Merge branch 'google:master' into gesture-recognizer-python

WORKSPACE

@@ -172,6 +172,10 @@ http_archive(
     urls = [
         "https://github.com/google/sentencepiece/archive/1.0.0.zip",
     ],
+    patches = [
+        "//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff",
+    ],
+    patch_args = ["-p1"],
     repo_mapping = {"@com_google_glog" : "@com_github_glog_glog"},
 )
 

docs/BUILD

@@ -0,0 +1,14 @@
+# Placeholder for internal Python strict binary compatibility macro.
+
+py_binary(
+    name = "build_py_api_docs",
+    srcs = ["build_py_api_docs.py"],
+    deps = [
+        "//mediapipe",
+        "//third_party/py/absl:app",
+        "//third_party/py/absl/flags",
+        "//third_party/py/tensorflow_docs",
+        "//third_party/py/tensorflow_docs/api_generator:generate_lib",
+        "//third_party/py/tensorflow_docs/api_generator:public_api",
+    ],
+)

docs/build_py_api_docs.py

@@ -0,0 +1,85 @@
+# Copyright 2022 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"""MediaPipe reference docs generation script.
+
+This script generates API reference docs for the `mediapipe` PIP package.
+
+$> pip install -U git+https://github.com/tensorflow/docs mediapipe
+$> python build_py_api_docs.py
+"""
+
+import os
+
+from absl import app
+from absl import flags
+
+from tensorflow_docs.api_generator import generate_lib
+from tensorflow_docs.api_generator import public_api
+
+try:
+  # mediapipe has not been set up to work with bazel yet, so catch & report.
+  import mediapipe  # pytype: disable=import-error
+except ImportError as e:
+  raise ImportError('Please `pip install mediapipe`.') from e
+
+
+PROJECT_SHORT_NAME = 'mp'
+PROJECT_FULL_NAME = 'MediaPipe'
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    'output_dir',
+    default='/tmp/generated_docs',
+    help='Where to write the resulting docs.')
+
+_URL_PREFIX = flags.DEFINE_string(
+    'code_url_prefix',
+    'https://github.com/google/mediapipe/tree/master/mediapipe',
+    'The url prefix for links to code.')
+
+_SEARCH_HINTS = flags.DEFINE_bool(
+    'search_hints', True,
+    'Include metadata search hints in the generated files')
+
+_SITE_PATH = flags.DEFINE_string('site_path', '/mediapipe/api_docs/python',
+                                 'Path prefix in the _toc.yaml')
+
+
+def gen_api_docs():
+  """Generates API docs for the mediapipe package."""
+
+  doc_generator = generate_lib.DocGenerator(
+      root_title=PROJECT_FULL_NAME,
+      py_modules=[(PROJECT_SHORT_NAME, mediapipe)],
+      base_dir=os.path.dirname(mediapipe.__file__),
+      code_url_prefix=_URL_PREFIX.value,
+      search_hints=_SEARCH_HINTS.value,
+      site_path=_SITE_PATH.value,
+      # This callback ensures that docs are only generated for objects that
+      # are explicitly imported in your __init__.py files. There are other
+      # options but this is a good starting point.
+      callbacks=[public_api.explicit_package_contents_filter],
+  )
+
+  doc_generator.build(_OUTPUT_DIR.value)
+
+  print('Docs output to:', _OUTPUT_DIR.value)
+
+
+def main(_):
+  gen_api_docs()
+
+
+if __name__ == '__main__':
+  app.run(main)

mediapipe/framework/api2/builder.h

@@ -289,8 +289,15 @@ class NodeBase {
 
   template <typename T>
   T& GetOptions() {
+    return GetOptions(T::ext);
+  }
+
+  // Use this API when the proto extension does not follow the "ext" naming
+  // convention.
+  template <typename E>
+  auto& GetOptions(const E& extension) {
     options_used_ = true;
-    return *options_.MutableExtension(T::ext);
+    return *options_.MutableExtension(extension);
   }
 
  protected:
@@ -386,8 +393,15 @@ class PacketGenerator {
 
   template <typename T>
   T& GetOptions() {
+    return GetOptions(T::ext);
+  }
+
+  // Use this API when the proto extension does not follow the "ext" naming
+  // convention.
+  template <typename E>
+  auto& GetOptions(const E& extension) {
     options_used_ = true;
-    return *options_.MutableExtension(T::ext);
+    return *options_.MutableExtension(extension);
   }
 
   template <typename B, typename T, bool kIsOptional, bool kIsMultiple>

mediapipe/modules/face_geometry/libs/effect_renderer.cc

@@ -161,7 +161,7 @@ class Texture {
 
   ~Texture() {
     if (is_owned_) {
-      glDeleteProgram(handle_);
+      glDeleteTextures(1, &handle_);
     }
   }
 

mediapipe/tasks/cc/components/BUILD

@@ -49,6 +49,7 @@ cc_library(
         "//mediapipe/gpu:gpu_origin_cc_proto",
         "//mediapipe/tasks/cc:common",
         "//mediapipe/tasks/cc/core:model_resources",
+        "//mediapipe/tasks/cc/core/proto:acceleration_cc_proto",
         "//mediapipe/tasks/cc/vision/utils:image_tensor_specs",
         "@com_google_absl//absl/status",
         "@com_google_absl//absl/status:statusor",

mediapipe/tasks/cc/components/image_preprocessing.cc

@@ -34,6 +34,7 @@ limitations under the License.
 #include "mediapipe/tasks/cc/common.h"
 #include "mediapipe/tasks/cc/components/image_preprocessing_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
+#include "mediapipe/tasks/cc/core/proto/acceleration.pb.h"
 #include "mediapipe/tasks/cc/vision/utils/image_tensor_specs.h"
 #include "tensorflow/lite/schema/schema_generated.h"
 
