Merge branch 'google:master' into face-stylizer-python-add-tests

.bazelrc

@@ -87,6 +87,9 @@ build:ios_fat --config=ios
 build:ios_fat --ios_multi_cpus=armv7,arm64
 build:ios_fat --watchos_cpus=armv7k
 
+build:ios_sim_fat --config=ios
+build:ios_sim_fat --ios_multi_cpus=x86_64,sim_arm64
+
 build:darwin_x86_64 --apple_platform_type=macos
 build:darwin_x86_64 --macos_minimum_os=10.12
 build:darwin_x86_64 --cpu=darwin_x86_64

.github/stale.yml

@@ -1,34 +0,0 @@
-# Copyright 2021 The MediaPipe Authors.
-#
-# 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.
-# ============================================================================
-#
-# This file was assembled from multiple pieces, whose use is documented
-# throughout. Please refer to the TensorFlow dockerfiles documentation
-# for more information.
-
-# Number of days of inactivity before an Issue or Pull Request becomes stale
-daysUntilStale: 7
-# Number of days of inactivity before a stale Issue or Pull Request is closed
-daysUntilClose: 7
-# Only issues or pull requests with all of these labels are checked if stale. Defaults to `[]` (disabled)
-onlyLabels:
- - stat:awaiting response
-# Comment to post when marking as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Thank you.
-# Comment to post when removing the stale label. Set to `false` to disable
-unmarkComment: false
-closeComment: >
-  Closing as stale. Please reopen if you'd like to work on this further.

.github/workflows/stale.yaml

@@ -0,0 +1,66 @@
+# Copyright 2023 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.
+# ==============================================================================
+
+# This workflow alerts and then closes the stale issues/PRs after specific time
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+
+name: 'Close stale issues and PRs'
+"on":
+  schedule:
+  - cron: "30 1 * * *"
+permissions:
+  contents: read
+  issues: write
+  pull-requests: write
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: 'actions/stale@v7'
+      with:
+        # Comma separated list of labels that can be assigned to issues to exclude them from being marked as stale.
+        exempt-issue-labels: 'override-stale'
+        # Comma separated list of labels that can be assigned to PRs to exclude them from being marked as stale.
+        exempt-pr-labels: "override-stale"
+        # Limit the No. of API calls in one run default value is 30.
+        operations-per-run: 500
+        # Prevent to remove stale label when PRs or issues are updated.
+        remove-stale-when-updated: false
+        # comment on issue if not active for more then 7 days.
+        stale-issue-message: 'This issue has been marked stale because it has no recent activity since 7 days. It will be closed if no further activity occurs. Thank you.'
+        # comment on PR if not active for more then 14 days.
+        stale-pr-message: 'This PR has been marked stale because it has no recent activity since 14 days. It will be closed if no further activity occurs. Thank you.'
+        # comment on issue if stale for more then 7 days.
+        close-issue-message: This issue was closed due to lack of activity after being marked stale for past 7 days.
+        # comment on PR if stale for more then 14 days.
+        close-pr-message: This PR was closed due to lack of activity after being marked stale for past 14 days.
+        # Number of days of inactivity before an Issue Request becomes stale
+        days-before-issue-stale: 7
+        # Number of days of inactivity before a stale Issue is closed
+        days-before-issue-close: 7
+        # reason for closed the issue default value is not_planned
+        close-issue-reason: completed
+        # Number of days of inactivity before a stale PR is closed
+        days-before-pr-close: 14
+        # Number of days of inactivity before an PR Request becomes stale
+        days-before-pr-stale: 14
+        # Check for label to stale or close the issue/PR
+        any-of-labels: 'stat:awaiting response'
+        # override stale to stalled for PR
+        stale-pr-label: 'stale'
+        # override stale to stalled for Issue
+        stale-issue-label: "stale"

docs/build_java_api_docs.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

docs/build_model_maker_api_docs.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

docs/build_py_api_docs.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/calculators/audio/spectrogram_calculator.cc

@@ -433,9 +433,9 @@ absl::Status SpectrogramCalculator::ProcessVectorToOutput(
 absl::Status SpectrogramCalculator::ProcessVector(const Matrix& input_stream,
                                                   CalculatorContext* cc) {
   switch (output_type_) {
-    // These blocks deliberately ignore clang-format to preserve the
+      // These blocks deliberately ignore clang-format to preserve the
-    // "silhouette" of the different cases.
+      // "silhouette" of the different cases.
-    // clang-format off
+      // clang-format off
     case SpectrogramCalculatorOptions::COMPLEX: {
       return ProcessVectorToOutput(
           input_stream,

mediapipe/calculators/core/merge_to_vector_calculator.cc

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/calculators/core/merge_to_vector_calculator.h

