Update build commands

Tested:
bazel build -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a //tensorflow/lite/java:tensorflow-lite

With NDK r17c

PiperOrigin-RevId: 295248025
Change-Id: Ice7895ffb5ef53f5885532fdf66ae6ee9d012892

tensorflow/lite/c/README.md

@@ -30,7 +30,7 @@ provided. Assuming a working [bazel](https://bazel.build/versions/master/docs/in
 configuration, this can be built as follows:
 
 ```sh
-bazel build -c opt --cxxopt=--std=c++11 //tensorflow/lite/c:tensorflowlite_c
+bazel build -c opt //tensorflow/lite/c:tensorflowlite_c
 ```
 
 and for Android (replace `android_arm` with `android_arm64` for 64-bit),

tensorflow/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md

@@ -9,14 +9,13 @@ Plugin, and placed in Assets/TensorFlowLite/SDK/Plugins/. For the editor (note
 that the generated shared library name and suffix are platform-dependent):
 
 ```sh
-bazel build -c opt --cxxopt=--std=c++11 //tensorflow/lite/c:tensorflowlite_c
+bazel build -c opt //tensorflow/lite/c:tensorflowlite_c
 ```
 
 and for Android (replace `android_arm` with `android_arm64` for 64-bit):
 
 ```sh
-bazel build -c opt --cxxopt=--std=c++11 --config=android_arm \
-  //tensorflow/lite/c:tensorflowlite_c
+bazel build -c opt --config=android_arm //tensorflow/lite/c:tensorflowlite_c
 ```
 
 If you encounter issues with native plugin discovery on Mac ("Darwin")

tensorflow/lite/g3doc/guide/android.md

@@ -144,8 +144,7 @@ Once Bazel is properly configured, you can build the TensorFlow Lite AAR from
 the root checkout directory as follows:
 
 ```sh
-bazel build --cxxopt='-std=c++11' -c opt         \
-  --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
+bazel build -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
   //tensorflow/lite/java:tensorflow-lite
 ```
 

tensorflow/lite/g3doc/guide/ops_select.md

@@ -148,9 +148,9 @@ AAR to your local Maven repository:
 
 ```sh
 mvn install:install-file \
-  -Dfile=bazel-bin/tensorflow/lite/java/tensorflow-lite-with-select-tf-ops.aar \
+  -Dfile=bazel-bin/tensorflow/lite/java/tensorflow-lite-select-tf-ops.aar \
   -DgroupId=org.tensorflow \
-  -DartifactId=tensorflow-lite-with-select-tf-ops -Dversion=0.1.100 -Dpackaging=aar
+  -DartifactId=tensorflow-lite-select-tf-ops -Dversion=0.1.100 -Dpackaging=aar
 ```
 
 Finally, in your app's `build.gradle`, ensure you have the `mavenLocal()`

tensorflow/lite/java/BUILD

@@ -20,7 +20,7 @@ JAVA_SRCS = glob([
 
 # Building tensorflow-lite.aar including 4 variants of .so
 # To build an aar for release, run below command:
-# bazel build --cxxopt='-std=c++11' -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
+# bazel build -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
 #   tensorflow/lite/java:tensorflow-lite
 aar_with_jni(
     name = "tensorflow-lite",

tensorflow/lite/java/build_aar_for_release.sh

@@ -26,7 +26,7 @@ BASEDIR=tensorflow/lite
 CROSSTOOL="//external:android/crosstool"
 HOST_CROSSTOOL="@bazel_tools//tools/cpp:toolchain"
 
-BUILD_OPTS="--cxxopt=--std=c++11 -c opt"
+BUILD_OPTS="-c opt"
 CROSSTOOL_OPTS="--crosstool_top=$CROSSTOOL --host_crosstool_top=$HOST_CROSSTOOL"
 
 test -d $BASEDIR || (echo "Aborting: not at top-level build directory"; exit 1)

tensorflow/lite/java/demo/README.md

@@ -41,8 +41,7 @@ code to merge.
 2. Build the app with Bazel. The demo needs C++11:
 
   ```shell
-  bazel build -c opt --cxxopt='--std=c++11' \
-    //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo
+  bazel build -c opt //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo
   ```
 