@@ -129,7 +130,7 @@ absl::Status ConfigureImageToTensorCalculator(
     options->mutable_output_tensor_float_range()->set_max((255.0f - mean) /
                                                           std);
   }
-  // TODO: need to.support different GPU origin on differnt
+  // TODO: need to support different GPU origin on differnt
   // platforms or applications.
   options->set_gpu_origin(mediapipe::GpuOrigin::TOP_LEFT);
   return absl::OkStatus();
@@ -137,7 +138,13 @@ absl::Status ConfigureImageToTensorCalculator(
 
 }  // namespace
 
+bool DetermineImagePreprocessingGpuBackend(
+    const core::proto::Acceleration& acceleration) {
+  return acceleration.has_gpu();
+}
+
 absl::Status ConfigureImagePreprocessing(const ModelResources& model_resources,
+                                         bool use_gpu,
                                          ImagePreprocessingOptions* options) {
   ASSIGN_OR_RETURN(auto image_tensor_specs,
                    BuildImageTensorSpecs(model_resources));
@@ -145,7 +152,9 @@ absl::Status ConfigureImagePreprocessing(const ModelResources& model_resources,
       image_tensor_specs, options->mutable_image_to_tensor_options()));
   // The GPU backend isn't able to process int data. If the input tensor is
   // quantized, forces the image preprocessing graph to use CPU backend.
-  if (image_tensor_specs.tensor_type == tflite::TensorType_UINT8) {
+  if (use_gpu && image_tensor_specs.tensor_type != tflite::TensorType_UINT8) {
+    options->set_backend(ImagePreprocessingOptions::GPU_BACKEND);
+  } else {
     options->set_backend(ImagePreprocessingOptions::CPU_BACKEND);
   }
   return absl::OkStatus();

mediapipe/tasks/cc/components/image_preprocessing.h

@@ -19,20 +19,26 @@ limitations under the License.
 #include "absl/status/status.h"
 #include "mediapipe/tasks/cc/components/image_preprocessing_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
+#include "mediapipe/tasks/cc/core/proto/acceleration.pb.h"
 
 namespace mediapipe {
 namespace tasks {
 namespace components {
 
-// Configures an ImagePreprocessing subgraph using the provided model resources.
+// Configures an ImagePreprocessing subgraph using the provided model resources
+// When use_gpu is true, use GPU as backend to convert image to tensor.
 // - Accepts CPU input images and outputs CPU tensors.
 //
 // Example usage:
 //
 //   auto& preprocessing =
 //       graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+//   core::proto::Acceleration acceleration;
+//   acceleration.mutable_xnnpack();
+//   bool use_gpu = DetermineImagePreprocessingGpuBackend(acceleration);
 //   MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
 //       model_resources,
+//       use_gpu,
 //       &preprocessing.GetOptions<ImagePreprocessingOptions>()));
 //
 // The resulting ImagePreprocessing subgraph has the following I/O:
@@ -56,9 +62,14 @@ namespace components {
 //     The image that has the pixel data stored on the target storage (CPU vs
 //     GPU).
 absl::Status ConfigureImagePreprocessing(
-    const core::ModelResources& model_resources,
+    const core::ModelResources& model_resources, bool use_gpu,
     ImagePreprocessingOptions* options);
 
+// Determine if the image preprocessing subgraph should use GPU as the backend
+// according to the given acceleration setting.
+bool DetermineImagePreprocessingGpuBackend(
+    const core::proto::Acceleration& acceleration);
+
 }  // namespace components
 }  // namespace tasks
 }  // namespace mediapipe

mediapipe/tasks/cc/vision/gesture_recognizer/calculators/BUILD

@@ -93,3 +93,46 @@ cc_test(
         "@com_google_absl//absl/strings",
     ],
 )
+
+mediapipe_proto_library(
+    name = "combined_prediction_calculator_proto",
+    srcs = ["combined_prediction_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+cc_library(
+    name = "combined_prediction_calculator",
+    srcs = ["combined_prediction_calculator.cc"],
+    deps = [
+        ":combined_prediction_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:collection",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework/api2:packet",
+        "//mediapipe/framework/api2:port",
+        "//mediapipe/framework/formats:classification_cc_proto",
+        "@com_google_absl//absl/container:btree",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings:str_format",
+    ],
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "combined_prediction_calculator_test",
+    srcs = ["combined_prediction_calculator_test.cc"],
+    deps = [
+        ":combined_prediction_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework/formats:classification_cc_proto",
+        "//mediapipe/framework/port:gtest",
+        "//mediapipe/framework/port:gtest_main",
+        "@com_google_absl//absl/strings",
+    ],
+)

mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.cc

@@ -0,0 +1,187 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/container/btree_map.h"
+#include "absl/memory/memory.h"
+#include "absl/status/status.h"
+#include "absl/strings/str_format.h"
+#include "mediapipe/framework/api2/node.h"
+#include "mediapipe/framework/api2/packet.h"
+#include "mediapipe/framework/api2/port.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/collection.h"
+#include "mediapipe/framework/formats/classification.pb.h"
+#include "mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.pb.h"
+
+namespace mediapipe {
+namespace api2 {
+namespace {
+
+constexpr char kPredictionTag[] = "PREDICTION";
+
+Classification GetMaxScoringClassification(
+    const ClassificationList& classifications) {
+  Classification max_classification;
+  max_classification.set_score(0);
+  for (const auto& input : classifications.classification()) {
+    if (max_classification.score() < input.score()) {
+      max_classification = input;
+    }
+  }
+  return max_classification;
+}
+
+float GetScoreThreshold(
+    const std::string& input_label,
+    const absl::btree_map<std::string, float>& classwise_thresholds,
+    const std::string& background_label, const float default_threshold) {
+  float threshold = default_threshold;
+  auto it = classwise_thresholds.find(input_label);
+  if (it != classwise_thresholds.end()) {
+    threshold = it->second;
+  }
+  return threshold;
+}
+
+std::unique_ptr<ClassificationList> GetWinningPrediction(
+    const ClassificationList& classification_list,
+    const absl::btree_map<std::string, float>& classwise_thresholds,
+    const std::string& background_label, const float default_threshold) {
+  auto prediction_list = std::make_unique<ClassificationList>();
+  if (classification_list.classification().empty()) {
+    return prediction_list;
+  }
+  Classification& prediction = *prediction_list->add_classification();
+  auto argmax_prediction = GetMaxScoringClassification(classification_list);
+  float argmax_prediction_thresh =
+      GetScoreThreshold(argmax_prediction.label(), classwise_thresholds,
+                        background_label, default_threshold);
+  if (argmax_prediction.score() >= argmax_prediction_thresh) {
+    prediction.set_label(argmax_prediction.label());
+    prediction.set_score(argmax_prediction.score());
+  } else {
+    for (const auto& input : classification_list.classification()) {
+      if (input.label() == background_label) {
+        prediction.set_label(input.label());
+        prediction.set_score(input.score());
+        break;
+      }
+    }
+  }
+  return prediction_list;
+}
+
+}  // namespace
+
+// This calculator accepts multiple ClassificationList input streams. Each
+// ClassificationList should contain classifications with labels and
+// corresponding softmax scores. The calculator computes the best prediction for
+// each ClassificationList input stream via argmax and thresholding. Thresholds
+// for all classes can be specified in the
+// `CombinedPredictionCalculatorOptions`, along with a default global
+// threshold.
+// Please note that for this calculator to work as designed, the class names
+// other than the background class in the ClassificationList objects must be
+// different, but the background class name has to be the same. This background
+// label name can be set via `background_label` in
+// `CombinedPredictionCalculatorOptions`.
+// The ClassificationList in the PREDICTION output stream contains the label of
+// the winning class and corresponding softmax score. If none of the
+// ClassificationList objects has a non-background winning class, the output
+// contains the background class and score of the background class in the first
+// ClassificationList. If multiple ClassificationList objects have a
+// non-background winning class, the output contains the winning prediction from
+// the ClassificationList with the highest priority. Priority is in decreasing
+// order of input streams to the graph node using this calculator.
+// Input:
+//   At least one stream with ClassificationList.
+// Output:
+//  PREDICTION - A ClassificationList with the winning label as the only item.
+//
+// Usage example:
+// node {
+//   calculator: "CombinedPredictionCalculator"
+//   input_stream: "classification_list_0"
+//   input_stream: "classification_list_1"
+//   output_stream: "PREDICTION:prediction"
+//   options {
+//     [mediapipe.CombinedPredictionCalculatorOptions.ext] {
+//       class {
+//         label: "A"
+//         score_threshold: 0.7
+//       }
+//       default_global_threshold: 0.1
+//       background_label: "B"
+//     }
+//   }
+// }
+
+class CombinedPredictionCalculator : public Node {
+ public:
+  static constexpr Input<ClassificationList>::Multiple kClassificationListIn{
+      ""};
+  static constexpr Output<ClassificationList> kPredictionOut{"PREDICTION"};
+  MEDIAPIPE_NODE_CONTRACT(kClassificationListIn, kPredictionOut);
+
+  absl::Status Open(CalculatorContext* cc) override {
+    options_ = cc->Options<CombinedPredictionCalculatorOptions>();
+    for (const auto& input : options_.class_()) {
+      classwise_thresholds_[input.label()] = input.score_threshold();
+    }
+    classwise_thresholds_[options_.background_label()] = 0;
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    // After loop, if have winning prediction return. Otherwise empty packet.
+    std::unique_ptr<ClassificationList> first_winning_prediction = nullptr;
+    auto collection = kClassificationListIn(cc);
+    for (int idx = 0; idx < collection.Count(); ++idx) {
+      const auto& packet = collection[idx];
+      if (packet.IsEmpty()) {
+        continue;
+      }
+      auto prediction = GetWinningPrediction(
+          packet.Get(), classwise_thresholds_, options_.background_label(),
+          options_.default_global_threshold());
+      if (prediction->classification(0).label() !=
+          options_.background_label()) {
+        kPredictionOut(cc).Send(std::move(prediction));
+        return absl::OkStatus();
+      }
+      if (first_winning_prediction == nullptr) {
+        first_winning_prediction = std::move(prediction);
+      }
+    }
+    if (first_winning_prediction != nullptr) {
+      kPredictionOut(cc).Send(std::move(first_winning_prediction));
+    }
+    return absl::OkStatus();
+  }
+
+ private:
+  CombinedPredictionCalculatorOptions options_;
+  absl::btree_map<std::string, float> classwise_thresholds_;
+};
+
+MEDIAPIPE_REGISTER_NODE(CombinedPredictionCalculator);
+
+}  // namespace api2
+}  // namespace mediapipe

mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.proto

@@ -0,0 +1,41 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message CombinedPredictionCalculatorOptions {
+  extend mediapipe.CalculatorOptions {
+    optional CombinedPredictionCalculatorOptions ext = 483738635;
+  }
+
+  message Class {
+    optional string label = 1;
+    optional float score_threshold = 2;
+  }
+
+  // List of classes with score thresholds.
+  repeated Class class = 1;
+
+  // Default score threshold applied to a label.
+  optional float default_global_threshold = 2 [default = 0];
+
+  // Name of the background class whose input scores will be ignored while
+  // thresholding.
+  optional string background_label = 3;
+}

mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator_test.cc

@@ -0,0 +1,315 @@
+/* Copyright 2022 The MediaPipe 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.
+==============================================================================*/
+
+#include <cmath>
+#include <cstdint>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/strings/string_view.h"
+#include "absl/strings/substitute.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_runner.h"
+#include "mediapipe/framework/formats/classification.pb.h"
+#include "mediapipe/framework/port/status_matchers.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kPredictionTag[] = "PREDICTION";
+
+std::unique_ptr<CalculatorRunner> BuildNodeRunnerWithOptions(
+    float drama_thresh, float llama_thresh, float bazinga_thresh,
+    float joy_thresh, float peace_thresh) {
+  constexpr absl::string_view kCalculatorProto = R"pb(
+    calculator: "CombinedPredictionCalculator"
+    input_stream: "custom_softmax_scores"
+    input_stream: "canned_softmax_scores"
+    output_stream: "PREDICTION:prediction"
+    options {
+      [mediapipe.CombinedPredictionCalculatorOptions.ext] {
+        class { label: "CustomDrama" score_threshold: $0 }
+        class { label: "CustomLlama" score_threshold: $1 }
+        class { label: "CannedBazinga" score_threshold: $2 }
+        class { label: "CannedJoy" score_threshold: $3 }
+        class { label: "CannedPeace" score_threshold: $4 }
+        background_label: "Negative"
+      }
+    }
+  )pb";
+  auto runner = std::make_unique<CalculatorRunner>(
+      absl::Substitute(kCalculatorProto, drama_thresh, llama_thresh,
+                       bazinga_thresh, joy_thresh, peace_thresh));
+  return runner;
+}
+
+std::unique_ptr<ClassificationList> BuildCustomScoreInput(
+    const float negative_score, const float drama_score,
+    const float llama_score) {
+  auto custom_scores = std::make_unique<ClassificationList>();
+  auto custom_negative = custom_scores->add_classification();
+  custom_negative->set_label("Negative");
+  custom_negative->set_score(negative_score);
+  auto drama = custom_scores->add_classification();
+  drama->set_label("CustomDrama");
+  drama->set_score(drama_score);
+  auto llama = custom_scores->add_classification();
+  llama->set_label("CustomLlama");
+  llama->set_score(llama_score);
+  return custom_scores;
+}
+
+std::unique_ptr<ClassificationList> BuildCannedScoreInput(
+    const float negative_score, const float bazinga_score,
+    const float joy_score, const float peace_score) {
+  auto canned_scores = std::make_unique<ClassificationList>();
+  auto canned_negative = canned_scores->add_classification();
+  canned_negative->set_label("Negative");
+  canned_negative->set_score(negative_score);
+  auto bazinga = canned_scores->add_classification();
+  bazinga->set_label("CannedBazinga");
+  bazinga->set_score(bazinga_score);
+  auto joy = canned_scores->add_classification();
+  joy->set_label("CannedJoy");
+  joy->set_score(joy_score);
+  auto peace = canned_scores->add_classification();
+  peace->set_label("CannedPeace");
+  peace->set_score(peace_score);
+  return canned_scores;
+}
+
+TEST(CombinedPredictionCalculatorPacketTest,
+     CustomEmpty_CannedEmpty_ResultIsEmpty) {
+  auto runner = BuildNodeRunnerWithOptions(
+      /*drama_thresh=*/0.0, /*llama_thresh=*/0.0, /*bazinga_thresh=*/0.0,
+      /*joy_thresh=*/0.0, /*peace_thresh=*/0.0);
+  MP_ASSERT_OK(runner->Run()) << "Calculator execution failed.";
+  EXPECT_THAT(runner->Outputs().Tag("PREDICTION").packets, testing::IsEmpty());
+}
+
+TEST(CombinedPredictionCalculatorPacketTest,
+     CustomEmpty_CannedNotEmpty_ResultIsCanned) {
+  auto runner = BuildNodeRunnerWithOptions(
+      /*drama_thresh=*/0.0, /*llama_thresh=*/0.0, /*bazinga_thresh=*/0.9,
+      /*joy_thresh=*/0.5, /*peace_thresh=*/0.8);
+  auto canned_scores = BuildCannedScoreInput(
+      /*negative_score=*/0.1,
+      /*bazinga_score=*/0.1, /*joy_score=*/0.6, /*peace_score=*/0.2);
+  runner->MutableInputs()->Index(1).packets.push_back(
+      Adopt(canned_scores.release()).At(Timestamp(1)));
+  MP_ASSERT_OK(runner->Run()) << "Calculator execution failed.";
+
+  auto output_prediction_packets =
+      runner->Outputs().Tag(kPredictionTag).packets;
+  ASSERT_EQ(output_prediction_packets.size(), 1);
+  Classification output_prediction =
+      output_prediction_packets[0].Get<ClassificationList>().classification(0);
+
+  EXPECT_EQ(output_prediction.label(), "CannedJoy");
+  EXPECT_NEAR(output_prediction.score(), 0.6, 1e-4);
+}
+
+TEST(CombinedPredictionCalculatorPacketTest,
+     CustomNotEmpty_CannedEmpty_ResultIsCustom) {
+  auto runner = BuildNodeRunnerWithOptions(
+      /*drama_thresh=*/0.3, /*llama_thresh=*/0.5, /*bazinga_thresh=*/0.0,
+      /*joy_thresh=*/0.0, /*peace_thresh=*/0.0);
+  auto custom_scores =
+      BuildCustomScoreInput(/*negative_score=*/0.1,
+                            /*drama_score=*/0.2, /*llama_score=*/0.7);
+  runner->MutableInputs()->Index(0).packets.push_back(
+      Adopt(custom_scores.release()).At(Timestamp(1)));
+  MP_ASSERT_OK(runner->Run()) << "Calculator execution failed.";
+
+  auto output_prediction_packets =
+      runner->Outputs().Tag(kPredictionTag).packets;
+  ASSERT_EQ(output_prediction_packets.size(), 1);
+  Classification output_prediction =