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/calculators/tensor/bert_preprocessor_calculator.proto

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/calculators/tensor/regex_preprocessor_calculator.proto

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/calculators/tensorflow/BUILD

@@ -400,6 +400,16 @@ cc_library(
 # compile your binary with the flag TENSORFLOW_PROTOS=lite.
 cc_library(
     name = "tensorflow_inference_calculator_no_envelope_loader",
+    deps = [
+        ":tensorflow_inference_calculator_for_boq",
+    ],
+    alwayslink = 1,
+)
+
+# This dependency removed tensorflow_jellyfish_deps and xprofilez_with_server because they failed
+# Boq conformance test. Weigh your use case to see if this will work for you.
+cc_library(
+    name = "tensorflow_inference_calculator_for_boq",
     srcs = ["tensorflow_inference_calculator.cc"],
     deps = [
         ":tensorflow_inference_calculator_cc_proto",
@@ -585,6 +595,24 @@ cc_library(
 # See yaqs/1092546221614039040
 cc_library(
     name = "tensorflow_session_from_saved_model_generator_no_envelope_loader",
+    defines = select({
+        "//mediapipe:android": ["__ANDROID__"],
+        "//conditions:default": [],
+    }),
+    deps = [
+        ":tensorflow_session_from_saved_model_generator_for_boq",
+    ] + select({
+        "//conditions:default": [
+            "//learning/brain/frameworks/uptc/public:uptc_session_no_envelope_loader",
+        ],
+    }),
+    alwayslink = 1,
+)
+
+# Same library as tensorflow_session_from_saved_model_generator without uptc_session,
+# envelop_loader and remote_session dependencies.
+cc_library(
+    name = "tensorflow_session_from_saved_model_generator_for_boq",
     srcs = ["tensorflow_session_from_saved_model_generator.cc"],
     defines = select({
         "//mediapipe:android": ["__ANDROID__"],

mediapipe/calculators/util/detections_deduplicate_calculator.cc

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/calculators/util/logic_calculator.proto

@@ -18,6 +18,9 @@ package mediapipe;
 
 import "mediapipe/framework/calculator.proto";
 
+option java_package = "com.google.mediapipe.calculator.proto";
+option java_outer_classname = "LogicCalculatorOptionsProto";
+
 message LogicCalculatorOptions {
   extend CalculatorOptions {
     optional LogicCalculatorOptions ext = 338731246;

mediapipe/framework/api2/builder.h

@@ -223,6 +223,16 @@ class SourceImpl {
     return !(*this == other);
   }
 
+  Src& SetName(const char* name) {
+    base_->name_ = std::string(name);
+    return *this;
+  }
+
+  Src& SetName(absl::string_view name) {
+    base_->name_ = std::string(name);
+    return *this;
+  }
+
   Src& SetName(std::string name) {
     base_->name_ = std::move(name);
     return *this;

mediapipe/framework/api2/port.h

@@ -467,6 +467,11 @@ class SideFallbackT : public Base {
 // CalculatorContext (e.g. kOut(cc)), and provides a type-safe interface to
 // OutputStreamShard. Like that class, this class will not be usually named in
 // calculator code, but used as a temporary object (e.g. kOut(cc).Send(...)).
+//
+// If not connected (!IsConnected()) SetNextTimestampBound is safe to call and
+// does nothing.
+// All the sub-classes that define Send should implement it to be safe to to
+// call if not connected and do nothing in such case.
 class OutputShardAccessBase {
  public:
   OutputShardAccessBase(const CalculatorContext& cc, OutputStreamShard* output)

mediapipe/framework/formats/frame_buffer.cc

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/framework/formats/frame_buffer.h

