words_list), FLAGS.sample_rate, FLAGS.clip_duration_ms, - FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.dct_coefficient_count) + FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.feature_bin_count, + 'mfcc') audio_processor = input_data.AudioProcessor( '', FLAGS.data_dir, FLAGS.silence_percentage, 10, FLAGS.wanted_words.split(','), FLAGS.validation_percentage, - FLAGS.testing_percentage, model_settings) + FLAGS.testing_percentage, model_settings, FLAGS.data_dir) output_audio_sample_count = FLAGS.sample_rate * FLAGS.test_duration_seconds output_audio = np.zeros((output_audio_sample_count,), dtype=np.float32) @@ -242,10 +243,11 @@ if __name__ == '__main__': default=10.0, help='How long the stride is between spectrogram timeslices',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--wanted_words', type=str, diff --git a/tensorflow/examples/speech_commands/input_data.py b/tensorflow/examples/speech_commands/input_data.py index 751652b330..30f2cfa9fe 100644 --- a/tensorflow/examples/speech_commands/input_data.py +++ b/tensorflow/examples/speech_commands/input_data.py @@ -153,14 +153,14 @@ class AudioProcessor(object): def __init__(self, data_url, data_dir, silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage, - model_settings): + model_settings, summaries_dir): self.data_dir = data_dir self.maybe_download_and_extract_dataset(data_url, data_dir) self.prepare_data_index(silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage) self.prepare_background_data() - self.prepare_processing_graph(model_settings) + self.prepare_processing_graph(model_settings, summaries_dir) def maybe_download_and_extract_dataset(self, data_url, dest_directory): """Download and extract data set tar file. @@ -325,7 +325,7 @@ class AudioProcessor(object): if not self.background_data: raise Exception('No background wav files were found in ' + search_path) - def prepare_processing_graph(self, model_settings): + def prepare_processing_graph(self, model_settings, summaries_dir): """Builds a TensorFlow graph to apply the input distortions. Creates a graph that loads a WAVE file, decodes it, scales the volume, @@ -341,48 +341,88 @@ class AudioProcessor(object): - time_shift_offset_placeholder_: How much to move the clip in time. - background_data_placeholder_: PCM sample data for background noise. - background_volume_placeholder_: Loudness of mixed-in background. - - mfcc_: Output 2D fingerprint of processed audio. + - output_: Output 2D fingerprint of processed audio. Args: model_settings: Information about the current model being trained. + summaries_dir: Path to save training summary information to. + + Raises: + ValueError: If the preprocessing mode isn't recognized. """ - desired_samples = model_settings['desired_samples'] - self.wav_filename_placeholder_ = tf.placeholder(tf.string, []) - wav_loader = io_ops.read_file(self.wav_filename_placeholder_) - wav_decoder = contrib_audio.decode_wav( - wav_loader, desired_channels=1, desired_samples=desired_samples) - # Allow the audio sample's volume to be adjusted. - self.foreground_volume_placeholder_ = tf.placeholder(tf.float32, []) - scaled_foreground = tf.multiply(wav_decoder.audio, - self.foreground_volume_placeholder_) - # Shift the sample's start position, and pad any gaps with zeros. - self.time_shift_padding_placeholder_ = tf.placeholder(tf.int32, [2, 2]) - self.time_shift_offset_placeholder_ = tf.placeholder(tf.int32, [2]) - padded_foreground = tf.pad( - scaled_foreground, - self.time_shift_padding_placeholder_, - mode='CONSTANT') - sliced_foreground = tf.slice(padded_foreground, - self.time_shift_offset_placeholder_, - [desired_samples, -1]) - # Mix in background noise. - self.background_data_placeholder_ = tf.placeholder(tf.float32, - [desired_samples, 1]) - self.background_volume_placeholder_ = tf.placeholder(tf.float32, []) - background_mul = tf.multiply(self.background_data_placeholder_, - self.background_volume_placeholder_) - background_add = tf.add(background_mul, sliced_foreground) - background_clamp = tf.clip_by_value(background_add, -1.0, 1.0) - # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio. - spectrogram = contrib_audio.audio_spectrogram( - background_clamp, - window_size=model_settings['window_size_samples'], - stride=model_settings['window_stride_samples'], - magnitude_squared=True) - self.mfcc_ = contrib_audio.mfcc( - spectrogram, - wav_decoder.sample_rate, - dct_coefficient_count=model_settings['dct_coefficient_count']) + with tf.get_default_graph().name_scope('data'): + desired_samples = model_settings['desired_samples'] + self.wav_filename_placeholder_ = tf.placeholder( + tf.string, [], name='wav_filename') + wav_loader = io_ops.read_file(self.wav_filename_placeholder_) + wav_decoder = contrib_audio.decode_wav( + wav_loader, desired_channels=1, desired_samples=desired_samples) + # Allow the audio sample's volume to be adjusted. + self.foreground_volume_placeholder_ = tf.placeholder( + tf.float32, [], name='foreground_volume') + scaled_foreground = tf.multiply(wav_decoder.audio, + self.foreground_volume_placeholder_) + # Shift the sample's start position, and pad any gaps with zeros. + self.time_shift_padding_placeholder_ = tf.placeholder( + tf.int32, [2, 2], name='time_shift_padding') + self.time_shift_offset_placeholder_ = tf.placeholder( + tf.int32, [2], name='time_shift_offset') + padded_foreground = tf.pad( + scaled_foreground, + self.time_shift_padding_placeholder_, + mode='CONSTANT') + sliced_foreground = tf.slice(padded_foreground, + self.time_shift_offset_placeholder_, + [desired_samples, -1]) + # Mix in background noise. + self.background_data_placeholder_ = tf.placeholder( + tf.float32, [desired_samples, 1], name='background_data') + self.background_volume_placeholder_ = tf.placeholder( + tf.float32, [], name='background_volume') + background_mul = tf.multiply(self.background_data_placeholder_, + self.background_volume_placeholder_) + background_add = tf.add(background_mul, sliced_foreground) + background_clamp = tf.clip_by_value(background_add, -1.0, 1.0) + # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio. + spectrogram = contrib_audio.audio_spectrogram( + background_clamp, + window_size=model_settings['window_size_samples'], + stride=model_settings['window_stride_samples'], + magnitude_squared=True) + tf.summary.image( + 'spectrogram', tf.expand_dims(spectrogram, -1), max_outputs=1) + # The number of buckets in each FFT row in the spectrogram will depend on + # how many input samples there are in each window. This can be quite + # large, with a 160 sample window producing 127 buckets for example. We + # don't need this level of detail for classification, so we often want to + # shrink them down to produce a smaller result. That's what this section + # implements. One method is to use average pooling to merge adjacent + # buckets, but a more sophisticated approach is to apply the MFCC + # algorithm to shrink the representation. + if model_settings['preprocess'] == 'average': + self.output_ = tf.nn.pool( + tf.expand_dims(spectrogram, -1), + window_shape=[1, model_settings['average_window_width']], + strides=[1, model_settings['average_window_width']], + pooling_type='AVG', + padding='SAME') + tf.summary.image('shrunk_spectrogram', self.output_, max_outputs=1) + elif model_settings['preprocess'] == 'mfcc': + self.output_ = contrib_audio.mfcc( + spectrogram, + wav_decoder.sample_rate, + dct_coefficient_count=model_settings['fingerprint_width']) + tf.summary.image( + 'mfcc', tf.expand_dims(self.output_, -1), max_outputs=1) + else: + raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (model_settings['preprocess'])) + + # Merge all the summaries and write them out to /tmp/retrain_logs (by + # default) + self.merged_summaries_ = tf.summary.merge_all(scope='data') + self.summary_writer_ = tf.summary.FileWriter(summaries_dir + '/data', + tf.get_default_graph()) def set_size(self, mode): """Calculates the number of samples in the dataset partition. @@ -417,8 +457,10 @@ class AudioProcessor(object): sess: TensorFlow session that was active when processor was created. Returns: - List of sample data for the transformed samples, and list of labels in - one-hot form. + List of sample data for the transformed samples, and list of label indexes + + Raises: + ValueError: If background samples are too short. """ # Pick one of the partitions to choose samples from. candidates = self.data_index[mode] @@ -428,7 +470,7 @@ class AudioProcessor(object): sample_count = max(0, min(how_many, len(candidates) - offset)) # Data and labels will be populated and returned. data = np.zeros((sample_count, model_settings['fingerprint_size'])) - labels = np.zeros((sample_count, model_settings['label_count'])) + labels = np.zeros(sample_count) desired_samples = model_settings['desired_samples'] use_background = self.background_data and (mode == 'training') pick_deterministically = (mode != 'training') @@ -458,15 +500,22 @@ class AudioProcessor(object): self.time_shift_offset_placeholder_: time_shift_offset, } # Choose a section of background noise to mix in. - if