From 0fd78c2ec66dda15e5acc2ae14c15347f6b86e40 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:28:24 -0800 Subject: [PATCH 01/21] Updated classifier python tasks to use the new classification_result dataclass --- .../tasks/python/components/containers/BUILD | 11 +- .../containers/classification_result.py | 31 ++++ .../components/containers/classifications.py | 168 ------------------ mediapipe/tasks/python/test/text/BUILD | 2 +- .../python/test/text/text_classifier_test.py | 136 +++++++------- mediapipe/tasks/python/test/vision/BUILD | 2 +- .../test/vision/image_classifier_test.py | 151 +++++++--------- mediapipe/tasks/python/text/BUILD | 2 +- .../tasks/python/text/text_classifier.py | 19 +- mediapipe/tasks/python/vision/BUILD | 2 +- .../tasks/python/vision/image_classifier.py | 47 ++--- 11 files changed, 191 insertions(+), 380 deletions(-) delete mode 100644 mediapipe/tasks/python/components/containers/classifications.py diff --git a/mediapipe/tasks/python/components/containers/BUILD b/mediapipe/tasks/python/components/containers/BUILD index 9e0a90911..b02021acd 100644 --- a/mediapipe/tasks/python/components/containers/BUILD +++ b/mediapipe/tasks/python/components/containers/BUILD @@ -85,21 +85,12 @@ py_library( ], ) -py_library( - name = "classifications", - srcs = ["classifications.py"], - deps = [ - ":category", - "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", - "//mediapipe/tasks/python/core:optional_dependencies", - ], -) - py_library( name = "classification_result", srcs = ["classification_result.py"], deps = [ ":category", + "//mediapipe/framework/formats:classification_py_pb2", "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", "//mediapipe/tasks/python/core:optional_dependencies", ], diff --git a/mediapipe/tasks/python/components/containers/classification_result.py b/mediapipe/tasks/python/components/containers/classification_result.py index cc25fc708..20dc6991a 100644 --- a/mediapipe/tasks/python/components/containers/classification_result.py +++ b/mediapipe/tasks/python/components/containers/classification_result.py @@ -16,10 +16,13 @@ import dataclasses from typing import List, Optional +from mediapipe.framework.formats import classification_pb2 from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 from mediapipe.tasks.python.components.containers import category as category_module from mediapipe.tasks.python.core.optional_dependencies import doc_controls +_ClassificationProto = classification_pb2.Classification +_ClassificationListProto = classification_pb2.ClassificationList _ClassificationsProto = classifications_pb2.Classifications _ClassificationResultProto = classifications_pb2.ClassificationResult @@ -41,6 +44,24 @@ class Classifications: head_index: int head_name: Optional[str] = None + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassificationsProto: + """Generates a Classifications protobuf object.""" + classification_list_proto = _ClassificationListProto() + classification_list_proto.classification.extend([ + _ClassificationProto( + index=category.index, + score=category.score, + label=category.category_name, + display_name=category.display_name + ) + for category in self.categories + ]) + return _ClassificationsProto( + classification_list=classification_list_proto, + head_index=self.head_index, + head_name=self.head_name) + @classmethod @doc_controls.do_not_generate_docs def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications': @@ -78,6 +99,16 @@ class ClassificationResult: classifications: List[Classifications] timestamp_ms: Optional[int] = None + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassificationResultProto: + """Generates a ClassificationResult protobuf object.""" + return _ClassificationResultProto( + classifications=[ + classification.to_pb2() + for classification in self.classifications + ], + timestamp_ms=self.timestamp_ms) + @classmethod @doc_controls.do_not_generate_docs def create_from_pb2( diff --git a/mediapipe/tasks/python/components/containers/classifications.py b/mediapipe/tasks/python/components/containers/classifications.py deleted file mode 100644 index 90ab22614..000000000 --- a/mediapipe/tasks/python/components/containers/classifications.py +++ /dev/null @@ -1,168 +0,0 @@ -# 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. -"""Classifications data class.""" - -import dataclasses -from typing import Any, List, Optional - -from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 -from mediapipe.tasks.python.components.containers import category as category_module -from mediapipe.tasks.python.core.optional_dependencies import doc_controls - -_ClassificationEntryProto = classifications_pb2.ClassificationEntry -_ClassificationsProto = classifications_pb2.Classifications -_ClassificationResultProto = classifications_pb2.ClassificationResult - - -@dataclasses.dataclass -class ClassificationEntry: - """List of predicted classes (aka labels) for a given classifier head. - - Attributes: - categories: The array of predicted categories, usually sorted by descending - scores (e.g. from high to low probability). - timestamp_ms: The optional timestamp (in milliseconds) associated to the - classification entry. This is useful for time series use cases, e.g., - audio classification. - """ - - categories: List[category_module.Category] - timestamp_ms: Optional[int] = None - - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _ClassificationEntryProto: - """Generates a ClassificationEntry protobuf object.""" - return _ClassificationEntryProto( - categories=[category.to_pb2() for category in self.categories], - timestamp_ms=self.timestamp_ms) - - @classmethod - @doc_controls.do_not_generate_docs - def create_from_pb2( - cls, pb2_obj: _ClassificationEntryProto) -> 'ClassificationEntry': - """Creates a `ClassificationEntry` object from the given protobuf object.""" - return ClassificationEntry( - categories=[ - category_module.Category.create_from_pb2(category) - for category in pb2_obj.categories - ], - timestamp_ms=pb2_obj.timestamp_ms) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - - Args: - other: The object to be compared with. - - Returns: - True if the objects are equal. - """ - if not isinstance(other, ClassificationEntry): - return False - - return self.to_pb2().__eq__(other.to_pb2()) - - -@dataclasses.dataclass -class Classifications: - """Represents the classifications for a given classifier head. - - Attributes: - entries: A list of `ClassificationEntry` objects. - head_index: The index of the classifier head these categories refer to. This - is useful for multi-head models. - head_name: The name of the classifier head, which is the corresponding - tensor metadata name. - """ - - entries: List[ClassificationEntry] - head_index: int - head_name: str - - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _ClassificationsProto: - """Generates a Classifications protobuf object.""" - return _ClassificationsProto( - entries=[entry.to_pb2() for entry in self.entries], - head_index=self.head_index, - head_name=self.head_name) - - @classmethod - @doc_controls.do_not_generate_docs - def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications': - """Creates a `Classifications` object from the given protobuf object.""" - return Classifications( - entries=[ - ClassificationEntry.create_from_pb2(entry) - for entry in pb2_obj.entries - ], - head_index=pb2_obj.head_index, - head_name=pb2_obj.head_name) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - - Args: - other: The object to be compared with. - - Returns: - True if the objects are equal. - """ - if not isinstance(other, Classifications): - return False - - return self.to_pb2().__eq__(other.to_pb2()) - - -@dataclasses.dataclass -class ClassificationResult: - """Contains one set of results per classifier head. - - Attributes: - classifications: A list of `Classifications` objects. - """ - - classifications: List[Classifications] - - @doc_controls.do_not_generate_docs - def to_pb2(self) -> _ClassificationResultProto: - """Generates a ClassificationResult protobuf object.""" - return _ClassificationResultProto(classifications=[ - classification.to_pb2() for classification in self.classifications - ]) - - @classmethod - @doc_controls.do_not_generate_docs - def create_from_pb2( - cls, pb2_obj: _ClassificationResultProto) -> 'ClassificationResult': - """Creates a `ClassificationResult` object from the given protobuf object. - """ - return ClassificationResult(classifications=[ - Classifications.create_from_pb2(classification) - for classification in pb2_obj.classifications - ]) - - def __eq__(self, other: Any) -> bool: - """Checks if this object is equal to the given object. - - Args: - other: The object to be compared with. - - Returns: - True if the objects are equal. - """ - if not isinstance(other, ClassificationResult): - return False - - return self.to_pb2().__eq__(other.to_pb2()) diff --git a/mediapipe/tasks/python/test/text/BUILD b/mediapipe/tasks/python/test/text/BUILD index d7176b0a5..f12c20bc4 100644 --- a/mediapipe/tasks/python/test/text/BUILD +++ b/mediapipe/tasks/python/test/text/BUILD @@ -27,7 +27,7 @@ py_test( ], deps = [ "//mediapipe/tasks/python/components/containers:category", - "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:classification_result", "//mediapipe/tasks/python/components/processors:classifier_options", "//mediapipe/tasks/python/core:base_options", "//mediapipe/tasks/python/test:test_utils", diff --git a/mediapipe/tasks/python/test/text/text_classifier_test.py b/mediapipe/tasks/python/test/text/text_classifier_test.py index c93def48e..d9221e503 100644 --- a/mediapipe/tasks/python/test/text/text_classifier_test.py +++ b/mediapipe/tasks/python/test/text/text_classifier_test.py @@ -20,18 +20,17 @@ from absl.testing import absltest from absl.testing import parameterized from mediapipe.tasks.python.components.containers import category -from mediapipe.tasks.python.components.containers import classifications as classifications_module +from mediapipe.tasks.python.components.containers import classification_result as classification_result_module from mediapipe.tasks.python.components.processors import classifier_options from mediapipe.tasks.python.core import base_options as base_options_module from mediapipe.tasks.python.test import test_utils from mediapipe.tasks.python.text import text_classifier +TextClassifierResult = classification_result_module.ClassificationResult _BaseOptions = base_options_module.BaseOptions _ClassifierOptions = classifier_options.ClassifierOptions _Category = category.Category -_ClassificationEntry = classifications_module.ClassificationEntry -_Classifications = classifications_module.Classifications -_TextClassifierResult = classifications_module.ClassificationResult +_Classifications = classification_result_module.Classifications _TextClassifier = text_classifier.TextClassifier _TextClassifierOptions = text_classifier.TextClassifierOptions @@ -43,90 +42,75 @@ _NEGATIVE_TEXT = 'What a waste of my time.' _POSITIVE_TEXT = ('This is the best movie I’ve seen in recent years.' 