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/framework/tool/ios.bzl

@@ -0,0 +1,53 @@
+# Copyright 2023 The MediaPipe Authors.
+#
+# 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.
+
+"""MediaPipe Task Library Helper Rules for iOS"""
+
+MPP_TASK_MINIMUM_OS_VERSION = "11.0"
+
+# When the static framework is built with bazel, the all header files are moved
+# to the "Headers" directory with no header path prefixes. This auxiliary rule
+# is used for stripping the path prefix to the C/iOS API header files included by
+# other C/iOS API header files.
+# In case of C header files, includes start with a keyword of "#include'.
+# Imports in iOS header files start with a keyword of '#import'.
+def strip_api_include_path_prefix(name, hdr_labels, prefix = ""):
+    """Create modified header files with the import path stripped out.
+
+    Args:
+      name: The name to be used as a prefix to the generated genrules.
+      hdr_labels: List of header labels to strip out the include path. Each
+          label must end with a colon followed by the header file name.
+      prefix: Optional prefix path to prepend to the header inclusion path.
+    """
+    for hdr_label in hdr_labels:
+        hdr_filename = hdr_label.split(":")[-1]
+
+        # The last path component of iOS header files is sources/some_file.h
+        # Hence it wiill contain a '/'. So the string can be split at '/' to get
+        # the header file name.
+        if "/" in hdr_filename:
+            hdr_filename = hdr_filename.split("/")[-1]
+
+        hdr_basename = hdr_filename.split(".")[0]
+        native.genrule(
+            name = "{}_{}".format(name, hdr_basename),
+            srcs = [hdr_label],
+            outs = [hdr_filename],
+            cmd = """
+            sed 's|#\\([a-z]*\\) ".*/\\([^/]\\{{1,\\}}\\.h\\)"|#\\1 "{}\\2"|'\
+            "$(location {})"\
+            > "$@"
+            """.format(prefix, hdr_label),
+        )

mediapipe/framework/tool/name_util.cc

@@ -60,7 +60,7 @@ std::string GetUnusedSidePacketName(
   }
   std::string candidate = input_side_packet_name_base;
   int iter = 2;
-  while (mediapipe::ContainsKey(input_side_packets, candidate)) {
+  while (input_side_packets.contains(candidate)) {
     candidate = absl::StrCat(input_side_packet_name_base, "_",
                              absl::StrFormat("%02d", iter));
     ++iter;

mediapipe/framework/tool/tag_map_test.cc

@@ -101,7 +101,7 @@ void TestSuccessTagMap(const std::vector<std::string>& tag_index_names,
   EXPECT_EQ(tags.size(), tag_map->Mapping().size())
       << "Parameters: in " << tag_map->DebugString();
   for (int i = 0; i < tags.size(); ++i) {
-    EXPECT_TRUE(mediapipe::ContainsKey(tag_map->Mapping(), tags[i]))
+    EXPECT_TRUE(tag_map->Mapping().contains(tags[i]))
         << "Parameters: Trying to find \"" << tags[i] << "\" in\n"
         << tag_map->DebugString();
   }

mediapipe/gpu/frame_buffer_view.h

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/gpu/gpu_buffer_storage_image_frame.cc

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/gpu/gpu_buffer_storage_image_frame.h

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/gpu/gpu_buffer_storage_yuv_image.cc

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/gpu/gpu_buffer_storage_yuv_image.h

@@ -1,4 +1,4 @@
-/* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2023 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/components/AudioDataProducer.java

@@ -14,8 +14,10 @@
 
 package com.google.mediapipe.components;
 
+import javax.annotation.Nullable;
+
 /** Lightweight abstraction for an object that can produce audio data. */
 public interface AudioDataProducer {
   /** Set the consumer that receives the audio data from this producer. */
-  void setAudioConsumer(AudioDataConsumer consumer);
+  void setAudioConsumer(@Nullable AudioDataConsumer consumer);
 }

mediapipe/java/com/google/mediapipe/components/BUILD

@@ -71,7 +71,10 @@ android_library(
         "AudioDataProducer.java",
     ],
     visibility = ["//visibility:public"],
-    deps = ["@maven//:com_google_guava_guava"],
+    deps = [
+        "@maven//:com_google_code_findbugs_jsr305",
+        "@maven//:com_google_guava_guava",
+    ],
 )
 
 # MicrophoneHelper that provides access to audio data from a microphone

mediapipe/java/com/google/mediapipe/components/GlSurfaceViewRenderer.java

@@ -231,7 +231,7 @@ public class GlSurfaceViewRenderer implements GLSurfaceView.Renderer {
   }
 