use_background: + if use_background or sample['label'] == SILENCE_LABEL: background_index = np.random.randint(len(self.background_data)) background_samples = self.background_data[background_index] + if len(background_samples) <= model_settings['desired_samples']: + raise ValueError( + 'Background sample is too short! Need more than %d' + ' samples but only %d were found' % + (model_settings['desired_samples'], len(background_samples))) background_offset = np.random.randint( 0, len(background_samples) - model_settings['desired_samples']) background_clipped = background_samples[background_offset:( background_offset + desired_samples)] background_reshaped = background_clipped.reshape([desired_samples, 1]) - if np.random.uniform(0, 1) < background_frequency: + if sample['label'] == SILENCE_LABEL: + background_volume = np.random.uniform(0, 1) + elif np.random.uniform(0, 1) < background_frequency: background_volume = np.random.uniform(0, background_volume_range) else: background_volume = 0 @@ -481,9 +530,12 @@ class AudioProcessor(object): else: input_dict[self.foreground_volume_placeholder_] = 1 # Run the graph to produce the output audio. - data[i - offset, :] = sess.run(self.mfcc_, feed_dict=input_dict).flatten() + summary, data_tensor = sess.run( + [self.merged_summaries_, self.output_], feed_dict=input_dict) + self.summary_writer_.add_summary(summary) + data[i - offset, :] = data_tensor.flatten() label_index = self.word_to_index[sample['label']] - labels[i - offset, label_index] = 1 + labels[i - offset] = label_index return data, labels def get_unprocessed_data(self, how_many, model_settings, mode): diff --git a/tensorflow/examples/speech_commands/input_data_test.py b/tensorflow/examples/speech_commands/input_data_test.py index 13f294d39d..2e551be9a2 100644 --- a/tensorflow/examples/speech_commands/input_data_test.py +++ b/tensorflow/examples/speech_commands/input_data_test.py @@ -25,6 +25,7 @@ import tensorflow as tf from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio from tensorflow.examples.speech_commands import input_data +from tensorflow.examples.speech_commands import models from tensorflow.python.platform import test @@ -32,7 +33,7 @@ class InputDataTest(test.TestCase): def _getWavData(self): with self.test_session() as sess: - sample_data = tf.zeros([1000, 2]) + sample_data = tf.zeros([32000, 2]) wav_encoder = contrib_audio.encode_wav(sample_data, 16000) wav_data = sess.run(wav_encoder) return wav_data @@ -57,9 +58,31 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } + def _runGetDataTest(self, preprocess, window_length_ms): + tmp_dir = self.get_temp_dir() + wav_dir = os.path.join(tmp_dir, "wavs") + os.mkdir(wav_dir) + self._saveWavFolders(wav_dir, ["a", "b", "c"], 100) + background_dir = os.path.join(wav_dir, "_background_noise_") + os.mkdir(background_dir) + wav_data = self._getWavData() + for i in range(10): + file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) + self._saveTestWavFile(file_path, wav_data) + model_settings = models.prepare_model_settings( + 4, 16000, 1000, window_length_ms, 20, 40, preprocess) + with self.test_session() as sess: + audio_processor = input_data.AudioProcessor( + "", wav_dir, 10, 10, ["a", "b"], 10, 10, model_settings, tmp_dir) + result_data, result_labels = audio_processor.get_data( + 10, 0, model_settings, 0.3, 0.1, 100, "training", sess) + self.assertEqual(10, len(result_data)) + self.assertEqual(10, len(result_labels)) + def testPrepareWordsList(self): words_list = ["a", "b"] self.assertGreater( @@ -76,8 +99,9 @@ class InputDataTest(test.TestCase): def testPrepareDataIndex(self): tmp_dir = self.get_temp_dir() self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) - audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], - 10, 10, self._model_settings()) + audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, + ["a", "b"], 10, 10, + self._model_settings(), tmp_dir) self.assertLess(0, audio_processor.set_size("training")) self.assertTrue("training" in audio_processor.data_index) self.assertTrue("validation" in audio_processor.data_index) @@ -90,7 +114,7 @@ class InputDataTest(test.TestCase): self._saveWavFolders(tmp_dir, ["a", "b", "c"], 0) with self.assertRaises(Exception) as e: _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 10, 10, - self._model_settings()) + self._model_settings(), tmp_dir) self.assertTrue("No .wavs found" in str(e.exception)) def testPrepareDataIndexMissing(self): @@ -98,7 +122,7 @@ class InputDataTest(test.TestCase): self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) with self.assertRaises(Exception) as e: _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b", "d"], 10, - 10, self._model_settings()) + 10, self._model_settings(), tmp_dir) self.assertTrue("Expected to find" in str(e.exception)) def testPrepareBackgroundData(self): @@ -110,8 +134,9 @@ class InputDataTest(test.TestCase): file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) self._saveTestWavFile(file_path, wav_data) self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) - audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], - 10, 10, self._model_settings()) + audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, + ["a", "b"], 10, 10, + self._model_settings(), tmp_dir) self.assertEqual(10, len(audio_processor.background_data)) def testLoadWavFile(self): @@ -148,44 +173,27 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) + 10, 10, model_settings, tmp_dir) self.assertIsNotNone(audio_processor.wav_filename_placeholder_) self.assertIsNotNone(audio_processor.foreground_volume_placeholder_) self.assertIsNotNone(audio_processor.time_shift_padding_placeholder_) self.assertIsNotNone(audio_processor.time_shift_offset_placeholder_) self.assertIsNotNone(audio_processor.background_data_placeholder_) self.assertIsNotNone(audio_processor.background_volume_placeholder_) - self.assertIsNotNone(audio_processor.mfcc_) + self.assertIsNotNone(audio_processor.output_) - def testGetData(self): - tmp_dir = self.get_temp_dir() - wav_dir = os.path.join(tmp_dir, "wavs") - os.mkdir(wav_dir) - self._saveWavFolders(wav_dir, ["a", "b", "c"], 100) - background_dir = os.path.join(wav_dir, "_background_noise_") - os.mkdir(background_dir) - wav_data = self._getWavData() - for i in range(10): - file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) - self._saveTestWavFile(file_path, wav_data) - model_settings = { - "desired_samples": 160, - "fingerprint_size": 40, - "label_count": 4, - "window_size_samples": 100, - "window_stride_samples": 100, - "dct_coefficient_count": 40, - } - audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) - with self.test_session() as sess: - result_data, result_labels = audio_processor.get_data( - 10, 0, model_settings, 0.3, 0.1, 100, "training", sess) - self.assertEqual(10, len(result_data)) - self.assertEqual(10, len(result_labels)) + def testGetDataAverage(self): + self._runGetDataTest("average", 10) + + def testGetDataAverageLongWindow(self): + self._runGetDataTest("average", 30) + + def testGetDataMfcc(self): + self._runGetDataTest("mfcc", 30) def testGetUnprocessedData(self): tmp_dir = self.get_temp_dir() @@ -198,10 +206,11 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) + 10, 10, model_settings, tmp_dir) result_data, result_labels = audio_processor.get_unprocessed_data( 10, model_settings, "training") self.assertEqual(10, len(result_data)) diff --git a/tensorflow/examples/speech_commands/label_wav_dir.py b/tensorflow/examples/speech_commands/label_wav_dir.py new file mode 100644 index 0000000000..a34db512dd --- /dev/null +++ b/tensorflow/examples/speech_commands/label_wav_dir.py @@ -0,0 +1,136 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Runs a trained audio graph against WAVE files and reports the results. + +The model, labels and .wav files specified in the arguments will be loaded, and +then the predictions from running the model against the audio data will be +printed to the console. This is a useful script for sanity checking trained +models, and as an example of how to use an audio model from Python. + +Here's an example of running it: + +python tensorflow/examples/speech_commands/label_wav_dir.py \ +--graph=/tmp/my_frozen_graph.pb \ +--labels=/tmp/speech_commands_train/conv_labels.txt \ +--wav_dir=/tmp/speech_dataset/left + +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import glob +import sys + +import tensorflow as tf + +# pylint: disable=unused-import +from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio +# pylint: enable=unused-import + +FLAGS = None + + +def load_graph(filename): + """Unpersists graph from file as default graph.""" + with tf.gfile.FastGFile(filename, 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name='') + + +def load_labels(filename): + """Read in labels, one label per line.""" + return [line.rstrip() for line in tf.gfile.GFile(filename)] + + +def run_graph(wav_dir, labels, input_layer_name, output_layer_name, + num_top_predictions): + """Runs the audio data through the graph and prints predictions.""" + with tf.Session() as sess: + # Feed