'Strongly recommend it!') -_BERT_NEGATIVE_RESULTS = _TextClassifierResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=0, - score=0.999479, - display_name='', - category_name='negative'), - _Category( - index=1, - score=0.00052154, - display_name='', - category_name='positive') - ], - timestamp_ms=0) + +_BERT_NEGATIVE_RESULTS = TextClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=0, + score=0.999479, + display_name='', + category_name='negative'), + _Category( + index=1, + score=0.00052154, + display_name='', + category_name='positive') ], head_index=0, head_name='probability') -]) -_BERT_POSITIVE_RESULTS = _TextClassifierResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=1, - score=0.999466, - display_name='', - category_name='positive'), - _Category( - index=0, - score=0.000533596, - display_name='', - category_name='negative') - ], - timestamp_ms=0) + ], + timestamp_ms=0) +_BERT_POSITIVE_RESULTS = TextClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=1, + score=0.999466, + display_name='', + category_name='positive'), + _Category( + index=0, + score=0.000533596, + display_name='', + category_name='negative') ], head_index=0, head_name='probability') -]) -_REGEX_NEGATIVE_RESULTS = _TextClassifierResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=0, - score=0.81313, - display_name='', - category_name='Negative'), - _Category( - index=1, - score=0.1868704, - display_name='', - category_name='Positive') - ], - timestamp_ms=0) + ], + timestamp_ms=0) +_REGEX_NEGATIVE_RESULTS = TextClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=0, + score=0.81313, + display_name='', + category_name='Negative'), + _Category( + index=1, + score=0.1868704, + display_name='', + category_name='Positive') ], head_index=0, head_name='probability') -]) -_REGEX_POSITIVE_RESULTS = _TextClassifierResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=1, - score=0.5134273, - display_name='', - category_name='Positive'), - _Category( - index=0, - score=0.486573, - display_name='', - category_name='Negative') - ], - timestamp_ms=0) + ], + timestamp_ms=0) +_REGEX_POSITIVE_RESULTS = TextClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=1, + score=0.5134273, + display_name='', + category_name='Positive'), + _Category( + index=0, + score=0.486573, + display_name='', + category_name='Negative') ], head_index=0, head_name='probability') -]) + ], + timestamp_ms=0) class ModelFileType(enum.Enum): diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index c29648160..1ea5f5b7d 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -47,7 +47,7 @@ py_test( deps = [ "//mediapipe/python:_framework_bindings", "//mediapipe/tasks/python/components/containers:category", - "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:classification_result", "//mediapipe/tasks/python/components/containers:rect", "//mediapipe/tasks/python/components/processors:classifier_options", "//mediapipe/tasks/python/core:base_options", diff --git a/mediapipe/tasks/python/test/vision/image_classifier_test.py b/mediapipe/tasks/python/test/vision/image_classifier_test.py index 11941ce23..1f4d39ec7 100644 --- a/mediapipe/tasks/python/test/vision/image_classifier_test.py +++ b/mediapipe/tasks/python/test/vision/image_classifier_test.py @@ -23,8 +23,8 @@ from absl.testing import parameterized import numpy as np from mediapipe.python._framework_bindings import image -from mediapipe.tasks.python.components.containers import category -from mediapipe.tasks.python.components.containers import classifications as classifications_module +from mediapipe.tasks.python.components.containers import category as category_module +from mediapipe.tasks.python.components.containers import classification_result as classification_result_module from mediapipe.tasks.python.components.containers import rect from mediapipe.tasks.python.components.processors import classifier_options from mediapipe.tasks.python.core import base_options as base_options_module @@ -33,13 +33,12 @@ from mediapipe.tasks.python.vision import image_classifier from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module from mediapipe.tasks.python.vision.core import vision_task_running_mode +ImageClassifierResult = classification_result_module.ClassificationResult _Rect = rect.Rect _BaseOptions = base_options_module.BaseOptions _ClassifierOptions = classifier_options.ClassifierOptions -_Category = category.Category -_ClassificationEntry = classifications_module.ClassificationEntry -_Classifications = classifications_module.Classifications -_ClassificationResult = classifications_module.ClassificationResult +_Category = category_module.Category +_Classifications = classification_result_module.Classifications _Image = image.Image _ImageClassifier = image_classifier.ImageClassifier _ImageClassifierOptions = image_classifier.ImageClassifierOptions @@ -55,68 +54,56 @@ _MAX_RESULTS = 3 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision' -def _generate_empty_results(timestamp_ms: int) -> _ClassificationResult: - return _ClassificationResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry(categories=[], timestamp_ms=timestamp_ms) +def _generate_empty_results() -> ImageClassifierResult: + return ImageClassifierResult(classifications=[ + _Classifications(categories=[], head_index=0, head_name='probability') + ], + timestamp_ms=0) + + +def _generate_burger_results() -> ImageClassifierResult: + return ImageClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=934, + score=0.793959, + display_name='', + category_name='cheeseburger'), + _Category( + index=932, + score=0.0273929, + display_name='', + category_name='bagel'), + _Category( + index=925, + score=0.0193408, + display_name='', + category_name='guacamole'), + _Category( + index=963, + score=0.00632786, + display_name='', + category_name='meat loaf') ], head_index=0, head_name='probability') - ]) + ], + timestamp_ms=0) -def _generate_burger_results(timestamp_ms: int) -> _ClassificationResult: - return _ClassificationResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=934, - score=0.793959, - display_name='', - category_name='cheeseburger'), - _Category( - index=932, - score=0.0273929, - display_name='', - category_name='bagel'), - _Category( - index=925, - score=0.0193408, - display_name='', - category_name='guacamole'), - _Category( - index=963, - score=0.00632786, - display_name='', - category_name='meat loaf') - ], - timestamp_ms=timestamp_ms) +def _generate_soccer_ball_results() -> ImageClassifierResult: + return ImageClassifierResult(classifications=[ + _Classifications(categories=[ + _Category( + index=806, + score=0.996527, + display_name='', + category_name='soccer ball') ], head_index=0, head_name='probability') - ]) - - -def _generate_soccer_ball_results(timestamp_ms: int) -> _ClassificationResult: - return _ClassificationResult(classifications=[ - _Classifications( - entries=[ - _ClassificationEntry( - categories=[ - _Category( - index=806, - score=0.996527, - display_name='', - category_name='soccer ball') - ], - timestamp_ms=timestamp_ms) - ], - head_index=0, - head_name='probability') - ]) + ], + timestamp_ms=0) class ModelFileType(enum.Enum): @@ -165,8 +152,8 @@ class ImageClassifierTest(parameterized.TestCase): self.assertIsInstance(classifier, _ImageClassifier) @parameterized.parameters( - (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)), - (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0))) + (ModelFileType.FILE_NAME, 4, _generate_burger_results()), + (ModelFileType.FILE_CONTENT, 4, _generate_burger_results())) def test_classify(self, model_file_type, max_results, expected_classification_result): # Creates classifier. @@ -195,8 +182,8 @@ class ImageClassifierTest(parameterized.TestCase): classifier.close() @parameterized.parameters( - (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)), - (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0))) + (ModelFileType.FILE_NAME, 4, _generate_burger_results()), + (ModelFileType.FILE_CONTENT, 4, _generate_burger_results())) def test_classify_in_context(self, model_file_type, max_results, expected_classification_result): if model_file_type is ModelFileType.FILE_NAME: @@ -236,7 +223,7 @@ class ImageClassifierTest(parameterized.TestCase): image_result = classifier.classify(test_image, image_processing_options) # Comparing results. test_utils.assert_proto_equals(self, image_result.to_pb2(), - _generate_soccer_ball_results(0).to_pb2()) + _generate_soccer_ball_results().to_pb2()) def test_score_threshold_option(self): custom_classifier_options = _ClassifierOptions( @@ -250,8 +237,8 @@ class ImageClassifierTest(parameterized.TestCase): classifications = image_result.classifications for classification in classifications: - for entry in classification.entries: - score = entry.categories[0].score + for category in classification.categories: + score = category.score self.assertGreaterEqual( score, _SCORE_THRESHOLD, f'Classification with score lower than threshold found. ' @@ -266,7 +253,7 @@ class ImageClassifierTest(parameterized.TestCase): with _ImageClassifier.create_from_options(options) as classifier: # Performs image classification on the input. image_result = classifier.classify(self.test_image) - categories = image_result.classifications[0].entries[0].categories + categories = image_result.classifications[0].categories self.assertLessEqual( len(categories), _MAX_RESULTS, 'Too many results returned.') @@ -283,8 +270,8 @@ class ImageClassifierTest(parameterized.TestCase): classifications = image_result.classifications for classification in classifications: - for entry in classification.entries: - label = entry.categories[0].category_name + for category in classification.categories: + label = category.category_name self.assertIn(label, _ALLOW_LIST, f'Label {label} found but not in label allow list') @@ -299,8 +286,8 @@ class ImageClassifierTest(parameterized.TestCase): classifications = image_result.classifications for classification in classifications: - for entry in classification.entries: - label = entry.categories[0].category_name + for category in classification.categories: + label = category.category_name self.assertNotIn(label, _DENY_LIST, f'Label {label} found but in deny list.') @@ -326,7 +313,7 @@ class ImageClassifierTest(parameterized.TestCase): with _ImageClassifier.create_from_options(options) as classifier: # Performs image classification on the input. image_result = classifier.classify(self.test_image) - self.assertEmpty(image_result.classifications[0].entries[0].categories) + self.assertEmpty(image_result.classifications[0].categories) def test_missing_result_callback(self): options = _ImageClassifierOptions( @@ -406,7 +393,7 @@ class ImageClassifierTest(parameterized.TestCase): self.test_image, timestamp) test_utils.assert_proto_equals( self, classification_result.to_pb2(), - _generate_burger_results(timestamp).to_pb2()) + _generate_burger_results().to_pb2()) def test_classify_for_video_succeeds_with_region_of_interest(self): custom_classifier_options = _ClassifierOptions(max_results=1) @@ -427,7 +414,7 @@ class ImageClassifierTest(parameterized.TestCase): test_image, timestamp, image_processing_options) test_utils.assert_proto_equals( self, classification_result.to_pb2(), - _generate_soccer_ball_results(timestamp).to_pb2()) + _generate_soccer_ball_results().to_pb2()) def test_calling_classify_in_live_stream_mode(self): options = _ImageClassifierOptions( @@ -462,15 +449,15 @@ class ImageClassifierTest(parameterized.TestCase): ValueError, r'Input timestamp must be monotonically increasing'): classifier.classify_async(self.test_image, 0) - @parameterized.parameters((0, _generate_burger_results), - (1, _generate_empty_results)) - def test_classify_async_calls(self, threshold, expected_result_fn): + @parameterized.parameters((0, _generate_burger_results()), + (1, _generate_empty_results())) + def test_classify_async_calls(self, threshold, expected_result): observed_timestamp_ms = -1 - def check_result(result: _ClassificationResult, output_image: _Image, + def check_result(result: ImageClassifierResult, output_image: _Image, timestamp_ms: int): test_utils.assert_proto_equals(self, result.to_pb2(), - expected_result_fn(timestamp_ms).to_pb2()) + expected_result.to_pb2()) self.assertTrue( np.array_equal(output_image.numpy_view(), self.test_image.numpy_view())) @@ -498,11 +485,11 @@ class ImageClassifierTest(parameterized.TestCase): image_processing_options = _ImageProcessingOptions(roi) observed_timestamp_ms = -1 - def check_result(result: _ClassificationResult, output_image: _Image, + def check_result(result: ImageClassifierResult, output_image: _Image, timestamp_ms: int): test_utils.assert_proto_equals( self, result.to_pb2(), - _generate_soccer_ball_results(timestamp_ms).to_pb2()) + _generate_soccer_ball_results().to_pb2()) self.assertEqual(output_image.width, test_image.width) self.assertEqual(output_image.height, test_image.height) self.assertLess(observed_timestamp_ms, timestamp_ms) diff --git a/mediapipe/tasks/python/text/BUILD b/mediapipe/tasks/python/text/BUILD index fd5d701b4..c7278ecb8 100644 --- a/mediapipe/tasks/python/text/BUILD +++ b/mediapipe/tasks/python/text/BUILD @@ -28,7 +28,7 @@ py_library( "//mediapipe/python:packet_getter", "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_py_pb2", - "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:classification_result", "//mediapipe/tasks/python/components/processors:classifier_options", "//mediapipe/tasks/python/core:base_options", "//mediapipe/tasks/python/core:optional_dependencies", diff --git a/mediapipe/tasks/python/text/text_classifier.py b/mediapipe/tasks/python/text/text_classifier.py index 1e230ee20..d4259d121 100644 --- a/mediapipe/tasks/python/text/text_classifier.py +++ b/mediapipe/tasks/python/text/text_classifier.py @@ -19,21 +19,21 @@ from mediapipe.python import packet_creator from mediapipe.python import packet_getter from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2 -from mediapipe.tasks.python.components.containers import classifications +from mediapipe.tasks.python.components.containers import classification_result as classification_result_module from mediapipe.tasks.python.components.processors import classifier_options from mediapipe.tasks.python.core import base_options as base_options_module from mediapipe.tasks.python.core import task_info as task_info_module from mediapipe.tasks.python.core.optional_dependencies import doc_controls from mediapipe.tasks.python.text.core import base_text_task_api -TextClassifierResult = classifications.ClassificationResult +TextClassifierResult = classification_result_module.ClassificationResult _BaseOptions = base_options_module.BaseOptions _TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions _ClassifierOptions = classifier_options.ClassifierOptions _TaskInfo = task_info_module.TaskInfo -_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out' -_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT' +_CLASSIFICATIONS_STREAM_NAME = 'classifications_out' +_CLASSIFICATIONS_TAG = 'CLASSIFICATIONS' _TEXT_IN_STREAM_NAME = 'text_in' _TEXT_TAG = 'TEXT' _TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph' @@ -105,8 +105,8 @@ class TextClassifier(base_text_task_api.BaseTextTaskApi): input_streams=[':'.join([_TEXT_TAG, _TEXT_IN_STREAM_NAME])], output_streams=[ ':'.join([ - _CLASSIFICATION_RESULT_TAG, - _CLASSIFICATION_RESULT_OUT_STREAM_NAME + _CLASSIFICATIONS_TAG, + _CLASSIFICATIONS_STREAM_NAME ]) ], task_options=options) @@ -132,9 +132,6 @@ class TextClassifier(base_text_task_api.BaseTextTaskApi): classification_result_proto = classifications_pb2.ClassificationResult() classification_result_proto.CopyFrom( packet_getter.get_proto( - output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + output_packets[_CLASSIFICATIONS_STREAM_NAME])) - return TextClassifierResult([ - classifications.Classifications.create_from_pb2(classification) - for classification in classification_result_proto.classifications - ]) + return TextClassifierResult.create_from_pb2(classification_result_proto) diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index 527c6d883..9b4e0ef1b 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -48,7 +48,7 @@ py_library( "//mediapipe/python:packet_getter", "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_py_pb2", - "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:classification_result", "//mediapipe/tasks/python/components/containers:rect", "//mediapipe/tasks/python/components/processors:classifier_options", "//mediapipe/tasks/python/core:base_options", diff --git a/mediapipe/tasks/python/vision/image_classifier.py b/mediapipe/tasks/python/vision/image_classifier.py index 02819ddf1..377d3fdcb 100644 --- a/mediapipe/tasks/python/vision/image_classifier.py +++ b/mediapipe/tasks/python/vision/image_classifier.py @@ -23,7 +23,7 @@ from mediapipe.python._framework_bindings import image as image_module from mediapipe.python._framework_bindings import packet from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2 -from mediapipe.tasks.python.components.containers import classifications +from mediapipe.tasks.python.components.containers import classification_result as classification_result_module from mediapipe.tasks.python.components.containers import rect from mediapipe.tasks.python.components.processors import classifier_options from mediapipe.tasks.python.core import base_options as base_options_module @@ -33,6 +33,7 @@ from mediapipe.tasks.python.vision.core import base_vision_task_api from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module from mediapipe.tasks.python.vision.core import vision_task_running_mode +ImageClassifierResult = classification_result_module.ClassificationResult _NormalizedRect = rect.NormalizedRect _BaseOptions = base_options_module.BaseOptions _ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions @@ -41,8 +42,8 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions _TaskInfo = task_info_module.TaskInfo -_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out' -_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT' +_CLASSIFICATIONS_STREAM_NAME = 'classifications_out' +_CLASSIFICATIONS_TAG = 'CLASSIFICATIONS' _IMAGE_IN_STREAM_NAME = 'image_in' _IMAGE_OUT_STREAM_NAME = 'image_out' _IMAGE_TAG = 'IMAGE' @@ -72,7 +73,7 @@ class ImageClassifierOptions: running_mode: _RunningMode = _RunningMode.IMAGE classifier_options: _ClassifierOptions = _ClassifierOptions() result_callback: Optional[ - Callable[[classifications.ClassificationResult, image_module.Image, int], + Callable[[ImageClassifierResult, image_module.Image, int], None]] = None @doc_controls.do_not_generate_docs @@ -137,17 +138,13 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): classification_result_proto = classifications_pb2.ClassificationResult() classification_result_proto.CopyFrom( - packet_getter.get_proto( - output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) - - classification_result = classifications.ClassificationResult([ - classifications.Classifications.create_from_pb2(classification) - for classification in classification_result_proto.classifications - ]) + packet_getter.get_proto(output_packets[_CLASSIFICATIONS_STREAM_NAME])) image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME]) timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp - options.result_callback(classification_result, image, - timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) + options.result_callback( + ImageClassifierResult.create_from_pb2(classification_result_proto), + image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) task_info = _TaskInfo( task_graph=_TASK_GRAPH_NAME, @@ -157,8 +154,8 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): ], output_streams=[ ':'.join([ - _CLASSIFICATION_RESULT_TAG, - _CLASSIFICATION_RESULT_OUT_STREAM_NAME + _CLASSIFICATIONS_TAG, + _CLASSIFICATIONS_STREAM_NAME ]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]) ], task_options=options) @@ -172,7 +169,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): self, image: image_module.Image, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> classifications.ClassificationResult: + ) -> ImageClassifierResult: """Performs image classification on the provided MediaPipe Image. Args: @@ -196,20 +193,16 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): classification_result_proto = classifications_pb2.ClassificationResult() classification_result_proto.CopyFrom( - packet_getter.get_proto( - output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + packet_getter.get_proto(output_packets[_CLASSIFICATIONS_STREAM_NAME])) - return classifications.ClassificationResult([ - classifications.Classifications.create_from_pb2(classification) - for classification in classification_result_proto.classifications - ]) + return ImageClassifierResult.create_from_pb2(classification_result_proto) def classify_for_video( self, image: image_module.Image, timestamp_ms: int, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> classifications.ClassificationResult: + ) -> ImageClassifierResult: """Performs image classification on the provided video frames. Only use this method when the ImageClassifier is created with the video @@ -241,13 +234,9 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): classification_result_proto = classifications_pb2.ClassificationResult() classification_result_proto.CopyFrom( - packet_getter.get_proto( - output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + packet_getter.get_proto(output_packets[_CLASSIFICATIONS_STREAM_NAME])) - return classifications.ClassificationResult([ - classifications.Classifications.create_from_pb2(classification) - for classification in classification_result_proto.classifications - ]) + return ImageClassifierResult.create_from_pb2(classification_result_proto) def classify_async( self, From 72f08c6878f5ada7573fbd802638f52019f14f4b Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:57:12 -0800 Subject: [PATCH 02/21] Used loops instead of list comprehension --- .../containers/classification_result.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/mediapipe/tasks/python/components/containers/classification_result.py b/mediapipe/tasks/python/components/containers/classification_result.py index 20dc6991a..aee4f0655 100644 --- a/mediapipe/tasks/python/components/containers/classification_result.py +++ b/mediapipe/tasks/python/components/containers/classification_result.py @@ -48,15 +48,13 @@ class Classifications: def to_pb2(self) -> _ClassificationsProto: """Generates a Classifications protobuf object.""" classification_list_proto = _ClassificationListProto() - classification_list_proto.classification.extend([ - _ClassificationProto( - index=category.index, - score=category.score, - label=category.category_name, - display_name=category.display_name - ) - for category in self.categories - ]) + for category in self.categories: + classification_proto = _ClassificationProto( + index=category.index, + score=category.score, + label=category.category_name, + display_name=category.display_name) + classification_list_proto.classification.append(classification_proto) return _ClassificationsProto( classification_list=classification_list_proto, head_index=self.head_index, From 0b12aa943585a39ea8cc4a7edb5d84f78d4b2ded Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 01:30:55 -0800 Subject: [PATCH 03/21] Internal change PiperOrigin-RevId: 487466061 --- .../tasks/web/audio/audio_classifier/BUILD | 2 +- .../audio_classifier/audio_classifier.ts | 40 +++++++------- .../audio_classifier_result.ts | 2 +- .../tasks/web/components/containers/BUILD | 4 +- ...ations.d.ts => classification_result.d.ts} | 36 +++++++------ .../tasks/web/components/processors/BUILD | 3 +- .../processors/classifier_result.ts | 54 +++++++++---------- .../tasks/web/text/text_classifier/BUILD | 2 +- .../text/text_classifier/text_classifier.ts | 34 +++++------- .../text_classifier/text_classifier_result.ts | 2 +- .../tasks/web/vision/image_classifier/BUILD | 2 +- .../image_classifier/image_classifier.ts | 36 +++++-------- .../image_classifier_result.ts | 2 +- 13 files changed, 103 insertions(+), 116 deletions(-) rename mediapipe/tasks/web/components/containers/{classifications.d.ts => classification_result.d.ts} (61%) diff --git a/mediapipe/tasks/web/audio/audio_classifier/BUILD b/mediapipe/tasks/web/audio/audio_classifier/BUILD index bc3048df1..1bc4af309 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/BUILD +++ b/mediapipe/tasks/web/audio/audio_classifier/BUILD @@ -21,7 +21,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_jspb_proto", "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts index 3d3ca5ae7..e3700cd7a 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts +++ b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts @@ -27,7 +27,7 @@ import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm // Placeholder for internal dependency on trusted resource url import {AudioClassifierOptions} from './audio_classifier_options'; -import {Classifications} from './audio_classifier_result'; +import {AudioClassifierResult} from './audio_classifier_result'; const MEDIAPIPE_GRAPH = 'mediapipe.tasks.audio.audio_classifier.AudioClassifierGraph'; @@ -38,14 +38,14 @@ const MEDIAPIPE_GRAPH = // implementation const AUDIO_STREAM = 'input_audio'; const SAMPLE_RATE_STREAM = 'sample_rate'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result'; +const TIMESTAMPED_CLASSIFICATIONS_STREAM = 'timestamped_classifications'; // The OSS JS API does not support the builder pattern. // tslint:disable:jspb-use-builder-pattern /** Performs audio classification. */ export class AudioClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResults: AudioClassifierResult[] = []; private defaultSampleRate = 48000; private readonly options = new AudioClassifierGraphOptions(); @@ -150,7 +150,8 @@ export class AudioClassifier extends TaskRunner { * `48000` if no custom default was set. * @return The classification result of the audio datas */ - classify(audioData: Float32Array, sampleRate?: number): Classifications[] { + classify(audioData: Float32Array, sampleRate?: number): + AudioClassifierResult[] { sampleRate = sampleRate ?? this.defaultSampleRate; // Configures the number of samples in the WASM layer. We re-configure the @@ -164,20 +165,22 @@ export class AudioClassifier extends TaskRunner { this.addDoubleToStream(sampleRate, SAMPLE_RATE_STREAM, timestamp); this.addAudioToStream(audioData, timestamp); - this.classifications = []; + this.classificationResults = []; this.finishProcessing(); - return [...this.classifications]; + return [...this.classificationResults]; } /** - * Internal function for converting raw data into a classification, and - * adding it to our classfications list. + * Internal function for converting raw data into classification results, and + * adding them to our classfication results list. **/ - private addJsAudioClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + private addJsAudioClassificationResults(binaryProtos: Uint8Array[]): void { + binaryProtos.forEach(binaryProto => { + const classificationResult = + ClassificationResult.deserializeBinary(binaryProto); + this.classificationResults.push( + convertFromClassificationResultProto(classificationResult)); + }); } /** Updates the MediaPipe graph configuration. */ @@ -185,7 +188,7 @@ export class AudioClassifier extends TaskRunner { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(AUDIO_STREAM); graphConfig.addInputStream(SAMPLE_RATE_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(TIMESTAMPED_CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -198,14 +201,15 @@ export class AudioClassifier extends TaskRunner { classifierNode.addInputStream('AUDIO:' + AUDIO_STREAM); classifierNode.addInputStream('SAMPLE_RATE:' + SAMPLE_RATE_STREAM); classifierNode.addOutputStream( - 'CLASSIFICATIONS:' + CLASSIFICATION_RESULT_STREAM); + 'TIMESTAMPED_CLASSIFICATIONS:' + TIMESTAMPED_CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsAudioClassification(binaryProto); - }); + this.attachProtoVectorListener( + TIMESTAMPED_CLASSIFICATIONS_STREAM, binaryProtos => { + this.addJsAudioClassificationResults(binaryProtos); + }); const binaryGraph = graphConfig.serializeBinary(); this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true); diff --git a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts index 0a51dee04..0b616126a 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts +++ b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as AudioClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; diff --git a/mediapipe/tasks/web/components/containers/BUILD b/mediapipe/tasks/web/components/containers/BUILD index 7d13fadcb..1b0e403ff 100644 --- a/mediapipe/tasks/web/components/containers/BUILD +++ b/mediapipe/tasks/web/components/containers/BUILD @@ -10,8 +10,8 @@ mediapipe_ts_library( ) mediapipe_ts_library( - name = "classifications", - srcs = ["classifications.d.ts"], + name = "classification_result", + srcs = ["classification_result.d.ts"], deps = [":category"], ) diff --git a/mediapipe/tasks/web/components/containers/classifications.d.ts b/mediapipe/tasks/web/components/containers/classification_result.d.ts similarity index 61% rename from mediapipe/tasks/web/components/containers/classifications.d.ts rename to mediapipe/tasks/web/components/containers/classification_result.d.ts index 67a259bbe..33632f925 100644 --- a/mediapipe/tasks/web/components/containers/classifications.d.ts +++ b/mediapipe/tasks/web/components/containers/classification_result.d.ts @@ -16,27 +16,14 @@ import {Category} from '../../../../tasks/web/components/containers/category'; -/** List of predicted categories with an optional timestamp. */ -export interface ClassificationEntry { +/** Classification results for a given classifier head. */ +export interface Classifications { /** * The array of predicted categories, usually sorted by descending scores, * e.g., from high to low probability. */ categories: Category[]; - /** - * The optional timestamp (in milliseconds) associated to the classification - * entry. This is useful for time series use cases, e.g., audio - * classification. - */ - timestampMs?: number; -} - -/** Classifications for a given classifier head. */ -export interface Classifications { - /** A list of classification entries. */ - entries: ClassificationEntry[]; - /** * The index of the classifier head these categories refer to. This is * useful for multi-head models. @@ -45,7 +32,24 @@ export interface Classifications { /** * The name of the classifier head, which is the corresponding tensor - * metadata name. + * metadata name. Defaults to an empty string if there is no such metadata. */ headName: string; } + +/** Classification results of a model. */ +export interface ClassificationResult { + /** The classification results for each head of the model. */ + classifications: Classifications[]; + + /** + * The optional timestamp (in milliseconds) of the start of the chunk of data + * corresponding to these results. + * + * This is only used for classification on time series (e.g. audio + * classification). In these use cases, the amount of data to process might + * exceed the maximum size that the model can process: to solve this, the + * input data is split into multiple chunks starting at different timestamps. + */ + timestampMs?: number; +} diff --git a/mediapipe/tasks/web/components/processors/BUILD b/mediapipe/tasks/web/components/processors/BUILD index cd7190dd9..e0d84b632 100644 --- a/mediapipe/tasks/web/components/processors/BUILD +++ b/mediapipe/tasks/web/components/processors/BUILD @@ -17,8 +17,9 @@ mediapipe_ts_library( name = "classifier_result", srcs = ["classifier_result.ts"], deps = [ + "//mediapipe/framework/formats:classification_jspb_proto", "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", ], ) diff --git a/mediapipe/tasks/web/components/processors/classifier_result.ts b/mediapipe/tasks/web/components/processors/classifier_result.ts index ade967932..90d10b84d 100644 --- a/mediapipe/tasks/web/components/processors/classifier_result.ts +++ b/mediapipe/tasks/web/components/processors/classifier_result.ts @@ -14,48 +14,46 @@ * limitations under the License. */ -import {ClassificationEntry as ClassificationEntryProto, ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb'; -import {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +import {ClassificationResult as ClassificationResultProto, Classifications as ClassificationsProto} from '../../../../tasks/cc/components/containers/proto/classifications_pb'; +import {ClassificationResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; const DEFAULT_INDEX = -1; const DEFAULT_SCORE = 0.0; /** - * Converts a ClassificationEntry proto to the ClassificationEntry result - * type. + * Converts a Classifications proto to a Classifications object. */ -function convertFromClassificationEntryProto(source: ClassificationEntryProto): - ClassificationEntry { - const categories = source.getCategoriesList().map(category => { - return { - index: category.getIndex() ?? DEFAULT_INDEX, - score: category.getScore() ?? DEFAULT_SCORE, - displayName: category.getDisplayName() ?? '', - categoryName: category.getCategoryName() ?? '', - }; - }); - +function convertFromClassificationsProto(source: ClassificationsProto): + Classifications { + const categories = + source.getClassificationList()?.getClassificationList().map( + classification => { + return { + index: classification.getIndex() ?? DEFAULT_INDEX, + score: classification.getScore() ?? DEFAULT_SCORE, + categoryName: classification.getLabel() ?? '', + displayName: classification.getDisplayName() ?? '', + }; + }) ?? + []; return { categories, - timestampMs: source.getTimestampMs(), + headIndex: source.getHeadIndex() ?? DEFAULT_INDEX, + headName: source.getHeadName() ?? '', }; } /** - * Converts a ClassificationResult proto to a list of classifications. + * Converts a ClassificationResult proto to a ClassificationResult object. */ export function convertFromClassificationResultProto( - classificationResult: ClassificationResult) : Classifications[] { - const result: Classifications[] = []; - for (const classificationsProto of - classificationResult.getClassificationsList()) { - const classifications: Classifications = { - entries: classificationsProto.getEntriesList().map( - entry => convertFromClassificationEntryProto(entry)), - headIndex: classificationsProto.getHeadIndex() ?? DEFAULT_INDEX, - headName: classificationsProto.getHeadName() ?? '', - }; - result.push(classifications); + source: ClassificationResultProto): ClassificationResult { + const result: ClassificationResult = { + classifications: source.getClassificationsList().map( + classififications => convertFromClassificationsProto(classififications)) + }; + if (source.hasTimestampMs()) { + result.timestampMs = source.getTimestampMs(); } return result; } diff --git a/mediapipe/tasks/web/text/text_classifier/BUILD b/mediapipe/tasks/web/text/text_classifier/BUILD index 25a8817d4..4ebdce18a 100644 --- a/mediapipe/tasks/web/text/text_classifier/BUILD +++ b/mediapipe/tasks/web/text/text_classifier/BUILD @@ -22,7 +22,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/text/text_classifier/text_classifier.ts b/mediapipe/tasks/web/text/text_classifier/text_classifier.ts index d92248b80..e1d0c9601 100644 --- a/mediapipe/tasks/web/text/text_classifier/text_classifier.ts +++ b/mediapipe/tasks/web/text/text_classifier/text_classifier.ts @@ -27,10 +27,10 @@ import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm // Placeholder for internal dependency on trusted resource url import {TextClassifierOptions} from './text_classifier_options'; -import {Classifications} from './text_classifier_result'; +import {TextClassifierResult} from './text_classifier_result'; const INPUT_STREAM = 'text_in'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result_out'; +const CLASSIFICATIONS_STREAM = 'classifications_out'; const TEXT_CLASSIFIER_GRAPH = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'; @@ -39,7 +39,7 @@ const TEXT_CLASSIFIER_GRAPH = /** Performs Natural Language classification. */ export class TextClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResult: TextClassifierResult = {classifications: []}; private readonly options = new TextClassifierGraphOptions(); /** @@ -129,30 +129,20 @@ export class TextClassifier extends TaskRunner { * @param text The text to process. * @return The classification result of the text */ - classify(text: string): Classifications[] { - // Get classification classes by running our MediaPipe graph. - this.classifications = []; + classify(text: string): TextClassifierResult { + // Get classification result by running our MediaPipe graph. + this.classificationResult = {classifications: []}; this.addStringToStream( text, INPUT_STREAM, /* timestamp= */ performance.now()); this.finishProcessing(); - return [...this.classifications]; - } - - // Internal function for converting raw data into a classification, and - // adding it to our classifications list. - private addJsTextClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - console.log(classificationResult.toObject()); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + return this.classificationResult; } /** Updates the MediaPipe graph configuration. */ private refreshGraph(): void { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(INPUT_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -161,14 +151,14 @@ export class TextClassifier extends TaskRunner { const classifierNode = new CalculatorGraphConfig.Node(); classifierNode.setCalculator(TEXT_CLASSIFIER_GRAPH); classifierNode.addInputStream('TEXT:' + INPUT_STREAM); - classifierNode.addOutputStream( - 'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM); + classifierNode.addOutputStream('CLASSIFICATIONS:' + CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsTextClassification(binaryProto); + this.attachProtoListener(CLASSIFICATIONS_STREAM, binaryProto => { + this.classificationResult = convertFromClassificationResultProto( + ClassificationResult.deserializeBinary(binaryProto)); }); const binaryGraph = graphConfig.serializeBinary(); diff --git a/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts b/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts index 0a51dee04..707ba5da2 100644 --- a/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts +++ b/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as TextClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; diff --git a/mediapipe/tasks/web/vision/image_classifier/BUILD b/mediapipe/tasks/web/vision/image_classifier/BUILD index 6937dc4f3..e96d6a8e3 100644 --- a/mediapipe/tasks/web/vision/image_classifier/BUILD +++ b/mediapipe/tasks/web/vision/image_classifier/BUILD @@ -21,7 +21,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts b/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts index cb63874c4..ba4b6c907 100644 --- a/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts +++ b/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts @@ -27,12 +27,12 @@ import {createMediaPipeLib, FileLocator, ImageSource} from '../../../../web/grap // Placeholder for internal dependency on trusted resource url import {ImageClassifierOptions} from './image_classifier_options'; -import {Classifications} from './image_classifier_result'; +import {ImageClassifierResult} from './image_classifier_result'; const IMAGE_CLASSIFIER_GRAPH = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'; const INPUT_STREAM = 'input_image'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result'; +const CLASSIFICATIONS_STREAM = 'classifications'; export {ImageSource}; // Used in the public API @@ -41,7 +41,7 @@ export {ImageSource}; // Used in the public API /** Performs classification on images. */ export class ImageClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResult: ImageClassifierResult = {classifications: []}; private readonly options = new ImageClassifierGraphOptions(); /** @@ -133,31 +133,21 @@ export class ImageClassifier extends TaskRunner { * provided, defaults to `performance.now()`. * @return The classification result of the image */ - classify(imageSource: ImageSource, timestamp?: number): Classifications[] { - // Get classification classes by running our MediaPipe graph. - this.classifications = []; + classify(imageSource: ImageSource, timestamp?: number): + ImageClassifierResult { + // Get classification result by running our MediaPipe graph. + this.classificationResult = {classifications: []}; this.addGpuBufferAsImageToStream( imageSource, INPUT_STREAM, timestamp ?? performance.now()); this.finishProcessing(); - return [...this.classifications]; - } - - /** - * Internal function for converting raw data into a classification, and - * adding it to our classfications list. - **/ - private addJsImageClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + return this.classificationResult; } /** Updates the MediaPipe graph configuration. */ private refreshGraph(): void { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(INPUT_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -168,14 +158,14 @@ export class ImageClassifier extends TaskRunner { const classifierNode = new CalculatorGraphConfig.Node(); classifierNode.setCalculator(IMAGE_CLASSIFIER_GRAPH); classifierNode.addInputStream('IMAGE:' + INPUT_STREAM); - classifierNode.addOutputStream( - 'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM); + classifierNode.addOutputStream('CLASSIFICATIONS:' + CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsImageClassification(binaryProto); + this.attachProtoListener(CLASSIFICATIONS_STREAM, binaryProto => { + this.classificationResult = convertFromClassificationResultProto( + ClassificationResult.deserializeBinary(binaryProto)); }); const binaryGraph = graphConfig.serializeBinary(); diff --git a/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts b/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts index 0a51dee04..44032234c 100644 --- a/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts +++ b/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as ImageClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; From 2ce3a9719e773e3bbc6754cf1f41ae9789210944 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 09:25:38 -0800 Subject: [PATCH 04/21] Remove the problematic packet release calls in OutputHandler::run. OutputHandler::run is installed as a MultiStreamCallback into the TaskRunner and it's invoked in the Graph::CallbackToJava() jni method. In CallbackToJava(), the packets are removed in RemovePacket() calls after the multi stream callback is invoked. The current logic lets the MultiStreamCallback release the packets first and leaves null pointers to RemovePacket() to remove, which may cause the app crash. The fix is to remove the packet release calls in the MultiStreamCallback and always leave the packet release work to RemovePacket(). PiperOrigin-RevId: 487558809 --- .../java/com/google/mediapipe/tasks/core/OutputHandler.java | 6 ------ 1 file changed, 6 deletions(-) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java index 6a83c7296..d18f0e41e 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java @@ -119,12 +119,6 @@ public class OutputHandler { } else { Log.e(TAG, "Error occurs when getting MediaPipe task result. " + e); } - } finally { - for (Packet packet : packets) { - if (packet != null) { - packet.release(); - } - } } } } From da4d455d0cb8acaf860abc40371456a498c448d0 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 10:08:02 -0800 Subject: [PATCH 05/21] Implement MediaPipe Tasks AudioClassifier Java API. PiperOrigin-RevId: 487570148 --- .../audio_classifier_graph_options.proto | 3 + .../com/google/mediapipe/tasks/audio/BUILD | 76 ++++ .../audio/audioclassifier/AndroidManifest.xml | 8 + .../audioclassifier/AudioClassifier.java | 399 ++++++++++++++++++ .../AudioClassifierResult.java | 76 ++++ .../tasks/audio/core/BaseAudioTaskApi.java | 151 +++++++ .../tasks/audio/core/RunningMode.java | 29 ++ .../components/containers/AudioData.java | 334 +++++++++++++++ .../tasks/components/containers/BUILD | 9 + .../mediapipe/tasks/core/OutputHandler.java | 21 +- .../mediapipe/tasks/core/TaskRunner.java | 1 + 11 files changed, 1106 insertions(+), 1 deletion(-) create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AndroidManifest.xml create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifierResult.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/BaseAudioTaskApi.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/RunningMode.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/AudioData.java diff --git a/mediapipe/tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options.proto b/mediapipe/tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options.proto index 16aa86aeb..5d4ba3296 100644 --- a/mediapipe/tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options.proto +++ b/mediapipe/tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options.proto @@ -21,6 +21,9 @@ import "mediapipe/framework/calculator.proto"; import "mediapipe/tasks/cc/components/processors/proto/classifier_options.proto"; import "mediapipe/tasks/cc/core/proto/base_options.proto"; +option java_package = "com.google.mediapipe.tasks.audio.audioclassifier.proto"; +option java_outer_classname = "AudioClassifierGraphOptionsProto"; + message AudioClassifierGraphOptions { extend mediapipe.CalculatorOptions { optional AudioClassifierGraphOptions ext = 451755788; diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD new file mode 100644 index 000000000..c5cb6f8a3 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD @@ -0,0 +1,76 @@ +# 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. + +licenses(["notice"]) + +package(default_visibility = ["//mediapipe/tasks:internal"]) + +android_library( + name = "core", + srcs = glob(["core/*.java"]), + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + deps = [ + ":libmediapipe_tasks_audio_jni_lib", + "//mediapipe/java/com/google/mediapipe/framework:android_framework_no_mff", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audio_data", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/core", + "@maven//:com_google_guava_guava", + ], +) + +# The native library of all MediaPipe audio tasks. +cc_binary( + name = "libmediapipe_tasks_audio_jni.so", + linkshared = 1, + linkstatic = 1, + deps = [ + "//mediapipe/java/com/google/mediapipe/framework/jni:mediapipe_framework_jni", + "//mediapipe/tasks/cc/audio/audio_classifier:audio_classifier_graph", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/core/jni:model_resources_cache_jni", + ], +) + +cc_library( + name = "libmediapipe_tasks_audio_jni_lib", + srcs = [":libmediapipe_tasks_audio_jni.so"], + alwayslink = 1, +) + +android_library( + name = "audioclassifier", + srcs = [ + "audioclassifier/AudioClassifier.java", + "audioclassifier/AudioClassifierResult.java", + ], + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + manifest = "audioclassifier/AndroidManifest.xml", + deps = [ + ":core", + "//mediapipe/framework:calculator_options_java_proto_lite", + "//mediapipe/java/com/google/mediapipe/framework:android_framework", + "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_java_proto_lite", + "//mediapipe/tasks/cc/components/containers/proto:classifications_java_proto_lite", + "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audio_data", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:classificationresult", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors:classifieroptions", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/core", + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AndroidManifest.xml b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AndroidManifest.xml new file mode 100644 index 000000000..1e9817efd --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AndroidManifest.xml @@ -0,0 +1,8 @@ + + + + + + diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java new file mode 100644 index 000000000..88b6daf0a --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java @@ -0,0 +1,399 @@ +// 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. + +package com.google.mediapipe.tasks.audio.audioclassifier; + +import android.content.Context; +import android.os.ParcelFileDescriptor; +import com.google.auto.value.AutoValue; +import com.google.mediapipe.proto.CalculatorOptionsProto.CalculatorOptions; +import com.google.mediapipe.framework.MediaPipeException; +import com.google.mediapipe.framework.Packet; +import com.google.mediapipe.framework.PacketGetter; +import com.google.mediapipe.framework.ProtoUtil; +import com.google.mediapipe.tasks.audio.audioclassifier.proto.AudioClassifierGraphOptionsProto; +import com.google.mediapipe.tasks.audio.core.BaseAudioTaskApi; +import com.google.mediapipe.tasks.audio.core.RunningMode; +import com.google.mediapipe.tasks.components.containers.AudioData; +import com.google.mediapipe.tasks.components.containers.proto.ClassificationsProto; +import com.google.mediapipe.tasks.components.processors.ClassifierOptions; +import com.google.mediapipe.tasks.core.BaseOptions; +import com.google.mediapipe.tasks.core.ErrorListener; +import com.google.mediapipe.tasks.core.OutputHandler; +import com.google.mediapipe.tasks.core.OutputHandler.PureResultListener; +import com.google.mediapipe.tasks.core.OutputHandler.ResultListener; +import com.google.mediapipe.tasks.core.TaskInfo; +import com.google.mediapipe.tasks.core.TaskOptions; +import com.google.mediapipe.tasks.core.TaskRunner; +import com.google.mediapipe.tasks.core.proto.BaseOptionsProto; +import java.io.File; +import java.io.IOException; +import java.nio.ByteBuffer; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; +import java.util.Optional; + +/** + * Performs audio classification on audio clips or audio stream. + * + *