   /** Returns the texture left, right, bottom, and top visible boundaries. */
-  protected float[] calculateTextureBoundary() {
+  public float[] calculateTextureBoundary() {
     // TODO: compute scale from surfaceTexture size.
     float scaleWidth = frameWidth > 0 ? (float) surfaceWidth / (float) frameWidth : 1.0f;
     float scaleHeight = frameHeight > 0 ? (float) surfaceHeight / (float) frameHeight : 1.0f;

mediapipe/java/com/google/mediapipe/framework/image/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/BitmapExtractor.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/BitmapImageBuilder.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/BitmapImageContainer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/ByteBufferExtractor.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/ByteBufferImageBuilder.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/ByteBufferImageContainer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MPImage.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MPImageConsumer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MPImageContainer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MPImageProducer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MPImageProperties.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MediaImageBuilder.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MediaImageContainer.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/framework/image/MediaImageExtractor.java

@@ -1,4 +1,4 @@
-/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+/* Copyright 2022 The MediaPipe Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

mediapipe/java/com/google/mediapipe/mediapipe_aar.bzl

@@ -1,4 +1,4 @@
-# Copyright 2019-2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2019-2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/models/gesture_recognizer/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/models/text_classifier/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/classification_dataset.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/classification_dataset_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/data_util.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/data_util_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/dataset.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/dataset_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/data/testdata/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/hyperparameters.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/classifier.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/classifier_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the 'License');
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/custom_model.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/tasks/custom_model_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the 'License');
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -67,11 +67,18 @@ py_library(
     name = "loss_functions",
     srcs = ["loss_functions.py"],
     srcs_version = "PY3",
+    deps = [
+        ":file_util",
+        ":model_util",
+    ],
 )
 
 py_test(
     name = "loss_functions_test",
     srcs = ["loss_functions_test.py"],
+    tags = [
+        "requires-net:external",
+    ],
     deps = [":loss_functions"],
 )
 

mediapipe/model_maker/python/core/utils/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/file_util.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/file_util_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/loss_functions.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,10 +13,21 @@
 # limitations under the License.
 """Loss function utility library."""
 
-from typing import Optional, Sequence
+import abc
+from typing import Mapping, Sequence
+import dataclasses
+from typing import Any, Optional
 
+import numpy as np
 import tensorflow as tf
 
+from mediapipe.model_maker.python.core.utils import file_util
+from mediapipe.model_maker.python.core.utils import model_util
+from official.modeling import tf_utils
+
+
+_VGG_IMAGENET_PERCEPTUAL_MODEL_URL = 'https://storage.googleapis.com/mediapipe-assets/vgg_feature_extractor.tar.gz'
+
 
 class FocalLoss(tf.keras.losses.Loss):
   """Implementation of focal loss (https://arxiv.org/pdf/1708.02002.pdf).
@@ -45,7 +56,6 @@ class FocalLoss(tf.keras.losses.Loss):
   ```python
   model.compile(optimizer='sgd', loss=FocalLoss(gamma))
   ```
-
   """
 