the audio data as input to the graph. + # predictions will contain a two-dimensional array, where one + # dimension represents the input image count, and the other has + # predictions per class + for wav_path in glob.glob(wav_dir + '/*.wav'): + if not wav_path or not tf.gfile.Exists(wav_path): + tf.logging.fatal('Audio file does not exist %s', wav_path) + + with open(wav_path, 'rb') as wav_file: + wav_data = wav_file.read() + + softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name) + predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data}) + + # Sort to show labels in order of confidence + print('\n%s' % (wav_path.split('/')[-1])) + top_k = predictions.argsort()[-num_top_predictions:][::-1] + for node_id in top_k: + human_string = labels[node_id] + score = predictions[node_id] + print('%s (score = %.5f)' % (human_string, score)) + + return 0 + + +def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels): + """Loads the model and labels, and runs the inference to print predictions.""" + if not labels or not tf.gfile.Exists(labels): + tf.logging.fatal('Labels file does not exist %s', labels) + + if not graph or not tf.gfile.Exists(graph): + tf.logging.fatal('Graph file does not exist %s', graph) + + labels_list = load_labels(labels) + + # load graph, which is stored in the default session + load_graph(graph) + + run_graph(wav_dir, labels_list, input_name, output_name, how_many_labels) + + +def main(_): + """Entry point for script, converts flags to arguments.""" + label_wav(FLAGS.wav_dir, FLAGS.labels, FLAGS.graph, FLAGS.input_name, + FLAGS.output_name, FLAGS.how_many_labels) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--wav_dir', type=str, default='', help='Audio file to be identified.') + parser.add_argument( + '--graph', type=str, default='', help='Model to use for identification.') + parser.add_argument( + '--labels', type=str, default='', help='Path to file containing labels.') + parser.add_argument( + '--input_name', + type=str, + default='wav_data:0', + help='Name of WAVE data input node in model.') + parser.add_argument( + '--output_name', + type=str, + default='labels_softmax:0', + help='Name of node outputting a prediction in the model.') + parser.add_argument( + '--how_many_labels', + type=int, + default=3, + help='Number of results to show.') + + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/models.py b/tensorflow/examples/speech_commands/models.py index ab611f414a..4d1454be0d 100644 --- a/tensorflow/examples/speech_commands/models.py +++ b/tensorflow/examples/speech_commands/models.py @@ -24,9 +24,21 @@ import math import tensorflow as tf +def _next_power_of_two(x): + """Calculates the smallest enclosing power of two for an input. + + Args: + x: Positive float or integer number. + + Returns: + Next largest power of two integer. + """ + return 1 if x == 0 else 2**(int(x) - 1).bit_length() + + def prepare_model_settings(label_count, sample_rate, clip_duration_ms, - window_size_ms, window_stride_ms, - dct_coefficient_count): + window_size_ms, window_stride_ms, feature_bin_count, + preprocess): """Calculates common settings needed for all models. Args: @@ -35,10 +47,14 @@ def prepare_model_settings(label_count, sample_rate, clip_duration_ms, clip_duration_ms: Length of each audio clip to be analyzed. window_size_ms: Duration of frequency analysis window. window_stride_ms: How far to move in time between frequency windows. - dct_coefficient_count: Number of frequency bins to use for analysis. + feature_bin_count: Number of frequency bins to use for analysis. + preprocess: How the spectrogram is processed to produce features. Returns: Dictionary containing common settings. + + Raises: + ValueError: If the preprocessing mode isn't recognized. """ desired_samples = int(sample_rate * clip_duration_ms / 1000) window_size_samples = int(sample_rate * window_size_ms / 1000) @@ -48,16 +64,28 @@ def prepare_model_settings(label_count, sample_rate, clip_duration_ms, spectrogram_length = 0 else: spectrogram_length = 1 + int(length_minus_window / window_stride_samples) - fingerprint_size = dct_coefficient_count * spectrogram_length + if preprocess == 'average': + fft_bin_count = 1 + (_next_power_of_two(window_size_samples) / 2) + average_window_width = int(math.floor(fft_bin_count / feature_bin_count)) + fingerprint_width = int(math.ceil(fft_bin_count / average_window_width)) + elif preprocess == 'mfcc': + average_window_width = -1 + fingerprint_width = feature_bin_count + else: + raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (preprocess)) + fingerprint_size = fingerprint_width * spectrogram_length return { 'desired_samples': desired_samples, 'window_size_samples': window_size_samples, 'window_stride_samples': window_stride_samples, 'spectrogram_length': spectrogram_length, - 'dct_coefficient_count': dct_coefficient_count, + 'fingerprint_width': fingerprint_width, 'fingerprint_size': fingerprint_size, 'label_count': label_count, 'sample_rate': sample_rate, + 'preprocess': preprocess, + 'average_window_width': average_window_width, } @@ -106,10 +134,14 @@ def create_model(fingerprint_input, model_settings, model_architecture, elif model_architecture == 'low_latency_svdf': return create_low_latency_svdf_model(fingerprint_input, model_settings, is_training, runtime_settings) + elif model_architecture == 'tiny_conv': + return create_tiny_conv_model(fingerprint_input, model_settings, + is_training) else: raise Exception('model_architecture argument "' + model_architecture + '" not recognized, should be one of "single_fc", "conv",' + - ' "low_latency_conv, or "low_latency_svdf"') + ' "low_latency_conv, "low_latency_svdf",' + + ' or "tiny_conv"') def load_variables_from_checkpoint(sess, start_checkpoint): @@ -152,9 +184,12 @@ def create_single_fc_model(fingerprint_input, model_settings, is_training): dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') fingerprint_size = model_settings['fingerprint_size'] label_count = model_settings['label_count'] - weights = tf.Variable( - tf.truncated_normal([fingerprint_size, label_count], stddev=0.001)) - bias = tf.Variable(tf.zeros([label_count])) + weights = tf.get_variable( + name='weights', + initializer=tf.truncated_normal_initializer(stddev=0.001), + shape=[fingerprint_size, label_count]) + bias = tf.get_variable( + name='bias', initializer=tf.zeros_initializer, shape=[label_count]) logits = tf.matmul(fingerprint_input, weights) + bias if is_training: return logits, dropout_prob @@ -212,18 +247,21 @@ def create_conv_model(fingerprint_input, model_settings, is_training): """ if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] fingerprint_4d = tf.reshape(fingerprint_input, [-1, input_time_size, input_frequency_size, 1]) first_filter_width = 8 first_filter_height = 20 first_filter_count = 64 - first_weights = tf.Variable( - tf.truncated_normal( - [first_filter_height, first_filter_width, 1, first_filter_count], - stddev=0.01)) - first_bias = tf.Variable(tf.zeros([first_filter_count])) + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [1, 1, 1, 1], 'SAME') + first_bias first_relu = tf.nn.relu(first_conv) @@ -235,14 +273,17 @@ def create_conv_model(fingerprint_input, model_settings, is_training): second_filter_width = 4 second_filter_height = 10 second_filter_count = 64 - second_weights = tf.Variable( - tf.truncated_normal( - [ - second_filter_height, second_filter_width, first_filter_count, - second_filter_count - ], - stddev=0.01)) - second_bias = tf.Variable(tf.zeros([second_filter_count])) + second_weights = tf.get_variable( + name='second_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[ + second_filter_height, second_filter_width, first_filter_count, + second_filter_count + ]) + second_bias = tf.get_variable( + name='second_bias', + initializer=tf.zeros_initializer, + shape=[second_filter_count]) second_conv = tf.nn.conv2d(max_pool, second_weights, [1, 1, 1, 1], 'SAME') + second_bias second_relu = tf.nn.relu(second_conv) @@ -259,10 +300,14 @@ def create_conv_model(fingerprint_input, model_settings, is_training): flattened_second_conv = tf.reshape(second_dropout, [-1, second_conv_element_count]) label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_conv_element_count, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[second_conv_element_count, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(flattened_second_conv, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob @@ -318,7 +363,7 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, """ if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] fingerprint_4d = tf.reshape(fingerprint_input, [-1, input_time_size, input_frequency_size, 1]) @@ -327,11 +372,14 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, first_filter_count = 186 first_filter_stride_x = 1 first_filter_stride_y = 1 - first_weights = tf.Variable( - tf.truncated_normal( - [first_filter_height, first_filter_width, 1, first_filter_count], - stddev=0.01)) - first_bias = tf.Variable(tf.zeros([first_filter_count])) + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [ 1, first_filter_stride_y, first_filter_stride_x, 