+      output_prediction_packets[0].Get<ClassificationList>().classification(0);
+
+  EXPECT_EQ(output_prediction.label(), "CustomLlama");
+  EXPECT_NEAR(output_prediction.score(), 0.7, 1e-4);
+}
+
+struct CombinedPredictionCalculatorTestCase {
+  std::string test_name;
+  float custom_negative_score;
+  float drama_score;
+  float llama_score;
+  float drama_thresh;
+  float llama_thresh;
+  float canned_negative_score;
+  float bazinga_score;
+  float joy_score;
+  float peace_score;
+  float bazinga_thresh;
+  float joy_thresh;
+  float peace_thresh;
+  std::string max_scoring_label;
+  float max_score;
+};
+
+using CombinedPredictionCalculatorTest =
+    testing::TestWithParam<CombinedPredictionCalculatorTestCase>;
+
+TEST_P(CombinedPredictionCalculatorTest, OutputsCorrectResult) {
+  const CombinedPredictionCalculatorTestCase& test_case = GetParam();
+
+  auto runner = BuildNodeRunnerWithOptions(
+      test_case.drama_thresh, test_case.llama_thresh, test_case.bazinga_thresh,
+      test_case.joy_thresh, test_case.peace_thresh);
+
+  auto custom_scores =
+      BuildCustomScoreInput(test_case.custom_negative_score,
+                            test_case.drama_score, test_case.llama_score);
+
+  runner->MutableInputs()->Index(0).packets.push_back(
+      Adopt(custom_scores.release()).At(Timestamp(1)));
+
+  auto canned_scores = BuildCannedScoreInput(
+      test_case.canned_negative_score, test_case.bazinga_score,
+      test_case.joy_score, test_case.peace_score);
+  runner->MutableInputs()->Index(1).packets.push_back(
+      Adopt(canned_scores.release()).At(Timestamp(1)));
+
+  MP_ASSERT_OK(runner->Run()) << "Calculator execution failed.";
+
+  auto output_prediction_packets =
+      runner->Outputs().Tag(kPredictionTag).packets;
+  ASSERT_EQ(output_prediction_packets.size(), 1);
+  Classification output_prediction =
+      output_prediction_packets[0].Get<ClassificationList>().classification(0);
+
+  EXPECT_EQ(output_prediction.label(), test_case.max_scoring_label);
+  EXPECT_NEAR(output_prediction.score(), test_case.max_score, 1e-4);
+}
+
+INSTANTIATE_TEST_CASE_P(
+    CombinedPredictionCalculatorTests, CombinedPredictionCalculatorTest,
+    testing::ValuesIn<CombinedPredictionCalculatorTestCase>({
+        {
+            .test_name = "TestCustomDramaWinnnerWith_HighCanned_Thresh",
+            .custom_negative_score = 0.1,
+            .drama_score = 0.5,
+            .llama_score = 0.3,
+            .drama_thresh = 0.25,
+            .llama_thresh = 0.7,
+            .canned_negative_score = 0.1,
+            .bazinga_score = 0.3,
+            .joy_score = 0.3,
+            .peace_score = 0.3,
+            .bazinga_thresh = 0.7,
+            .joy_thresh = 0.7,
+            .peace_thresh = 0.7,
+            .max_scoring_label = "CustomDrama",
+            .max_score = 0.5,
+        },
+        {
+            .test_name = "TestCannedWinnerWith_HighCustom_ZeroCanned_Thresh",
+            .custom_negative_score = 0.1,
+            .drama_score = 0.3,
+            .llama_score = 0.6,
+            .drama_thresh = 0.4,
+            .llama_thresh = 0.8,
+            .canned_negative_score = 0.1,
+            .bazinga_score = 0.4,
+            .joy_score = 0.3,
+            .peace_score = 0.2,
+            .bazinga_thresh = 0.0,
+            .joy_thresh = 0.0,
+            .peace_thresh = 0.0,
+            .max_scoring_label = "CannedBazinga",
+            .max_score = 0.4,
+        },
+        {
+            .test_name = "TestNegativeWinnerWith_LowCustom_HighCanned_Thresh",
+            .custom_negative_score = 0.5,
+            .drama_score = 0.1,
+            .llama_score = 0.4,
+            .drama_thresh = 0.1,
+            .llama_thresh = 0.05,
+            .canned_negative_score = 0.1,
+            .bazinga_score = 0.3,
+            .joy_score = 0.3,
+            .peace_score = 0.3,
+            .bazinga_thresh = 0.7,
+            .joy_thresh = 0.7,
+            .peace_thresh = 0.7,
+            .max_scoring_label = "Negative",
+            .max_score = 0.5,
+        },
+        {
+            .test_name = "TestNegativeWinnerWith_HighCustom_HighCanned_Thresh",
+            .custom_negative_score = 0.8,
+            .drama_score = 0.1,
+            .llama_score = 0.1,
+            .drama_thresh = 0.25,
+            .llama_thresh = 0.7,
+            .canned_negative_score = 0.1,
+            .bazinga_score = 0.3,
+            .joy_score = 0.3,
+            .peace_score = 0.3,
+            .bazinga_thresh = 0.7,
+            .joy_thresh = 0.7,
+            .peace_thresh = 0.7,
+            .max_scoring_label = "Negative",
+            .max_score = 0.8,
+        },
+        {
+            .test_name = "TestNegativeWinnerWith_HighCustom_HighCannedThresh2",
+            .custom_negative_score = 0.1,
+            .drama_score = 0.2,
+            .llama_score = 0.7,
+            .drama_thresh = 1.1,
+            .llama_thresh = 1.1,
+            .canned_negative_score = 0.1,
+            .bazinga_score = 0.3,
+            .joy_score = 0.3,
+            .peace_score = 0.3,
+            .bazinga_thresh = 0.7,
+            .joy_thresh = 0.7,
+            .peace_thresh = 0.7,
+            .max_scoring_label = "Negative",
+            .max_score = 0.1,
+        },
+        {
+            .test_name = "TestNegativeWinnerWith_HighCustom_HighCanned_Thresh3",
+            .custom_negative_score = 0.1,
+            .drama_score = 0.3,
+            .llama_score = 0.6,
+            .drama_thresh = 0.4,
+            .llama_thresh = 0.8,
+            .canned_negative_score = 0.3,
+            .bazinga_score = 0.2,
+            .joy_score = 0.3,
+            .peace_score = 0.2,
+            .bazinga_thresh = 0.5,
+            .joy_thresh = 0.5,
+            .peace_thresh = 0.5,
+            .max_scoring_label = "Negative",
+            .max_score = 0.1,
+        },
+    }),
+    [](const testing::TestParamInfo<
+        CombinedPredictionCalculatorTest::ParamType>& info) {
+      return info.param.test_name;
+    });
+
+}  // namespace
+
+}  // namespace mediapipe