   def __init__(self, gamma, class_weight: Optional[Sequence[float]] = None):
@@ -103,3 +113,252 @@ class FocalLoss(tf.keras.losses.Loss):
     # By default, this function uses "sum_over_batch_size" reduction for the
     # loss per batch.
     return tf.reduce_sum(losses) / batch_size
+
+
+@dataclasses.dataclass
+class PerceptualLossWeight:
+  """The weight for each perceptual loss.
+
+  Attributes:
+    l1: weight for L1 loss.
+    content: weight for content loss.
+    style: weight for style loss.
+  """
+
+  l1: float = 1.0
+  content: float = 1.0
+  style: float = 1.0
+
+
+class ImagePerceptualQualityLoss(tf.keras.losses.Loss):
+  """Image perceptual quality loss.
+
+  It obtains a weighted loss between the VGGPerceptualLoss and L1 loss.
+  """
+
+  def __init__(
+      self,
+      loss_weight: Optional[PerceptualLossWeight] = None,
+      reduction: tf.keras.losses.Reduction = tf.keras.losses.Reduction.NONE,
+  ):
+    """Initializes ImagePerceptualQualityLoss."""
+    self._loss_weight = loss_weight
+    self._losses = {}
+    self._vgg_loss = VGGPerceptualLoss(self._loss_weight)
+    self._reduction = reduction
+
+  def _l1_loss(
+      self,
+      reduction: tf.keras.losses.Reduction = tf.keras.losses.Reduction.NONE,
+  ) -> Any:
+    """Calculates L1 loss."""
+    return tf.keras.losses.MeanAbsoluteError(reduction)
+
+  def __call__(
+      self,
+      img1: tf.Tensor,
+      img2: tf.Tensor,
+  ) -> tf.Tensor:
+    """Computes image perceptual quality loss."""
+    loss_value = []
+    if self._loss_weight is None:
+      self._loss_weight = PerceptualLossWeight()
+
+    if self._loss_weight.content > 0 or self._loss_weight.style > 0:
+      vgg_loss = self._vgg_loss(img1, img2)
+      vgg_loss_value = tf.math.add_n(vgg_loss.values())
+      loss_value.append(vgg_loss_value)
+    if self._loss_weight.l1 > 0:
+      l1_loss = self._l1_loss(reduction=self._reduction)(img1, img2)
+      l1_loss_value = tf_utils.safe_mean(l1_loss * self._loss_weight.l1)
+      loss_value.append(l1_loss_value)
+    total_loss = tf.math.add_n(loss_value)
+    return total_loss
+
+
+class PerceptualLoss(tf.keras.Model, metaclass=abc.ABCMeta):
+  """Base class for perceptual loss model."""
+
+  def __init__(
+      self,
+      feature_weight: Optional[Sequence[float]] = None,
+      loss_weight: Optional[PerceptualLossWeight] = None,
+  ):
+    """Instantiates perceptual loss.
+
+    Args:
+      feature_weight: The weight coeffcients of multiple model extracted
+        features used for calculating the perceptual loss.
+      loss_weight: The weight coefficients between `style_loss` and
+        `content_loss`.
+    """
+    super().__init__()
+    self._loss_op = lambda x, y: tf.math.reduce_mean(tf.abs(x - y))
+    self._loss_style = tf.constant(0.0)
+    self._loss_content = tf.constant(0.0)
+    self._feature_weight = feature_weight
+    self._loss_weight = loss_weight
+
+  def __call__(
+      self,
+      img1: tf.Tensor,
+      img2: tf.Tensor,
+  ) -> Mapping[str, tf.Tensor]:
+    """Computes perceptual loss between two images.
+
+    Args:
+      img1: First batch of images. The pixel values should be normalized to [-1,
+        1].
+      img2: Second batch of images. The pixel values should be normalized to
+        [-1, 1].
+
+    Returns:
+      A mapping between loss name and loss tensors.
+    """
+    x_features = self._compute_features(img1)
+    y_features = self._compute_features(img2)
+
+    if self._loss_weight is None:
+      self._loss_weight = PerceptualLossWeight()
+
+    # If the _feature_weight is not initialized, then initialize it as a list of
+    # all the element equals to 1.0.
+    if self._feature_weight is None:
+      self._feature_weight = [1.0] * len(x_features)
+
+    # If the length of _feature_weight smallert than the length of the feature,
+    # raise a ValueError. Otherwise, only use the first len(x_features) weight
+    # for computing the loss.
+    if len(self._feature_weight) < len(x_features):
+      raise ValueError(