This API expects a TFLite model with mandatory TFLite Model Metadata that contains the + * mandatory AudioProperties of the solo input audio tensor and the optional (but recommended) label + * items as AssociatedFiles with type TENSOR_AXIS_LABELS per output classification tensor. + * + *

Input tensor: (kTfLiteFloat32) + * + *

    + *
  • input audio buffer of size `[batch * samples]`. + *
  • batch inference is not supported (`batch` is required to be 1). + *
  • for multi-channel models, the channels need be interleaved. + *
+ * + *

At least one output tensor with: (kTfLiteFloat32) + * + *

    + *
  • `[1 x N]` array with `N` represents the number of categories. + *
  • optional (but recommended) label items as AssociatedFiles with type TENSOR_AXIS_LABELS, + * containing one label per line. The first such AssociatedFile (if any) is used to fill the + * `category_name` field of the results. The `display_name` field is filled from the + * AssociatedFile (if any) whose locale matches the `display_names_locale` field of the + * `AudioClassifierOptions` used at creation time ("en" by default, i.e. English). If none of + * these are available, only the `index` field of the results will be filled. + *
+ */ +public final class AudioClassifier extends BaseAudioTaskApi { + private static final String TAG = AudioClassifier.class.getSimpleName(); + private static final String AUDIO_IN_STREAM_NAME = "audio_in"; + private static final String SAMPLE_RATE_IN_STREAM_NAME = "sample_rate_in"; + private static final List INPUT_STREAMS = + Collections.unmodifiableList( + Arrays.asList( + "AUDIO:" + AUDIO_IN_STREAM_NAME, "SAMPLE_RATE:" + SAMPLE_RATE_IN_STREAM_NAME)); + private static final List OUTPUT_STREAMS = + Collections.unmodifiableList( + Arrays.asList( + "CLASSIFICATIONS:classifications_out", + "TIMESTAMPED_CLASSIFICATIONS:timestamped_classifications_out")); + private static final int CLASSIFICATIONS_OUT_STREAM_INDEX = 0; + private static final int TIMESTAMPED_CLASSIFICATIONS_OUT_STREAM_INDEX = 1; + private static final String TASK_GRAPH_NAME = + "mediapipe.tasks.audio.audio_classifier.AudioClassifierGraph"; + private static final long MICROSECONDS_PER_MILLISECOND = 1000; + + static { + ProtoUtil.registerTypeName( + ClassificationsProto.ClassificationResult.class, + "mediapipe.tasks.components.containers.proto.ClassificationResult"); + } + + /** + * Creates an {@link AudioClassifier} instance from a model file and default {@link + * AudioClassifierOptions}. + * + * @param context an Android {@link Context}. + * @param modelPath path to the classification model in the assets. + * @throws MediaPipeException if there is an error during {@link AudioClassifier} creation. + */ + public static AudioClassifier createFromFile(Context context, String modelPath) { + BaseOptions baseOptions = BaseOptions.builder().setModelAssetPath(modelPath).build(); + return createFromOptions( + context, AudioClassifierOptions.builder().setBaseOptions(baseOptions).build()); + } + + /** + * Creates an {@link AudioClassifier} instance from a model file and default {@link + * AudioClassifierOptions}. + * + * @param context an Android {@link Context}. + * @param modelFile the classification model {@link File} instance. + * @throws IOException if an I/O error occurs when opening the tflite model file. + * @throws MediaPipeException if there is an error during {@link AudioClassifier} creation. + */ + public static AudioClassifier createFromFile(Context context, File modelFile) throws IOException { + try (ParcelFileDescriptor descriptor = + ParcelFileDescriptor.open(modelFile, ParcelFileDescriptor.MODE_READ_ONLY)) { + BaseOptions baseOptions = + BaseOptions.builder().setModelAssetFileDescriptor(descriptor.getFd()).build(); + return createFromOptions( + context, AudioClassifierOptions.builder().setBaseOptions(baseOptions).build()); + } + } + + /** + * Creates an {@link AudioClassifier} instance from a model buffer and default {@link + * AudioClassifierOptions}. + * + * @param context an Android {@link Context}. + * @param modelBuffer a direct {@link ByteBuffer} or a {@link MappedByteBuffer} of the + * classification model. + * @throws MediaPipeException if there is an error during {@link AudioClassifier} creation. + */ + public static AudioClassifier createFromBuffer(Context context, final ByteBuffer modelBuffer) { + BaseOptions baseOptions = BaseOptions.builder().setModelAssetBuffer(modelBuffer).build(); + return createFromOptions( + context, AudioClassifierOptions.builder().setBaseOptions(baseOptions).build()); + } + + /** + * Creates an {@link AudioClassifier} instance from an {@link AudioClassifierOptions} instance. + * + * @param context an Android {@link Context}. + * @param options an {@link AudioClassifierOptions} instance. + * @throws MediaPipeException if there is an error during {@link AudioClassifier} creation. + */ + public static AudioClassifier createFromOptions(Context context, AudioClassifierOptions options) { + OutputHandler handler = new OutputHandler<>(); + handler.setOutputPacketConverter( + new OutputHandler.OutputPacketConverter() { + @Override + public AudioClassifierResult convertToTaskResult(List packets) { + try { + if (!packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX).isEmpty()) { + // For audio stream mode. + return AudioClassifierResult.createFromProto( + PacketGetter.getProto( + packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX), + ClassificationsProto.ClassificationResult.getDefaultInstance()), + packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX).getTimestamp() + / MICROSECONDS_PER_MILLISECOND); + } else { + // For audio clips mode. + return AudioClassifierResult.createFromProtoList( + PacketGetter.getProtoVector( + packets.get(TIMESTAMPED_CLASSIFICATIONS_OUT_STREAM_INDEX), + ClassificationsProto.ClassificationResult.parser()), + -1); + } + } catch (IOException e) { + throw new MediaPipeException( + MediaPipeException.StatusCode.INTERNAL.ordinal(), e.getMessage()); + } + } + + @Override + public Void convertToTaskInput(List packets) { + return null; + } + }); + if (options.resultListener().isPresent()) { + ResultListener resultListener = + new ResultListener<>() { + @Override + public void run(AudioClassifierResult audioClassifierResult, Void input) { + options.resultListener().get().run(audioClassifierResult); + } + }; + handler.setResultListener(resultListener); + } + options.errorListener().ifPresent(handler::setErrorListener); + // Audio tasks should not drop input audio due to flow limiting, which may cause data + // inconsistency. + TaskRunner runner = + TaskRunner.create( + context, + TaskInfo.builder() + .setTaskGraphName(TASK_GRAPH_NAME) + .setInputStreams(INPUT_STREAMS) + .setOutputStreams(OUTPUT_STREAMS) + .setTaskOptions(options) + .setEnableFlowLimiting(false) + .build(), + handler); + return new AudioClassifier(runner, options.runningMode()); + } + + /** + * Constructor to initialize an {@link AudioClassifier} from a {@link TaskRunner} and {@link + * RunningMode}. + * + * @param taskRunner a {@link TaskRunner}. + * @param runningMode a mediapipe audio task {@link RunningMode}. + */ + private AudioClassifier(TaskRunner taskRunner, RunningMode runningMode) { + super(taskRunner, runningMode, AUDIO_IN_STREAM_NAME, SAMPLE_RATE_IN_STREAM_NAME); + } + + /* + * Performs audio classification on the provided audio clip. Only use this method when the + * AudioClassifier is created with the audio clips mode. + * + *

The audio clip is represented as a MediaPipe {@link AudioData} object The method accepts + * audio clips with various length and audio sample rate. It's required to provide the + * corresponding audio sample rate within the {@link AudioData} object. + * + *

The input audio clip may be longer than what the model is able to process in a single + * inference. When this occurs, the input audio clip is split into multiple chunks starting at + * different timestamps. For this reason, this function returns a vector of ClassificationResult + * objects, each associated with a timestamp corresponding to the start (in milliseconds) of the + * chunk data that was classified, e.g: + * + * ClassificationResult #0 (first chunk of data): + * timestamp_ms: 0 (starts at 0ms) + * classifications #0 (single head model): + * category #0: + * category_name: "Speech" + * score: 0.6 + * category #1: + * category_name: "Music" + * score: 0.2 + * ClassificationResult #1 (second chunk of data): + * timestamp_ms: 800 (starts at 800ms) + * classifications #0 (single head model): + * category #0: + * category_name: "Speech" + * score: 0.5 + * category #1: + * category_name: "Silence" + * score: 0.1 + * + * @param audioClip a MediaPipe {@link AudioData} object for processing. + * @throws MediaPipeException if there is an internal error. + */ + public AudioClassifierResult classify(AudioData audioClip) { + return (AudioClassifierResult) processAudioClip(audioClip); + } + + /* + * Sends audio data (a block in a continuous audio stream) to perform audio classification. Only + * use this method when the AudioClassifier is created with the audio stream mode. + * + *

The audio block is represented as a MediaPipe {@link AudioData} object. The audio data will + * be resampled, accumulated, and framed to the proper size for the underlying model to consume. + * It's required to provide the corresponding audio sample rate within {@link AudioData} object as + * well as a timestamp (in milliseconds) to indicate the start time of the input audio block. The + * timestamps must be monotonically increasing. This method will return immediately after + * the input audio data is accepted. The results will be available in the `resultListener` + * provided in the `AudioClassifierOptions`. The `classifyAsync` method is designed to process + * auido stream data such as microphone input. + * + *