mediapipe/tasks/cc/vision/hand_detector/hand_detector_graph.cc

@@ -235,8 +235,10 @@ class HandDetectorGraph : public core::ModelTaskGraph {
     image_to_tensor_options.set_keep_aspect_ratio(true);
     image_to_tensor_options.set_border_mode(
         mediapipe::ImageToTensorCalculatorOptions::BORDER_ZERO);
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        subgraph_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In("IMAGE");

mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarks_detector_graph.cc

@@ -283,8 +283,10 @@ class SingleHandLandmarksDetectorGraph : public core::ModelTaskGraph {
 
     auto& preprocessing =
         graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        subgraph_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In("IMAGE");

mediapipe/tasks/cc/vision/image_classifier/image_classifier_graph.cc

@@ -138,8 +138,10 @@ class ImageClassifierGraph : public core::ModelTaskGraph {
     // stream.
     auto& preprocessing =
         graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        task_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In(kImageTag);

mediapipe/tasks/cc/vision/image_embedder/image_embedder_graph.cc

@@ -134,8 +134,10 @@ class ImageEmbedderGraph : public core::ModelTaskGraph {
     // stream.
     auto& preprocessing =
         graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        task_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In(kImageTag);

mediapipe/tasks/cc/vision/image_segmenter/image_segmenter_graph.cc

@@ -243,8 +243,10 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
     // stream.
     auto& preprocessing =
         graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        task_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In(kImageTag);

mediapipe/tasks/cc/vision/object_detector/object_detector_graph.cc

@@ -563,8 +563,10 @@ class ObjectDetectorGraph : public core::ModelTaskGraph {
     // stream.
     auto& preprocessing =
         graph.AddNode("mediapipe.tasks.components.ImagePreprocessingSubgraph");
+    bool use_gpu = components::DetermineImagePreprocessingGpuBackend(
+        task_options.base_options().acceleration());
     MP_RETURN_IF_ERROR(ConfigureImagePreprocessing(
-        model_resources,
+        model_resources, use_gpu,
         &preprocessing
              .GetOptions<tasks::components::ImagePreprocessingOptions>()));
     image_in >> preprocessing.In(kImageTag);

mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl

@@ -40,6 +40,10 @@ _VISION_TASKS_JAVA_PROTO_LITE_TARGETS = [
     "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_java_proto_lite",
 ]
 
+_TEXT_TASKS_JAVA_PROTO_LITE_TARGETS = [
+    "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_java_proto_lite",
+]
+
 def mediapipe_tasks_core_aar(name, srcs, manifest):
     """Builds medaipipe tasks core AAR.
 