+          f'Input feature weight length {len(self._feature_weight)} is smaller'
+          f' than feature length {len(x_features)}'
+      )
+
+    if self._loss_weight.style > 0.0:
+      self._loss_style = tf_utils.safe_mean(
+          self._loss_weight.style
+          * self._get_style_loss(x_feats=x_features, y_feats=y_features)
+      )
+    if self._loss_weight.content > 0.0:
+      self._loss_content = tf_utils.safe_mean(
+          self._loss_weight.content
+          * self._get_content_loss(x_feats=x_features, y_feats=y_features)
+      )
+
+    return {'style_loss': self._loss_style, 'content_loss': self._loss_content}
+
+  @abc.abstractmethod
+  def _compute_features(self, img: tf.Tensor) -> Sequence[tf.Tensor]:
+    """Computes features from the given image tensor.
+
+    Args:
+      img: Image tensor.
+
+    Returns:
+      A list of multi-scale feature maps.
+    """
+
+  def _get_content_loss(
+      self, x_feats: Sequence[tf.Tensor], y_feats: Sequence[tf.Tensor]
+  ) -> tf.Tensor:
+    """Gets weighted multi-scale content loss.
+
+    Args:
+      x_feats: Reconstructed face image.
+      y_feats: Target face image.
+
+    Returns:
+      A scalar tensor for the content loss.
+    """
+    content_losses = []
+    for coef, x_feat, y_feat in zip(self._feature_weight, x_feats, y_feats):
+      content_loss = self._loss_op(x_feat, y_feat) * coef
+      content_losses.append(content_loss)
+    return tf.math.reduce_sum(content_losses)
+
+  def _get_style_loss(
+      self, x_feats: Sequence[tf.Tensor], y_feats: Sequence[tf.Tensor]
+  ) -> tf.Tensor:
+    """Gets weighted multi-scale style loss.
+
+    Args:
+      x_feats: Reconstructed face image.
+      y_feats: Target face image.
+
+    Returns:
+      A scalar tensor for the style loss.
+    """
+    style_losses = []
+    i = 0
+    for coef, x_feat, y_feat in zip(self._feature_weight, x_feats, y_feats):
+      x_feat_g = _compute_gram_matrix(x_feat)
+      y_feat_g = _compute_gram_matrix(y_feat)
+      style_loss = self._loss_op(x_feat_g, y_feat_g) * coef
+      style_losses.append(style_loss)
+      i = i + 1
+
+    return tf.math.reduce_sum(style_loss)
+
+
+class VGGPerceptualLoss(PerceptualLoss):
+  """Perceptual loss based on VGG19 pretrained on the ImageNet dataset.
+
+  Reference:
+  - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](
+      https://arxiv.org/abs/1603.08155) (ECCV 2016)
+
+  Perceptual loss measures high-level perceptual and semantic differences
+  between images.
+  """
+
+  def __init__(
+      self,
+      loss_weight: Optional[PerceptualLossWeight] = None,
+  ):
+    """Initializes image quality loss essentials.
+
+    Args:
+      loss_weight: Loss weight coefficients.
+    """
+    super().__init__(
+        feature_weight=np.array([0.1, 0.1, 1.0, 1.0, 1.0]),
+        loss_weight=loss_weight,
+    )
+
+    rgb_mean = tf.constant([0.485, 0.456, 0.406])
+    rgb_std = tf.constant([0.229, 0.224, 0.225])
+
+    self._rgb_mean = tf.reshape(rgb_mean, (1, 1, 1, 3))
+    self._rgb_std = tf.reshape(rgb_std, (1, 1, 1, 3))
+
+    model_path = file_util.DownloadedFiles(
+        'vgg_feature_extractor',
+        _VGG_IMAGENET_PERCEPTUAL_MODEL_URL,
+        is_folder=True,
+    )
+    self._vgg19 = model_util.load_keras_model(model_path.get_path())
+
+  def _compute_features(self, img: tf.Tensor) -> Sequence[tf.Tensor]:
+    """Computes VGG19 features."""
+    img = (img + 1) / 2.0
+    norm_img = (img - self._rgb_mean) / self._rgb_std
+    # no grad, as it only serves as a frozen feature extractor.
+    return self._vgg19(norm_img)
+
+
+def _compute_gram_matrix(feature: tf.Tensor) -> tf.Tensor:
+  """Computes gram matrix for the feature map.
+
+  Args:
+    feature: [B, H, W, C] feature map.
+
+  Returns:
+    [B, C, C] gram matrix.
+  """
+  h, w, c = feature.shape[1:].as_list()
+  feat_reshaped = tf.reshape(feature, shape=(-1, h * w, c))
+  feat_gram = tf.matmul(
+      tf.transpose(feat_reshaped, perm=[0, 2, 1]), feat_reshaped
+  )
+  return feat_gram / (c * h * w)