1 ], 'VALID') + first_bias @@ -351,30 +399,42 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, flattened_first_conv = tf.reshape(first_dropout, [-1, first_conv_element_count]) first_fc_output_channels = 128 - first_fc_weights = tf.Variable( - tf.truncated_normal( - [first_conv_element_count, first_fc_output_channels], stddev=0.01)) - first_fc_bias = tf.Variable(tf.zeros([first_fc_output_channels])) + first_fc_weights = tf.get_variable( + name='first_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_conv_element_count, first_fc_output_channels]) + first_fc_bias = tf.get_variable( + name='first_fc_bias', + initializer=tf.zeros_initializer, + shape=[first_fc_output_channels]) first_fc = tf.matmul(flattened_first_conv, first_fc_weights) + first_fc_bias if is_training: second_fc_input = tf.nn.dropout(first_fc, dropout_prob) else: second_fc_input = first_fc second_fc_output_channels = 128 - second_fc_weights = tf.Variable( - tf.truncated_normal( - [first_fc_output_channels, second_fc_output_channels], stddev=0.01)) - second_fc_bias = tf.Variable(tf.zeros([second_fc_output_channels])) + second_fc_weights = tf.get_variable( + name='second_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_fc_output_channels, second_fc_output_channels]) + second_fc_bias = tf.get_variable( + name='second_fc_bias', + initializer=tf.zeros_initializer, + shape=[second_fc_output_channels]) second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias if is_training: final_fc_input = tf.nn.dropout(second_fc, dropout_prob) else: final_fc_input = second_fc label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_fc_output_channels, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[second_fc_output_channels, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob @@ -422,7 +482,7 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, Args: fingerprint_input: TensorFlow node that will output audio feature vectors. The node is expected to produce a 2D Tensor of shape: - [batch, model_settings['dct_coefficient_count'] * + [batch, model_settings['fingerprint_width'] * model_settings['spectrogram_length']] with the features corresponding to the same time slot arranged contiguously, and the oldest slot at index [:, 0], and newest at [:, -1]. @@ -440,7 +500,7 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] # Validation. @@ -462,8 +522,11 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, num_filters = rank * num_units # Create the runtime memory: [num_filters, batch, input_time_size] batch = 1 - memory = tf.Variable(tf.zeros([num_filters, batch, input_time_size]), - trainable=False, name='runtime-memory') + memory = tf.get_variable( + initializer=tf.zeros_initializer, + shape=[num_filters, batch, input_time_size], + trainable=False, + name='runtime-memory') # Determine the number of new frames in the input, such that we only operate # on those. For training we do not use the memory, and thus use all frames # provided in the input. @@ -483,8 +546,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, new_fingerprint_input = tf.expand_dims(new_fingerprint_input, 2) # Create the frequency filters. - weights_frequency = tf.Variable( - tf.truncated_normal([input_frequency_size, num_filters], stddev=0.01)) + weights_frequency = tf.get_variable( + name='weights_frequency', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[input_frequency_size, num_filters]) # Expand to add input channels dimensions. # weights_frequency: [input_frequency_size, 1, num_filters] weights_frequency = tf.expand_dims(weights_frequency, 1) @@ -506,8 +571,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, activations_time = new_memory # Create the time filters. - weights_time = tf.Variable( - tf.truncated_normal([num_filters, input_time_size], stddev=0.01)) + weights_time = tf.get_variable( + name='weights_time', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[num_filters, input_time_size]) # Apply the time filter on the outputs of the feature filters. # weights_time: [num_filters, input_time_size, 1] # outputs: [num_filters, batch, 1] @@ -524,7 +591,8 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, units_output = tf.transpose(units_output) # Appy bias. - bias = tf.Variable(tf.zeros([num_units])) + bias = tf.get_variable( + name='bias', initializer=tf.zeros_initializer, shape=[num_units]) first_bias = tf.nn.bias_add(units_output, bias) # Relu. @@ -536,31 +604,135 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, first_dropout = first_relu first_fc_output_channels = 256 - first_fc_weights = tf.Variable( - tf.truncated_normal([num_units, first_fc_output_channels], stddev=0.01)) - first_fc_bias = tf.Variable(tf.zeros([first_fc_output_channels])) + first_fc_weights = tf.get_variable( + name='first_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[num_units, first_fc_output_channels]) + first_fc_bias = tf.get_variable( + name='first_fc_bias', + initializer=tf.zeros_initializer, + shape=[first_fc_output_channels]) first_fc = tf.matmul(first_dropout, first_fc_weights) + first_fc_bias if is_training: second_fc_input = tf.nn.dropout(first_fc, dropout_prob) else: second_fc_input = first_fc second_fc_output_channels = 256 - second_fc_weights = tf.Variable( - tf.truncated_normal( - [first_fc_output_channels, second_fc_output_channels], stddev=0.01)) - second_fc_bias = tf.Variable(tf.zeros([second_fc_output_channels])) + second_fc_weights = tf.get_variable( + name='second_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_fc_output_channels, second_fc_output_channels]) + second_fc_bias = tf.get_variable( + name='second_fc_bias', + initializer=tf.zeros_initializer, + shape=[second_fc_output_channels]) second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias if is_training: final_fc_input = tf.nn.dropout(second_fc, dropout_prob) else: final_fc_input = second_fc label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_fc_output_channels, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal(stddev=0.01), + shape=[second_fc_output_channels, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob else: return final_fc + + +def create_tiny_conv_model(fingerprint_input, model_settings, is_training): + """Builds a convolutional model aimed at microcontrollers. + + Devices like DSPs and microcontrollers can have very small amounts of + memory and limited processing power. This model is designed to use less + than 20KB of working RAM, and fit within 32KB of read-only (flash) memory. + + Here's the layout of the graph: + + (fingerprint_input) + v + [Conv2D]<-(weights) + v + [BiasAdd]<-(bias) + v + [Relu] + v + [MatMul]<-(weights) + v + [BiasAdd]<-(bias) + v + + This doesn't produce particularly accurate results, but it's designed to be + used as the first stage of a pipeline, running on a low-energy piece of + hardware that can always be on, and then wake higher-power chips when a + possible utterance has been found, so that more accurate analysis can be done. + + During training, a dropout node is introduced after the relu, controlled by a + placeholder. + + Args: + fingerprint_input: TensorFlow node that will output audio feature vectors. + model_settings: Dictionary of information about the model. + is_training: Whether the model is going to be used for training. + + Returns: + TensorFlow node outputting logits results, and optionally a dropout + placeholder. + """ + if is_training: + dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') + input_frequency_size = model_settings['fingerprint_width'] + input_time_size = model_settings['spectrogram_length'] + fingerprint_4d = tf.reshape(fingerprint_input, + [-1, input_time_size, input_frequency_size, 1]) + first_filter_width = 8 + first_filter_height = 10 + first_filter_count = 8 + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) + first_conv_stride_x = 2 + first_conv_stride_y = 2 + first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, + [1, first_conv_stride_y, first_conv_stride_x, 1], + 'SAME') + first_bias + first_relu = tf.nn.relu(first_conv) + if is_training: + first_dropout = tf.nn.dropout(first_relu, dropout_prob) + else: + first_dropout = first_relu + first_dropout_shape = first_dropout.get_shape() + first_dropout_output_width = first_dropout_shape[2] + first_dropout_output_height = first_dropout_shape[1] + first_dropout_element_count = int( + first_dropout_output_width * first_dropout_output_height * + first_filter_count) + flattened_first_dropout = tf.reshape(first_dropout, + [-1, first_dropout_element_count]) + label_count = model_settings['label_count'] + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_dropout_element_count, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) + final_fc = ( + tf.matmul(flattened_first_dropout, final_fc_weights) + final_fc_bias) + if is_training: + return final_fc, dropout_prob + else: + return final_fc diff --git a/tensorflow/examples/speech_commands/models_test.py b/tensorflow/examples/speech_commands/models_test.py index 80c795367f..0c373967ed 100644 --- a/tensorflow/examples/speech_commands/models_test.py +++ b/tensorflow/examples/speech_commands/models_test.py @@ -26,12 +26,29 @@ from tensorflow.python.platform import test class ModelsTest(test.TestCase): + def _modelSettings(self): + return models.prepare_model_settings( + label_count=10, + sample_rate=16000, + clip_duration_ms=1000, + window_size_ms=20, + window_stride_ms=10, + feature_bin_count=40, + preprocess="mfcc") + def testPrepareModelSettings(self): self.assertIsNotNone( - models.prepare_model_settings(10, 16000, 1000, 20, 10, 40)) + models.prepare_model_settings( + label_count=10, + sample_rate=16000, + clip_duration_ms=1000, + window_size_ms=20, + window_stride_ms=10, + feature_bin_count=40, + preprocess="mfcc")) def testCreateModelConvTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model(fingerprint_input, @@ -42,7 +59,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelConvInference(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits = models.create_model(fingerprint_input, model_settings, "conv", @@ -51,7 +68,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name)) def testCreateModelLowLatencyConvTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model( @@ -62,7 +79,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelFullyConnectedTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model( @@ -73,7 +90,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelBadArchitecture(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session(): fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) with self.assertRaises(Exception) as e: @@ -81,6 +98,17 @@ class ModelsTest(test.TestCase): "bad_architecture", True) self.assertTrue("not recognized" in str(e.exception)) + def testCreateModelTinyConvTraining(self): + model_settings = self._modelSettings() + with self.test_session() as sess: + fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) + logits, dropout_prob = models.create_model( + fingerprint_input, model_settings, "tiny_conv", True) + self.assertIsNotNone(logits) + self.assertIsNotNone(dropout_prob) + self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name)) + self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/examples/speech_commands/recognize_commands.h b/tensorflow/examples/speech_commands/recognize_commands.h index 7f8041f9ed..a7cd194bec 100644 --- a/tensorflow/examples/speech_commands/recognize_commands.h +++ b/tensorflow/examples/speech_commands/recognize_commands.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ +#ifndef TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ +#define TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ #include <deque> #include <unordered_set> @@ -76,4 +76,4 @@ class RecognizeCommands { } // namespace tensorflow -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ +#endif // TENSORFLOW_EXAMPLES_SPEECH_COMMANDS_RECOGNIZE_COMMANDS_H_ diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py index f46d5e59b4..eca34f8812 100644 --- a/tensorflow/examples/speech_commands/train.py +++ b/tensorflow/examples/speech_commands/train.py @@ -98,12 +98,12 @@ def main(_): model_settings = models.prepare_model_settings( len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))), FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms, - FLAGS.window_stride_ms, FLAGS.dct_coefficient_count) + FLAGS.window_stride_ms, FLAGS.feature_bin_count, FLAGS.preprocess) audio_processor = input_data.AudioProcessor( - FLAGS.data_url, FLAGS.data_dir, FLAGS.silence_percentage, - FLAGS.unknown_percentage, + FLAGS.data_url, FLAGS.data_dir, + FLAGS.silence_percentage, FLAGS.unknown_percentage, FLAGS.wanted_words.split(','), FLAGS.validation_percentage, - FLAGS.testing_percentage, model_settings) + FLAGS.testing_percentage, model_settings, FLAGS.summaries_dir) fingerprint_size = model_settings['fingerprint_size'] label_count = model_settings['label_count'] time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000) @@ -122,8 +122,25 @@ def main(_): 'lists, but are %d and %d long instead' % (len(training_steps_list), len(learning_rates_list))) - fingerprint_input = tf.placeholder( + input_placeholder = tf.placeholder( tf.float32, [None, fingerprint_size], name='fingerprint_input') + if FLAGS.quantize: + # TODO(petewarden): These values have been derived from the observed ranges + # of spectrogram and MFCC inputs. If the preprocessing pipeline changes, + # they may need to be updated. + if FLAGS.preprocess == 'average': + fingerprint_min = 0.0 + fingerprint_max = 2048.0 + elif FLAGS.preprocess == 'mfcc': + fingerprint_min = -247.0 + fingerprint_max = 30.0 + else: + raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (FLAGS.preprocess)) + fingerprint_input = tf.fake_quant_with_min_max_args( + input_placeholder, fingerprint_min, fingerprint_max) + else: + fingerprint_input = input_placeholder logits, dropout_prob = models.create_model( fingerprint_input, @@ -133,7 +150,7 @@ def main(_): # Define loss and optimizer ground_truth_input = tf.placeholder( - tf.float32, [None, label_count], name='groundtruth_input') + tf.int64, [None], name='groundtruth_input') # Optionally we can add runtime checks to spot when NaNs or other symptoms of # numerical errors start occurring during training. @@ -144,29 +161,31 @@ def main(_): # Create the back propagation and training evaluation machinery in the graph. with tf.name_scope('cross_entropy'): - cross_entropy_mean = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits( - labels=ground_truth_input, logits=logits)) - tf.summary.scalar('cross_entropy', cross_entropy_mean) + cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy( + labels=ground_truth_input, logits=logits) + if FLAGS.quantize: + tf.contrib.quantize.create_training_graph(quant_delay=0) with tf.name_scope('train'), tf.control_dependencies(control_dependencies): learning_rate_input = tf.placeholder( tf.float32, [], name='learning_rate_input') train_step = tf.train.GradientDescentOptimizer( learning_rate_input).minimize(cross_entropy_mean) predicted_indices = tf.argmax(logits, 1) - expected_indices = tf.argmax(ground_truth_input, 1) - correct_prediction = tf.equal(predicted_indices, expected_indices) - confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices, num_classes=label_count) + correct_prediction = tf.equal(predicted_indices, ground_truth_input) + confusion_matrix = tf.confusion_matrix( + ground_truth_input, predicted_indices, num_classes=label_count) evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - tf.summary.scalar('accuracy', evaluation_step) + with tf.get_default_graph().name_scope('eval'): + tf.summary.scalar('cross_entropy', cross_entropy_mean) + tf.summary.scalar('accuracy', evaluation_step) - global_step = tf.contrib.framework.get_or_create_global_step() + global_step = tf.train.get_or_create_global_step() increment_global_step = tf.assign(global_step, global_step + 1) saver = tf.train.Saver(tf.global_variables()) # Merge all the summaries and write them out to /tmp/retrain_logs (by default) - merged_summaries = tf.summary.merge_all() + merged_summaries = tf.summary.merge_all(scope='eval') train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', sess.graph) validation_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/validation') @@ -208,8 +227,11 @@ def main(_): # Run the graph with this batch of training data. train_summary, train_accuracy, cross_entropy_value, _, _ = sess.run( [ - merged_summaries, evaluation_step, cross_entropy_mean, train_step, - increment_global_step + merged_summaries, + evaluation_step, + cross_entropy_mean, + train_step, + increment_global_step, ], feed_dict={ fingerprint_input: train_fingerprints, @@ -289,7 +311,7 @@ if __name__ == '__main__': '--data_url', type=str, # pylint: disable=line-too-long - default='http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz', + default='http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz', # pylint: enable=line-too-long help='Location of speech training data archive on the web.') parser.add_argument( @@ -358,17 +380,18 @@ if __name__ == '__main__': '--window_size_ms', type=float, default=30.0, - help='How long each spectrogram timeslice is',) + help='How long each spectrogram timeslice is.',) parser.add_argument( '--window_stride_ms', type=float, default=10.0, - help='How long each spectrogram timeslice is',) + help='How far to move in time between spectogram timeslices.',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--how_many_training_steps', type=str, @@ -424,6 +447,16 @@ if __name__ == '__main__': type=bool, default=False, help='Whether to check for invalid numbers during processing') + parser.add_argument( + '--quantize', + type=bool, + default=False, + help='Whether to train the model for eight-bit deployment') + parser.add_argument( + '--preprocess', + type=str, + default='mfcc', + help='Spectrogram processing mode. Can be "mfcc" or "average"') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/tutorials/deepdream/README.md b/tensorflow/examples/tutorials/deepdream/README.md index 3a715f6224..403e4b34f9 100644 --- a/tensorflow/examples/tutorials/deepdream/README.md +++ b/tensorflow/examples/tutorials/deepdream/README.md @@ -2,7 +2,7 @@ by [Alexander Mordvintsev](mailto:moralex@google.com) -This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network +This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow: - visualizing individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries) @@ -11,8 +11,8 @@ image generation techniques implemented with TensorFlow: - using Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost - generating DeepDream-like images with TensorFlow -You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes -embedded graph visualizations. You can still see them online +You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes +embedded graph visualizations. You can still see them online [using nbviewer](http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb) service. @@ -23,5 +23,5 @@ In order to run the notebook locally, the following dependencies must be install - NumPy - Jupyter Notebook -To open the notebook, run `ipython notebook` command in this directory, and +To open the notebook, run `ipython notebook` command in this directory, and select 'deepdream.ipynb' in the opened browser window. diff --git a/tensorflow/examples/tutorials/estimators/BUILD b/tensorflow/examples/tutorials/estimators/BUILD index ecbc1a431d..bab609f208 100644 --- a/tensorflow/examples/tutorials/estimators/BUILD +++ b/tensorflow/examples/tutorials/estimators/BUILD @@ -20,15 +20,3 @@ py_binary( "//third_party/py/numpy", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/image_retraining/__init__.py b/tensorflow/examples/tutorials/estimators/__init__.py index e69de29bb2..e69de29bb2 100644 --- a/tensorflow/examples/image_retraining/__init__.py +++ b/tensorflow/examples/tutorials/estimators/__init__.py diff --git a/tensorflow/examples/tutorials/input_fn/__init__.py b/tensorflow/examples/tutorials/input_fn/__init__.py new file mode 100644 index 0000000000..e69de29bb2 --- /dev/null +++ b/tensorflow/examples/tutorials/input_fn/__init__.py diff --git a/tensorflow/examples/tutorials/layers/BUILD b/tensorflow/examples/tutorials/layers/BUILD index f8a29c79c6..aad78b1840 100644 --- a/tensorflow/examples/tutorials/layers/BUILD +++ b/tensorflow/examples/tutorials/layers/BUILD @@ -19,15 +19,3 @@ py_binary( "//third_party/py/numpy", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/tutorials/layers/__init__.py b/tensorflow/examples/tutorials/layers/__init__.py new file mode 100644 index 0000000000..e69de29bb2 --- /dev/null +++ b/tensorflow/examples/tutorials/layers/__init__.py diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py index 2124843fcb..1e8d7d05e1 100644 --- a/tensorflow/examples/tutorials/layers/cnn_mnist.py +++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py @@ -97,9 +97,7 @@ def cnn_model_fn(features, labels, mode): return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) # Calculate Loss (for both TRAIN and EVAL modes) - onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10) - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) + loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) # Configure the Training Op (for TRAIN mode) if mode == tf.estimator.ModeKeys.TRAIN: diff --git a/tensorflow/examples/tutorials/mnist/BUILD b/tensorflow/examples/tutorials/mnist/BUILD index 6d4e67063d..d4070fdd1e 100644 --- a/tensorflow/examples/tutorials/mnist/BUILD +++ b/tensorflow/examples/tutorials/mnist/BUILD @@ -51,6 +51,7 @@ py_binary( "fully_connected_feed.py", ], srcs_version = "PY2AND3", + tags = ["optonly"], deps = [ ":input_data", ":mnist", @@ -96,7 +97,7 @@ py_binary( py_test( name = "fully_connected_feed_test", - size = "small", + size = "medium", srcs = [ "fully_connected_feed.py", ], @@ -132,15 +133,3 @@ py_test( "//tensorflow:tensorflow_py", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/tutorials/mnist/input_data.py b/tensorflow/examples/tutorials/mnist/input_data.py index f1a7e1c4af..fa148ae3e6 100644 --- a/tensorflow/examples/tutorials/mnist/input_data.py +++ b/tensorflow/examples/tutorials/mnist/input_data.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +# pylint: disable=unused-import import gzip import os import tempfile @@ -27,3 +28,4 @@ from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets +# pylint: enable=unused-import diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py index 3585043a2a..7cedd0e264 100644 --- a/tensorflow/examples/tutorials/mnist/mnist.py +++ b/tensorflow/examples/tutorials/mnist/mnist.py @@ -94,9 +94,7 @@ def loss(logits, labels): loss: Loss tensor of type float. """ labels = tf.to_int64(labels) - cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( - labels=labels, logits=logits, name='xentropy') - return tf.reduce_mean(cross_entropy, name='xentropy_mean') + return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) def training(loss, learning_rate): diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py index a4dbab5123..5d8d8d84fe 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_deep.py +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -34,6 +34,8 @@ from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf +import numpy + FLAGS = None @@ -125,27 +127,27 @@ def bias_variable(shape): def main(_): # Import data - mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + mnist = input_data.read_data_sets(FLAGS.data_dir) # Create the model x = tf.placeholder(tf.float32, [None, 784]) # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) + y_ = tf.placeholder(tf.int64, [None]) # Build the graph for the deep net y_conv, keep_prob = deepnn(x) with tf.name_scope('loss'): - cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, - logits=y_conv) + cross_entropy = tf.losses.sparse_softmax_cross_entropy( + labels=y_, logits=y_conv) cross_entropy = tf.reduce_mean(cross_entropy) with tf.name_scope('adam_optimizer'): train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) with tf.name_scope('accuracy'): - correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_) correct_prediction = tf.cast(correct_prediction, tf.float32) accuracy = tf.reduce_mean(correct_prediction) @@ -164,8 +166,15 @@ def main(_): print('step %d, training accuracy %g' % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) - print('test accuracy %g' % accuracy.eval(feed_dict={ - x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) + # compute in batches to avoid OOM on GPUs + accuracy_l = [] + for _ in range(20): + batch = mnist.test.next_batch(500, shuffle=False) + accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], + y_: batch[1], + keep_prob: 1.0})) + print('test accuracy %g' % numpy.mean(accuracy_l)) + if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py index addd2d3810..47dd6a1947 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py +++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """A very simple MNIST classifier. See extensive documentation at @@ -34,7 +33,7 @@ FLAGS = None def main(_): # Import data - mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + mnist = input_data.read_data_sets(FLAGS.data_dir) # Create the model x = tf.placeholder(tf.float32, [None, 784]) @@ -43,7 +42,7 @@ def main(_): y = tf.matmul(x, W) + b # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) + y_ = tf.placeholder(tf.int64, [None]) # The raw formulation of cross-entropy, # @@ -52,10 +51,9 @@ def main(_): # # can be numerically unstable. # - # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw # outputs of 'y', and then average across the batch. - cross_entropy = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() @@ -66,14 +64,21 @@ def main(_): sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # Test trained model - correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) + correct_prediction = tf.equal(tf.argmax(y, 1), y_) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print(sess.run(accuracy, feed_dict={x: mnist.test.images, - y_: mnist.test.labels})) + print(sess.run( + accuracy, feed_dict={ + x: mnist.test.images, + y_: mnist.test.labels + })) + if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') + parser.add_argument( + '--data_dir', + type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py b/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py index eaff05913a..e89317494f 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py +++ b/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py @@ -32,7 +32,7 @@ FLAGS = None def main(_): # Import data - mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + mnist = input_data.read_data_sets(FLAGS.data_dir) # Create the model x = tf.placeholder(tf.float32, [None, 784]) @@ -41,7 +41,7 @@ def main(_): y = tf.matmul(x, w) + b # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) + y_ = tf.placeholder(tf.int64, [None]) # The raw formulation of cross-entropy, # @@ -50,10 +50,9 @@ def main(_): # # can be numerically unstable. # - # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw - # outputs of 'y', and then average across the batch. - cross_entropy = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw + # logit outputs