@@ -60,6 +64,11 @@ def mediapipe_tasks_core_aar(name, srcs, manifest):
             _mediapipe_tasks_java_proto_src_extractor(target = target),
         )
 
+    for target in _TEXT_TASKS_JAVA_PROTO_LITE_TARGETS:
+        mediapipe_tasks_java_proto_srcs.append(
+            _mediapipe_tasks_java_proto_src_extractor(target = target),
+        )
+
     mediapipe_tasks_java_proto_srcs.append(mediapipe_java_proto_src_extractor(
         target = "//mediapipe/calculators/core:flow_limiter_calculator_java_proto_lite",
         src_out = "com/google/mediapipe/calculator/proto/FlowLimiterCalculatorProto.java",
@@ -106,7 +115,10 @@ def mediapipe_tasks_core_aar(name, srcs, manifest):
                    "@maven//:com_google_flogger_flogger",
                    "@maven//:com_google_flogger_flogger_system_backend",
                    "@maven//:com_google_code_findbugs_jsr305",
-        ] + _CORE_TASKS_JAVA_PROTO_LITE_TARGETS + _VISION_TASKS_JAVA_PROTO_LITE_TARGETS,
+               ] +
+               _CORE_TASKS_JAVA_PROTO_LITE_TARGETS +
+               _VISION_TASKS_JAVA_PROTO_LITE_TARGETS +
+               _TEXT_TASKS_JAVA_PROTO_LITE_TARGETS,
     )
 
 def mediapipe_tasks_vision_aar(name, srcs, native_library):
@@ -142,6 +154,39 @@ EOF
         native_library = native_library,
     )
 
+def mediapipe_tasks_text_aar(name, srcs, native_library):
+    """Builds medaipipe tasks text AAR.
+
+    Args:
+      name: The bazel target name.
+      srcs: MediaPipe Text Tasks' source files.
+      native_library: The native library that contains text tasks' graph and calculators.
+    """
+
+    native.genrule(
+        name = name + "tasks_manifest_generator",
+        outs = ["AndroidManifest.xml"],
+        cmd = """
+cat > $(OUTS) <<EOF
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.google.mediapipe.tasks.text">
+    <uses-sdk
+        android:minSdkVersion="24"
+        android:targetSdkVersion="30" />
+</manifest>
+EOF
+""",
+    )
+
+    _mediapipe_tasks_aar(
+        name = name,
+        srcs = srcs,
+        manifest = "AndroidManifest.xml",
+        java_proto_lite_targets = _CORE_TASKS_JAVA_PROTO_LITE_TARGETS + _TEXT_TASKS_JAVA_PROTO_LITE_TARGETS,
+        native_library = native_library,
+    )
+
 def _mediapipe_tasks_aar(name, srcs, manifest, java_proto_lite_targets, native_library):
     """Builds medaipipe tasks AAR."""
 

mediapipe/tasks/java/com/google/mediapipe/tasks/text/BUILD

@@ -61,3 +61,11 @@ android_library(
         "@maven//:com_google_guava_guava",
     ],
 )
+
+load("//mediapipe/tasks/java/com/google/mediapipe/tasks:mediapipe_tasks_aar.bzl", "mediapipe_tasks_text_aar")
+
+mediapipe_tasks_text_aar(
+    name = "tasks_text",
+    srcs = glob(["**/*.java"]),
+    native_library = ":libmediapipe_tasks_text_jni_lib",
+)

mediapipe/util/tflite/BUILD

@@ -97,20 +97,15 @@ cc_library(
                    "@org_tensorflow//tensorflow/lite/delegates/gpu/common:model_builder",
                    "@org_tensorflow//tensorflow/lite/delegates/gpu/gl:api2",
                ],
+           }) +
+           select({
                "//mediapipe:android": [
-            "@com_google_absl//absl/strings",
-            "//mediapipe/framework/port:ret_check",
-            "//mediapipe/framework/port:status",
-            "//mediapipe/framework/port:statusor",
-            "@org_tensorflow//tensorflow/lite:framework",
-            "@org_tensorflow//tensorflow/lite/delegates/gpu:api",
                    "@org_tensorflow//tensorflow/lite/delegates/gpu/cl:api",
-            "@org_tensorflow//tensorflow/lite/delegates/gpu/common:model",
-            "@org_tensorflow//tensorflow/lite/delegates/gpu/common:model_builder",
-            "@org_tensorflow//tensorflow/lite/delegates/gpu/gl:api2",
                ],
+               "//conditions:default": [],
            }) + [
         "@com_google_absl//absl/status",
+        "//mediapipe/framework:port",
         "@org_tensorflow//tensorflow/lite/core/api",
     ],
 )

mediapipe/util/tflite/tflite_gpu_runner.cc

@@ -34,7 +34,7 @@
 
 // This code should be enabled as soon as TensorFlow version, which mediapipe
 // uses, will include this module.
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 #include "tensorflow/lite/delegates/gpu/cl/api.h"
 #endif
 