mediapipe/model_maker/python/core/utils/loss_functions_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,7 +13,9 @@
 # limitations under the License.
 
 import math
-from typing import Optional
+import tempfile
+from typing import Dict, Optional, Sequence
+from unittest import mock as unittest_mock
 
 from absl.testing import parameterized
 import tensorflow as tf
@@ -21,7 +23,7 @@ import tensorflow as tf
 from mediapipe.model_maker.python.core.utils import loss_functions
 
 
-class LossFunctionsTest(tf.test.TestCase, parameterized.TestCase):
+class FocalLossTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.named_parameters(
       dict(testcase_name='no_sample_weight', sample_weight=None),
@@ -99,5 +101,228 @@ class LossFunctionsTest(tf.test.TestCase, parameterized.TestCase):
     self.assertNear(loss, expected_loss, 1e-4)
 
 
+class MockPerceptualLoss(loss_functions.PerceptualLoss):
+  """A mock class with implementation of abstract methods for testing."""
+
+  def __init__(
+      self,
+      use_mock_loss_op: bool = False,
+      feature_weight: Optional[Sequence[float]] = None,
+      loss_weight: Optional[loss_functions.PerceptualLossWeight] = None,
+  ):
+    super().__init__(feature_weight=feature_weight, loss_weight=loss_weight)
+    if use_mock_loss_op:
+      self._loss_op = lambda x, y: tf.math.reduce_mean(x - y)
+
+  def _compute_features(self, img: tf.Tensor) -> Sequence[tf.Tensor]:
+    return [tf.random.normal(shape=(1, 8, 8, 3))] * 5
+
+
+class PerceptualLossTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self._img1 = tf.fill(dims=(8, 8), value=0.2)
+    self._img2 = tf.fill(dims=(8, 8), value=0.8)
+
+  def test_invalid_feature_weight_raise_value_error(self):
+    with self.assertRaisesRegex(
+        ValueError,
+        'Input feature weight length 2 is smaller than feature length 5',
+    ):
+      MockPerceptualLoss(feature_weight=[1.0, 2.0])(
+          img1=self._img1, img2=self._img2
+      )
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='default_loss_weight_and_loss_op',
+          use_mock_loss_op=False,
+          feature_weight=None,
+          loss_weight=None,
+          loss_values={
+              'style_loss': 0.032839,
+              'content_loss': 5.639870,
+          },
+      ),
+      dict(
+          testcase_name='style_loss_weight_is_0_default_loss_op',
+          use_mock_loss_op=False,
+          feature_weight=None,
+          loss_weight=loss_functions.PerceptualLossWeight(style=0),
+          loss_values={
+              'style_loss': 0,
+              'content_loss': 5.639870,
+          },
+      ),
+      dict(
+          testcase_name='content_loss_weight_is_0_default_loss_op',
+          use_mock_loss_op=False,
+          feature_weight=None,
+          loss_weight=loss_functions.PerceptualLossWeight(content=0),
+          loss_values={
+              'style_loss': 0.032839,
+              'content_loss': 0,
+          },
+      ),
+      dict(
+          testcase_name='customized_loss_weight_default_loss_op',
+          use_mock_loss_op=False,
+          feature_weight=None,
+          loss_weight=loss_functions.PerceptualLossWeight(
+              style=1.0, content=2.0
+          ),
+          loss_values={'style_loss': 0.032839, 'content_loss': 11.279739},
+      ),
+      dict(
+          testcase_name=(
+              'customized_feature_weight_and_loss_weight_default_loss_op'
+          ),
+          use_mock_loss_op=False,
+          feature_weight=[1.0, 2.0, 3.0, 4.0, 5.0],
+          loss_weight=loss_functions.PerceptualLossWeight(
+              style=1.0, content=2.0
+          ),
+          loss_values={'style_loss': 0.164193, 'content_loss': 33.839218},
+      ),
+      dict(
+          testcase_name='no_loss_change_if_extra_feature_weight_provided',
+          use_mock_loss_op=False,
+          feature_weight=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
+          loss_weight=loss_functions.PerceptualLossWeight(
+              style=1.0, content=2.0
+          ),
+          loss_values={
+              'style_loss': 0.164193,
+              'content_loss': 33.839218,
+          },
+      ),
+      dict(
+          testcase_name='customized_loss_weight_custom_loss_op',
+          use_mock_loss_op=True,
+          feature_weight=None,
+          loss_weight=loss_functions.PerceptualLossWeight(
+              style=1.0, content=2.0
+          ),
+          loss_values={'style_loss': 0.000395, 'content_loss': -1.533469},
+      ),
+  )
+  def test_weighted_perceptul_loss(
+      self,
+      use_mock_loss_op: bool,
+      feature_weight: Sequence[float],
+      loss_weight: loss_functions.PerceptualLossWeight,
+      loss_values: Dict[str, float],
+  ):
+    perceptual_loss = MockPerceptualLoss(
+        use_mock_loss_op=use_mock_loss_op,
+        feature_weight=feature_weight,
+        loss_weight=loss_weight,
+    )
+    loss = perceptual_loss(img1=self._img1, img2=self._img2)
+    self.assertEqual(list(loss.keys()), ['style_loss', 'content_loss'])
+    self.assertNear(loss['style_loss'], loss_values['style_loss'], 1e-4)
+    self.assertNear(loss['content_loss'], loss_values['content_loss'], 1e-4)
+
+
+class VGGPerceptualLossTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    # Mock tempfile.gettempdir() to be unique for each test to avoid race
+    # condition when downloading model since these tests may run in parallel.
+    mock_gettempdir = unittest_mock.patch.object(
+        tempfile,
+        'gettempdir',
+        return_value=self.create_tempdir(),
+        autospec=True,
+    )
+    self.mock_gettempdir = mock_gettempdir.start()
+    self.addCleanup(mock_gettempdir.stop)
+    self._img1 = tf.fill(dims=(1, 256, 256, 3), value=0.1)
+    self._img2 = tf.fill(dims=(1, 256, 256, 3), value=0.9)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='default_loss_weight',
+          loss_weight=None,
+          loss_values={
+              'style_loss': 5.8363257e-06,
+              'content_loss': 1.7016045,
+          },
+      ),
+      dict(
+          testcase_name='customized_loss_weight',
+          loss_weight=loss_functions.PerceptualLossWeight(
+              style=10.0, content=20.0
+          ),
+          loss_values={
+              'style_loss': 5.8363257e-05,
+              'content_loss': 34.03208,
+          },
+      ),
+  )
+  def test_vgg_perceptual_loss(self, loss_weight, loss_values):
+    vgg_loss = loss_functions.VGGPerceptualLoss(loss_weight=loss_weight)
+    loss = vgg_loss(img1=self._img1, img2=self._img2)
+    self.assertEqual(list(loss.keys()), ['style_loss', 'content_loss'])
+    self.assertNear(
+        loss['style_loss'],
+        loss_values['style_loss'],
+        loss_values['style_loss'] / 1e5,
+    )
+    self.assertNear(
+        loss['content_loss'],
+        loss_values['content_loss'],
+        loss_values['content_loss'] / 1e5,
+    )
+
+
+class ImagePerceptualQualityLossTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    # Mock tempfile.gettempdir() to be unique for each test to avoid race
+    # condition when downloading model since these tests may run in parallel.
+    mock_gettempdir = unittest_mock.patch.object(
+        tempfile,
+        'gettempdir',
+        return_value=self.create_tempdir(),
+        autospec=True,
+    )
+    self.mock_gettempdir = mock_gettempdir.start()
+    self.addCleanup(mock_gettempdir.stop)
+    self._img1 = tf.fill(dims=(1, 256, 256, 3), value=0.1)
+    self._img2 = tf.fill(dims=(1, 256, 256, 3), value=0.9)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='default_loss_weight',
+          loss_weight=None,
+          loss_value=2.501612,
+      ),
+      dict(
+          testcase_name='customized_loss_weight_zero_l1',
+          loss_weight=loss_functions.PerceptualLossWeight(
+              l1=0.0, style=10.0, content=20.0
+          ),
+          loss_value=34.032139,
+      ),
+      dict(
+          testcase_name='customized_loss_weight_nonzero_l1',
+          loss_weight=loss_functions.PerceptualLossWeight(
+              l1=10.0, style=10.0, content=20.0
+          ),
+          loss_value=42.032139,
+      ),
+  )
+  def test_image_perceptual_quality_loss(self, loss_weight, loss_value):
+    image_quality_loss = loss_functions.ImagePerceptualQualityLoss(
+        loss_weight=loss_weight
+    )
+    loss = image_quality_loss(img1=self._img1, img2=self._img2)
+    self.assertNear(loss, loss_value, 1e-4)
+
+
 if __name__ == '__main__':
   tf.test.main()