of 'y', and then average across the batch. + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) config = tf.ConfigProto() @@ -86,7 +85,7 @@ def main(_): sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # Test trained model - correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) + correct_prediction = tf.equal(tf.argmax(y, 1), y_) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py index c401d09df8..7967e22d6a 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py +++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py @@ -38,7 +38,6 @@ FLAGS = None def train(): # Import data mnist = input_data.read_data_sets(FLAGS.data_dir, - one_hot=True, fake_data=FLAGS.fake_data) sess = tf.InteractiveSession() @@ -47,7 +46,7 @@ def train(): # Input placeholders with tf.name_scope('input'): x = tf.placeholder(tf.float32, [None, 784], name='x-input') - y_ = tf.placeholder(tf.float32, [None, 10], name='y-input') + y_ = tf.placeholder(tf.int64, [None], name='y-input') with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) @@ -117,12 +116,12 @@ def train(): # # can be numerically unstable. # - # So here we use tf.nn.softmax_cross_entropy_with_logits on the - # raw outputs of the nn_layer above, and then average across + # So here we use tf.losses.sparse_softmax_cross_entropy on the + # raw logit outputs of the nn_layer above, and then average across # the batch. - diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y) with tf.name_scope('total'): - cross_entropy = tf.reduce_mean(diff) + cross_entropy = tf.losses.sparse_softmax_cross_entropy( + labels=y_, logits=y) tf.summary.scalar('cross_entropy', cross_entropy) with tf.name_scope('train'): @@ -131,7 +130,7 @@ def train(): with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): - correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) + correct_prediction = tf.equal(tf.argmax(y, 1), y_) with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar('accuracy', accuracy) diff --git a/tensorflow/examples/tutorials/monitors/BUILD b/tensorflow/examples/tutorials/monitors/BUILD index 4220e8144d..1c49e3fe53 100644 --- a/tensorflow/examples/tutorials/monitors/BUILD +++ b/tensorflow/examples/tutorials/monitors/BUILD @@ -23,15 +23,3 @@ py_binary( "//third_party/py/numpy", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) diff --git a/tensorflow/examples/tutorials/monitors/__init__.py b/tensorflow/examples/tutorials/monitors/__init__.py new file mode 100644 index 0000000000..e69de29bb2 --- /dev/null +++ b/tensorflow/examples/tutorials/monitors/__init__.py diff --git a/tensorflow/examples/tutorials/monitors/iris_monitors.py b/tensorflow/examples/tutorials/monitors/iris_monitors.py index 850d105f7b..a2b7fe6023 100644 --- a/tensorflow/examples/tutorials/monitors/iris_monitors.py +++ b/tensorflow/examples/tutorials/monitors/iris_monitors.py @@ -32,9 +32,9 @@ IRIS_TEST = os.path.join(os.path.dirname(__file__), "iris_test.csv") def main(unused_argv): # Load datasets. training_set = tf.contrib.learn.datasets.base.load_csv_with_header( - filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float) + filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32) test_set = tf.contrib.learn.datasets.base.load_csv_with_header( - filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float) + filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32) validation_metrics = { "accuracy": @@ -83,7 +83,7 @@ def main(unused_argv): # Classify two new flower samples. new_samples = np.array( - [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) + [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=np.float32) y = list(classifier.predict(new_samples)) print("Predictions: {}".format(str(y))) diff --git a/tensorflow/examples/tutorials/word2vec/BUILD b/tensorflow/examples/tutorials/word2vec/BUILD index 42d6355b4f..2e19c038bd 100644 --- a/tensorflow/examples/tutorials/word2vec/BUILD +++ b/tensorflow/examples/tutorials/word2vec/BUILD @@ -13,19 +13,11 @@ py_binary( "word2vec_basic.py", ], srcs_version = "PY2AND3", + tags = [ + "no-internal-py3", + ], deps = [ "//tensorflow:tensorflow_py", "//third_party/py/numpy", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), -) diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index 142e45a2e8..b09ee99768 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -21,6 +21,8 @@ from __future__ import print_function import collections import math import os +import sys +import argparse import random from tempfile import gettempdir import zipfile @@ -30,6 +32,24 @@ from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf +from tensorflow.contrib.tensorboard.plugins import projector + +# Give a folder path as an argument with '--log_dir' to save +# TensorBoard summaries. Default is a log folder in current directory. +current_path = os.path.dirname(os.path.realpath(sys.argv[0])) + +parser = argparse.ArgumentParser() +parser.add_argument( + '--log_dir', + type=str, + default=os.path.join(current_path, 'log'), + help='The log directory for TensorBoard summaries.') +FLAGS, unparsed = parser.parse_known_args() + +# Create the directory for TensorBoard variables if there is not. +if not os.path.exists(FLAGS.log_dir): + os.makedirs(FLAGS.log_dir) + # Step 1: Download the data. url = 'http://mattmahoney.net/dc/' @@ -61,6 +81,7 @@ def read_data(filename): data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data + vocabulary = read_data(filename) print('Data size', len(vocabulary)) @@ -86,20 +107,22 @@ def build_dataset(words, n_words): reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reversed_dictionary + # Filling 4 global variables: # data - list of codes (integers from 0 to vocabulary_size-1). # This is the original text but words are replaced by their codes # count - map of words(strings) to count of occurrences # dictionary - map of words(strings) to their codes(integers) # reverse_dictionary - maps codes(integers) to words(strings) -data, count, dictionary, reverse_dictionary = build_dataset(vocabulary, - vocabulary_size) +data, count, dictionary, reverse_dictionary = build_dataset( + vocabulary, vocabulary_size) del vocabulary # Hint to reduce memory. print('Most common words (+UNK)', count[:5]) print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]]) data_index = 0 + # Step 3: Function to generate a training batch for the skip-gram model. def generate_batch(batch_size, num_skips, skip_window): global data_index @@ -108,7 +131,7 @@ def generate_batch(batch_size, num_skips, skip_window): batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] - buffer = collections.deque(maxlen=span) + buffer = collections.deque(maxlen=span) # pylint: disable=redefined-builtin if data_index + span > len(data): data_index = 0 buffer.extend(data[data_index:data_index + span]) @@ -120,7 +143,7 @@ def generate_batch(batch_size, num_skips, skip_window): batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[context_word] if data_index == len(data): - buffer[:] = data[:span] + buffer.extend(data[0:span]) data_index = span else: buffer.append(data[data_index]) @@ -129,96 +152,130 @@ def generate_batch(batch_size, num_skips, skip_window): data_index = (data_index + len(data) - span) % len(data) return batch, labels + batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) for i in range(8): - print(batch[i], reverse_dictionary[batch[i]], - '->', labels[i, 0], reverse_dictionary[labels[i, 0]]) + print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], + reverse_dictionary[labels[i, 0]]) # Step 4: Build and train a skip-gram model. batch_size = 128 embedding_size = 128 # Dimension of the embedding vector. -skip_window = 1 # How many words to consider left and right. -num_skips = 2 # How many times to reuse an input to generate a label. -num_sampled = 64 # Number of negative examples to sample. +skip_window = 1 # How many words to consider left and right. +num_skips = 2 # How many times to reuse an input to generate a label. +num_sampled = 64 # Number of negative examples to sample. # We pick a random validation set to sample nearest neighbors. Here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. These 3 variables are used only for # displaying model accuracy, they don't affect calculation. -valid_size = 16 # Random set of words to evaluate similarity on. +valid_size = 16 # Random set of words to evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of the distribution. valid_examples = np.random.choice(valid_window, valid_size, replace=False) - graph = tf.Graph() with graph.as_default(): # Input data. - train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) - train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) - valid_dataset = tf.constant(valid_examples, dtype=tf.int32) + with tf.name_scope('inputs'): + train_inputs = tf.placeholder(tf.int32, shape=[batch_size]) + train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) + valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Ops and variables pinned to the CPU because of missing GPU implementation with tf.device('/cpu:0'): # Look up embeddings for inputs. - embeddings = tf.Variable( - tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) - embed = tf.nn.embedding_lookup(embeddings, train_inputs) + with tf.name_scope('embeddings'): + embeddings = tf.Variable( + tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) + embed = tf.nn.embedding_lookup(embeddings, train_inputs) # Construct the variables for the NCE loss - nce_weights = tf.Variable( - tf.truncated_normal([vocabulary_size, embedding_size], - stddev=1.0 / math.sqrt(embedding_size))) - nce_biases = tf.Variable(tf.zeros([vocabulary_size])) + with tf.name_scope('weights'): + nce_weights = tf.Variable( + tf.truncated_normal( + [vocabulary_size, embedding_size], + stddev=1.0 / math.sqrt(embedding_size))) + with tf.name_scope('biases'): + nce_biases = tf.Variable(tf.zeros([vocabulary_size])) # Compute the average NCE loss for the batch. # tf.nce_loss automatically draws a new sample of the negative labels each # time we evaluate the loss. # Explanation of the meaning of NCE loss: # http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ - loss = tf.reduce_mean( - tf.nn.nce_loss(weights=nce_weights, - biases=nce_biases, - labels=train_labels, - inputs=embed, - num_sampled=num_sampled, - num_classes=vocabulary_size)) + with tf.name_scope('loss'): + loss = tf.reduce_mean( + tf.nn.nce_loss( + weights=nce_weights, + biases=nce_biases, + labels=train_labels, + inputs=embed, + num_sampled=num_sampled, + num_classes=vocabulary_size)) + + # Add the loss value as a scalar to summary. + tf.summary.scalar('loss', loss) # Construct the SGD optimizer using a learning rate of 1.0. - optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) + with tf.name_scope('optimizer'): + optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) # Compute the cosine similarity between minibatch examples and all embeddings. - norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) + norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True)) normalized_embeddings = embeddings / norm - valid_embeddings = tf.nn.embedding_lookup( - normalized_embeddings, valid_dataset) + valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, + valid_dataset) similarity = tf.matmul( valid_embeddings, normalized_embeddings, transpose_b=True) + # Merge all summaries. + merged = tf.summary.merge_all() + # Add variable initializer. init = tf.global_variables_initializer() + # Create a saver. + saver = tf.train.Saver() + # Step 5: Begin training. num_steps = 100001 with tf.Session(graph=graph) as session: + # Open a writer to write summaries. + writer = tf.summary.FileWriter(FLAGS.log_dir, session.graph) + # We must initialize all variables before we use them. init.run() print('Initialized') average_loss = 0 for step in xrange(num_steps): - batch_inputs, batch_labels = generate_batch( - batch_size, num_skips, skip_window) + batch_inputs, batch_labels = generate_batch(batch_size, num_skips, + skip_window) feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels} + # Define metadata variable. + run_metadata = tf.RunMetadata() + # We perform one update step by evaluating the optimizer op (including it # in the list of returned values for session.run() - _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict) + # Also, evaluate the merged op to get all summaries from the returned "summary" variable. + # Feed metadata variable to session for visualizing the graph in TensorBoard. + _, summary, loss_val = session.run( + [optimizer, merged, loss], + feed_dict=feed_dict, + run_metadata=run_metadata) average_loss += loss_val + # Add returned summaries to writer in each step. + writer.add_summary(summary, step) + # Add metadata to visualize the graph for the last run. + if step == (num_steps - 1): + writer.add_run_metadata(run_metadata, 'step%d' % step) + if step % 2000 == 0: if step > 0: average_loss /= 2000 @@ -240,6 +297,23 @@ with tf.Session(graph=graph) as session: print(log_str) final_embeddings = normalized_embeddings.eval() + # Write corresponding labels for the embeddings. + with open(FLAGS.log_dir + '/metadata.tsv', 'w') as f: + for i in xrange(vocabulary_size): + f.write(reverse_dictionary[i] + '\n') + + # Save the model for checkpoints. + saver.save(session, os.path.join(FLAGS.log_dir, 'model.ckpt')) + + # Create a configuration for visualizing embeddings with the labels in TensorBoard. + config = projector.ProjectorConfig() + embedding_conf = config.embeddings.add() + embedding_conf.tensor_name = embeddings.name + embedding_conf.metadata_path = os.path.join(FLAGS.log_dir, 'metadata.tsv') + projector.visualize_embeddings(writer, config) + +writer.close() + # Step 6: Visualize the embeddings. @@ -251,21 +325,24 @@ def plot_with_labels(low_dim_embs, labels, filename): for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) - plt.annotate(label, - xy=(x, y), - xytext=(5, 2), - textcoords='offset points', - ha='right', - va='bottom') + plt.annotate( + label, + xy=(x, y), + xytext=(5, 2), + textcoords='offset points', + ha='right', + va='bottom') plt.savefig(filename) + try: # pylint: disable=g-import-not-at-top from sklearn.manifold import TSNE import matplotlib.pyplot as plt - tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact') + tsne = TSNE( + perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact') plot_only = 500 low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in xrange(plot_only)] diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb index 18c456cad7..3b43d1fb55 100644 --- a/tensorflow/examples/udacity/5_word2vec.ipynb +++ b/tensorflow/examples/udacity/5_word2vec.ipynb @@ -455,7 +455,7 @@ " \n", " # Compute the similarity between minibatch examples and all embeddings.\n", " # We use the cosine distance:\n", - " norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n", + " norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n", " normalized_embeddings = embeddings / norm\n", " valid_embeddings = tf.nn.embedding_lookup(\n", " normalized_embeddings, valid_dataset)\n", diff --git a/tensorflow/examples/udacity/Dockerfile b/tensorflow/examples/udacity/Dockerfile index 3ca58566c1..00eb853e52 100644 --- a/tensorflow/examples/udacity/Dockerfile +++ b/tensorflow/examples/udacity/Dockerfile @@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ apt-get clean && \ rm -rf /var/lib/apt/lists/* -RUN pip install scikit-learn pyreadline Pillow +RUN pip install scikit-learn pyreadline Pillow imageio RUN rm -rf /notebooks/* ADD *.ipynb /notebooks/ WORKDIR /notebooks diff --git a/tensorflow/examples/udacity/README.md b/tensorflow/examples/udacity/README.md index 6faad294c2..f80c56d1c1 100644 --- a/tensorflow/examples/udacity/README.md +++ b/tensorflow/examples/udacity/README.md @@ -43,15 +43,15 @@ In addition, you may need to pass `--memory=8g` as an extra argument to `docker-machine` is a tool to provision and manage docker hosts, it supports multiple platform (ex. aws, gce, azure, virtualbox, ...). To create a new virtual machine locally with built-in docker engine, you can use docker-machine create -d virtualbox --virtualbox-memory 8196 tensorflow - + `-d` means the driver for the cloud platform, supported drivers listed [here](https://docs.docker.com/machine/drivers/). Here we use virtualbox to create a new virtual machine locally. `tensorflow` means the name of the virtual machine, feel free to use whatever you like. You can use docker-machine ip tensorflow - + to get the ip of the new virtual machine. To switch from default virtual machine to a new one (here we use tensorflow), type eval $(docker-machine env tensorflow) - + Note that `docker-machine env tensorflow` outputs some environment variables such like `DOCKER_HOST`. Then your docker client is now connected to the docker host in virtual machine `tensorflow` * **I'm getting a TLS connection error.** diff --git a/tensorflow/examples/wav_to_spectrogram/BUILD b/tensorflow/examples/wav_to_spectrogram/BUILD index c99870c686..cc8835728d 100644 --- a/tensorflow/examples/wav_to_spectrogram/BUILD +++ b/tensorflow/examples/wav_to_spectrogram/BUILD @@ -49,17 +49,3 @@ tf_cc_test( "//tensorflow/core:test_main", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) 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)); diff --git a/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.h b/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.h index fa8cb0abe9..eada07e06f 100644 --- a/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.h +++ b/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef THIRD_PARTY_TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ -#define THIRD_PARTY_TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ +#ifndef TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ +#define TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/types.h" @@ -28,4 +28,4 @@ tensorflow::Status WavToSpectrogram(const tensorflow::string& input_wav, tensorflow::int32 stride, float brightness, const tensorflow::string& output_image); -#endif // THIRD_PARTY_TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ +#endif // TENSORFLOW_EXAMPLES_WAV_TO_SPECTROGRAM_WAV_TO_SPECTROGRAM_H_ |