@@ -82,7 +82,7 @@ ObjectDef GetSSBOObjectDef(int channels) {
   return gpu_object_def;
 }
 
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
 cl::InferenceOptions GetClInferenceOptions(const InferenceOptions& options) {
   cl::InferenceOptions result{};
@@ -106,7 +106,7 @@ absl::Status VerifyShapes(const std::vector<TensorObjectDef>& actual,
   return absl::OkStatus();
 }
 
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
 }  // namespace
 
@@ -225,7 +225,7 @@ absl::Status TFLiteGPURunner::InitializeOpenGL(
 
 absl::Status TFLiteGPURunner::InitializeOpenCL(
     std::unique_ptr<InferenceBuilder>* builder) {
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
   cl::InferenceEnvironmentOptions env_options;
   if (!serialized_binary_cache_.empty()) {
     env_options.serialized_binary_cache = serialized_binary_cache_;
@@ -254,11 +254,12 @@ absl::Status TFLiteGPURunner::InitializeOpenCL(
 
   return absl::OkStatus();
 #else
-  return mediapipe::UnimplementedError("Currently only Android is supported");
-#endif  // __ANDROID__
+  return mediapipe::UnimplementedError(
+      "Currently only Android & ChromeOS are supported");
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 }
 
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
 absl::Status TFLiteGPURunner::InitializeOpenCLFromSerializedModel(
     std::unique_ptr<InferenceBuilder>* builder) {
@@ -283,7 +284,7 @@ absl::StatusOr<std::vector<uint8_t>> TFLiteGPURunner::GetSerializedModel() {
   return serialized_model;
 }
 
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
 }  // namespace gpu
 }  // namespace tflite

mediapipe/util/tflite/tflite_gpu_runner.h

@@ -20,6 +20,7 @@
 #include <vector>
 
 #include "absl/status/status.h"
+#include "mediapipe/framework/port.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/framework/port/statusor.h"
 #include "tensorflow/lite/core/api/op_resolver.h"
@@ -28,9 +29,9 @@
 #include "tensorflow/lite/delegates/gpu/gl/api2.h"
 #include "tensorflow/lite/model.h"
 
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 #include "tensorflow/lite/delegates/gpu/cl/api.h"
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
 namespace tflite {
 namespace gpu {
@@ -83,7 +84,7 @@ class TFLiteGPURunner {
     return output_shape_from_model_;
   }
 
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
   void SetSerializedBinaryCache(std::vector<uint8_t>&& cache) {
     serialized_binary_cache_ = std::move(cache);
   }
@@ -98,26 +99,26 @@ class TFLiteGPURunner {
   }
 
   absl::StatusOr<std::vector<uint8_t>> GetSerializedModel();
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
  private:
   absl::Status InitializeOpenGL(std::unique_ptr<InferenceBuilder>* builder);
   absl::Status InitializeOpenCL(std::unique_ptr<InferenceBuilder>* builder);
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
   absl::Status InitializeOpenCLFromSerializedModel(
       std::unique_ptr<InferenceBuilder>* builder);
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
   InferenceOptions options_;
   std::unique_ptr<gl::InferenceEnvironment> gl_environment_;
 
-#ifdef __ANDROID__
+#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
   std::unique_ptr<cl::InferenceEnvironment> cl_environment_;
 
   std::vector<uint8_t> serialized_binary_cache_;
   std::vector<uint8_t> serialized_model_;
   bool serialized_model_used_ = false;
-#endif  // __ANDROID__
+#endif  // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
 
   // graph_gl_ is maintained temporarily and becomes invalid after runner_ is
   // ready

third_party/com_google_sentencepiece_no_gflag_no_gtest.diff

@@ -0,0 +1,34 @@
+diff --git a/src/BUILD b/src/BUILD
+index b4298d2..f3877a3 100644
+--- a/src/BUILD
++++ b/src/BUILD
+@@ -71,9 +71,7 @@ cc_library(
+             ":common",
+             ":sentencepiece_cc_proto",
+             ":sentencepiece_model_cc_proto",
+-            "@com_github_gflags_gflags//:gflags",
+             "@com_google_glog//:glog",
+-            "@com_google_googletest//:gtest",
+             "@com_google_absl//absl/memory",
+             "@com_google_absl//absl/strings",
+             "@com_google_absl//absl/container:flat_hash_map",
+diff --git a/src/normalizer.h b/src/normalizer.h
+index c16ac16..2af58be 100644
+--- a/src/normalizer.h
++++ b/src/normalizer.h
+@@ -21,7 +21,6 @@
+ #include <utility>
+ #include <vector>
+
+-#include "gtest/gtest_prod.h"
+ #include "absl/strings/string_view.h"
+ #include "third_party/darts_clone/include/darts.h"
+ #include "src/common.h"
+@@ -97,7 +96,6 @@ class Normalizer {
+   friend class Builder;
+
+  private:
+-  FRIEND_TEST(NormalizerTest, EncodeDecodePrecompiledCharsMapTest);
+
+   void Init();
+