mediapipe/model_maker/python/core/utils/model_util.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/model_util_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/quantization.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/quantization_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/core/utils/test_util.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,7 +16,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from typing import List, Union
+from typing import Sequence
+from typing import Dict, List, Union
 
 # Dependency imports
 
@@ -94,6 +95,17 @@ def is_same_output(tflite_model: bytearray,
   return np.allclose(lite_output, keras_output, atol=atol)
 
 
+def run_tflite(
+    tflite_filename: str,
+    input_tensors: Union[List[tf.Tensor], Dict[str, tf.Tensor]],
+) -> Union[Sequence[tf.Tensor], tf.Tensor]:
+  """Runs TFLite model inference."""
+  with tf.io.gfile.GFile(tflite_filename, "rb") as f:
+    tflite_model = f.read()
+  lite_runner = model_util.get_lite_runner(tflite_model)
+  return lite_runner.run(input_tensors)
+
+
 def test_tflite(keras_model: tf.keras.Model,
                 tflite_model: bytearray,
                 size: Union[int, List[int]],

mediapipe/model_maker/python/core/utils/testdata/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/core/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/core/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/core/bert_model_options.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/core/bert_model_spec.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/text_classifier/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/text_classifier/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/text_classifier/dataset.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/text_classifier/dataset_test.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mediapipe/model_maker/python/text/text_classifier/model_options.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+# Copyright 2022 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.