 3. Install the demo on a

tensorflow/lite/java/ovic/README.md

@@ -37,9 +37,9 @@ unzip -j /tmp/ovic.zip -d tensorflow/lite/java/ovic/src/testdata/
 You can run test with Bazel as below. This helps to ensure that the installation is correct.
 
 ```sh
-bazel test --cxxopt=--std=c++11 //tensorflow/lite/java/ovic:OvicClassifierTest --cxxopt=-Wno-all --test_output=all
+bazel test //tensorflow/lite/java/ovic:OvicClassifierTest --cxxopt=-Wno-all --test_output=all
 
-bazel test --cxxopt=--std=c++11 //tensorflow/lite/java/ovic:OvicDetectorTest --cxxopt=-Wno-all --test_output=all
+bazel test //tensorflow/lite/java/ovic:OvicDetectorTest --cxxopt=-Wno-all --test_output=all
 ```
 
 ### Test your submissions
@@ -51,7 +51,7 @@ Once you have a submission that follows the instructions from the [competition s
 You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output for classification should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at `/path/to/my_model.lite`, then call:
 
 ```sh
-bazel build --cxxopt=--std=c++11 //tensorflow/lite/java/ovic:ovic_validator --cxxopt=-Wno-all
+bazel build //tensorflow/lite/java/ovic:ovic_validator --cxxopt=-Wno-all
 bazel-bin/tensorflow/lite/java/ovic/ovic_validator /path/to/my_model.lite classify
 ```
 
@@ -160,7 +160,7 @@ Note: You'll need ROOT access to the phone to change processor affinity.
 * Build and install the app.
 
 ```
-bazel build -c opt --cxxopt=--std=c++11 --cxxopt=-Wno-all //tensorflow/lite/java/ovic/demo/app:ovic_benchmarker_binary
+bazel build -c opt --cxxopt=-Wno-all //tensorflow/lite/java/ovic/demo/app:ovic_benchmarker_binary
 adb install -r bazel-bin/tensorflow/lite/java/ovic/demo/app/ovic_benchmarker_binary.apk
 ```
 

tensorflow/lite/tools/accuracy/ilsvrc/README.md

@@ -159,7 +159,6 @@ adb shell /data/local/tmp/imagenet_accuracy_eval \
 
 ```
 bazel run -c opt \
-  --cxxopt='--std=c++11' \
   -- \
   //tensorflow/lite/tools/accuracy/ilsvrc:imagenet_accuracy_eval \
   --model_file=mobilenet_quant_v1_224.tflite \

tensorflow/lite/tools/benchmark/android/README.md

@@ -28,7 +28,6 @@ to edit the `WORKSPACE` to configure the android NDK/SDK.
 ```
 bazel build -c opt \
   --config=android_arm64 \
-  --cxxopt='--std=c++11' \
   tensorflow/lite/tools/benchmark/android:benchmark_model
 ```
 

tensorflow/lite/tools/evaluation/tasks/coco_object_detection/README.md

@@ -256,7 +256,6 @@ Optionally, you could also pass in the `--num_interpreter_threads` &
 
 ```
 bazel run -c opt \
-  --cxxopt='--std=c++11' \
   -- \
   //tensorflow/lite/tools/evaluation/tasks/coco_object_detection:run_eval \
   --model_file=/path/to/ssd_mobilenet_v1_float.tflite \

tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/README.md

@@ -204,7 +204,6 @@ adb shell /data/local/tmp/run_eval \
 
 ```
 bazel run -c opt \
-  --cxxopt='--std=c++11' \
   -- \
   //tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification:run_eval \
   --model_file=mobilenet_quant_v1_224.tflite \

tensorflow/lite/tools/make/Makefile

@@ -59,7 +59,6 @@ LIBS := \
 CXXFLAGS := -O3 -DNDEBUG -fPIC
 CXXFLAGS += $(EXTRA_CXXFLAGS)
 CFLAGS := ${CXXFLAGS}
-CXXFLAGS += --std=c++11
 LDOPTS := -L/usr/local/lib
 ARFLAGS := -r
 TARGET_TOOLCHAIN_PREFIX :=

tensorflow/lite/tools/pip_package/setup.py

@@ -139,7 +139,6 @@ ext = Extension(
                '-I%s' % TENSORFLOW_DIR,
                '-module', 'interpreter_wrapper',
                '-outdir', PACKAGE_NAME],
-    extra_compile_args=['-std=c++11'],
     include_dirs=[TENSORFLOW_DIR,
                   os.path.join(TENSORFLOW_DIR, 'tensorflow', 'lite', 'tools',
                                'pip_package'),