The input audio block may be longer than what the model is able to process in a single + * inference. When this occurs, the input audio block is split into multiple chunks. For this + * reason, the callback may be called multiple times (once per chunk) for each call to this + * function. + * + * @param audioBlock a MediaPipe {@link AudioData} object for processing. + * @param timestampMs the input timestamp (in milliseconds). + * @throws MediaPipeException if there is an internal error. + */ + public void classifyAsync(AudioData audioBlock, long timestampMs) { + checkOrSetSampleRate(audioBlock.getFormat().getSampleRate()); + sendAudioStreamData(audioBlock, timestampMs); + } + + /** Options for setting up and {@link AudioClassifier}. */ + @AutoValue + public abstract static class AudioClassifierOptions extends TaskOptions { + + /** Builder for {@link AudioClassifierOptions}. */ + @AutoValue.Builder + public abstract static class Builder { + /** Sets the {@link BaseOptions} for the audio classifier task. */ + public abstract Builder setBaseOptions(BaseOptions baseOptions); + + /** + * Sets the {@link RunningMode} for the audio classifier task. Default to the audio clips + * mode. Image classifier has two modes: + * + *

    + *
  • AUDIO_CLIPS: The mode for running audio classification on audio clips. Users feed + * audio clips to the `classify` method, and will receive the classification results as + * the return value. + *
  • AUDIO_STREAM: The mode for running audio classification on the audio stream, such as + * from microphone. Users call `classifyAsync` to push the audio data into the + * AudioClassifier, the classification results will be available in the result callback + * when the audio classifier finishes the work. + *
+ */ + public abstract Builder setRunningMode(RunningMode runningMode); + + /** + * Sets the optional {@link ClassifierOptions} controling classification behavior, such as + * score threshold, number of results, etc. + */ + public abstract Builder setClassifierOptions(ClassifierOptions classifierOptions); + + /** + * Sets the {@link ResultListener} to receive the classification results asynchronously when + * the audio classifier is in the audio stream mode. + */ + public abstract Builder setResultListener( + PureResultListener resultListener); + + /** Sets an optional {@link ErrorListener}. */ + public abstract Builder setErrorListener(ErrorListener errorListener); + + abstract AudioClassifierOptions autoBuild(); + + /** + * Validates and builds the {@link AudioClassifierOptions} instance. + * + * @throws IllegalArgumentException if the result listener and the running mode are not + * properly configured. The result listener should only be set when the audio classifier + * is in the audio stream mode. + */ + public final AudioClassifierOptions build() { + AudioClassifierOptions options = autoBuild(); + if (options.runningMode() == RunningMode.AUDIO_STREAM) { + if (!options.resultListener().isPresent()) { + throw new IllegalArgumentException( + "The audio classifier is in the audio stream mode, a user-defined result listener" + + " must be provided in the AudioClassifierOptions."); + } + } else if (options.resultListener().isPresent()) { + throw new IllegalArgumentException( + "The audio classifier is in the audio clips mode, a user-defined result listener" + + " shouldn't be provided in AudioClassifierOptions."); + } + return options; + } + } + + abstract BaseOptions baseOptions(); + + abstract RunningMode runningMode(); + + abstract Optional classifierOptions(); + + abstract Optional> resultListener(); + + abstract Optional errorListener(); + + public static Builder builder() { + return new AutoValue_AudioClassifier_AudioClassifierOptions.Builder() + .setRunningMode(RunningMode.AUDIO_CLIPS); + } + + /** + * Converts a {@link AudioClassifierOptions} to a {@link CalculatorOptions} protobuf message. + */ + @Override + public CalculatorOptions convertToCalculatorOptionsProto() { + BaseOptionsProto.BaseOptions.Builder baseOptionsBuilder = + BaseOptionsProto.BaseOptions.newBuilder(); + baseOptionsBuilder.setUseStreamMode(runningMode() == RunningMode.AUDIO_STREAM); + baseOptionsBuilder.mergeFrom(convertBaseOptionsToProto(baseOptions())); + AudioClassifierGraphOptionsProto.AudioClassifierGraphOptions.Builder taskOptionsBuilder = + AudioClassifierGraphOptionsProto.AudioClassifierGraphOptions.newBuilder() + .setBaseOptions(baseOptionsBuilder); + if (classifierOptions().isPresent()) { + taskOptionsBuilder.setClassifierOptions(classifierOptions().get().convertToProto()); + } + return CalculatorOptions.newBuilder() + .setExtension( + AudioClassifierGraphOptionsProto.AudioClassifierGraphOptions.ext, + taskOptionsBuilder.build()) + .build(); + } + } +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifierResult.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifierResult.java new file mode 100644 index 000000000..fcc3c6e22 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifierResult.java @@ -0,0 +1,76 @@ +// 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. + +package com.google.mediapipe.tasks.audio.audioclassifier; + +import com.google.auto.value.AutoValue; +import com.google.mediapipe.tasks.components.containers.ClassificationResult; +import com.google.mediapipe.tasks.components.containers.proto.ClassificationsProto; +import com.google.mediapipe.tasks.core.TaskResult; +import java.util.ArrayList; +import java.util.List; +import java.util.Optional; + +/** Represents the classification results generated by {@link AudioClassifier}. */ +@AutoValue +public abstract class AudioClassifierResult implements TaskResult { + + /** + * Creates an {@link AudioClassifierResult} instance from a list of {@link + * ClassificationsProto.ClassificationResult} protobuf messages. + * + * @param protoList a list of {@link ClassificationsProto.ClassificationResult} protobuf message + * to convert. + * @param timestampMs a timestamp for this result. + */ + static AudioClassifierResult createFromProtoList( + List protoList, long timestampMs) { + List classificationResultList = new ArrayList<>(); + for (ClassificationsProto.ClassificationResult proto : protoList) { + classificationResultList.add(ClassificationResult.createFromProto(proto)); + } + return new AutoValue_AudioClassifierResult( + Optional.of(classificationResultList), Optional.empty(), timestampMs); + } + + /** + * Creates an {@link AudioClassifierResult} instance from a {@link + * ClassificationsProto.ClassificationResult} protobuf message. + * + * @param proto the {@link ClassificationsProto.ClassificationResult} protobuf message to convert. + * @param timestampMs a timestamp for this result. + */ + static AudioClassifierResult createFromProto( + ClassificationsProto.ClassificationResult proto, long timestampMs) { + return new AutoValue_AudioClassifierResult( + Optional.empty(), Optional.of(ClassificationResult.createFromProto(proto)), timestampMs); + } + + /** + * A list of of timpstamed {@link ClassificationResult} objects, each contains one set of results + * per classifier head. The list represents the audio classification result of an audio clip, and + * is only available when running with the audio clips mode. + */ + public abstract Optional> classificationResultList(); + + /** + * Contains one set of results per classifier head. A {@link ClassificationResult} usually + * represents one audio classification result in an audio stream, and s only available when + * running with the audio stream mode. + */ + public abstract Optional classificationResult(); + + @Override + public abstract long timestampMs(); +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/BaseAudioTaskApi.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/BaseAudioTaskApi.java new file mode 100644 index 000000000..affe43559 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/BaseAudioTaskApi.java @@ -0,0 +1,151 @@ +// 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. + +package com.google.mediapipe.tasks.audio.core; + +import com.google.mediapipe.framework.MediaPipeException; +import com.google.mediapipe.framework.Packet; +import com.google.mediapipe.tasks.components.containers.AudioData; +import com.google.mediapipe.tasks.core.TaskResult; +import com.google.mediapipe.tasks.core.TaskRunner; +import java.util.HashMap; +import java.util.Map; + +/** The base class of MediaPipe audio tasks. */ +public class BaseAudioTaskApi implements AutoCloseable { + private static final long MICROSECONDS_PER_MILLISECOND = 1000; + private static final long PRESTREAM_TIMESTAMP = Long.MIN_VALUE + 2; + + private final TaskRunner runner; + private final RunningMode runningMode; + private final String audioStreamName; + private final String sampleRateStreamName; + private double defaultSampleRate; + + static { + System.loadLibrary("mediapipe_tasks_audio_jni"); + } + + /** + * Constructor to initialize a {@link BaseAudioTaskApi}. + * + * @param runner a {@link TaskRunner}. + * @param runningMode a mediapipe audio task {@link RunningMode}. + * @param audioStreamName the name of the input audio stream. + * @param sampleRateStreamName the name of the audio sample rate stream. + */ + public BaseAudioTaskApi( + TaskRunner runner, + RunningMode runningMode, + String audioStreamName, + String sampleRateStreamName) { + this.runner = runner; + this.runningMode = runningMode; + this.audioStreamName = audioStreamName; + this.sampleRateStreamName = sampleRateStreamName; + this.defaultSampleRate = -1.0; + } + + /** + * A synchronous method to process audio clips. The call blocks the current thread until a failure + * status or a successful result is returned. + * + * @param audioClip a MediaPipe {@link AudioDatra} object for processing. + * @throws MediaPipeException if the task is not in the audio clips mode. + */ + protected TaskResult processAudioClip(AudioData audioClip) { + if (runningMode != RunningMode.AUDIO_CLIPS) { + throw new MediaPipeException( + MediaPipeException.StatusCode.FAILED_PRECONDITION.ordinal(), + "Task is not initialized with the audio clips mode. Current running mode:" + + runningMode.name()); + } + Map inputPackets = new HashMap<>(); + inputPackets.put( + audioStreamName, + runner + .getPacketCreator() + .createMatrix( + audioClip.getFormat().getNumOfChannels(), + audioClip.getBufferLength(), + audioClip.getBuffer())); + inputPackets.put( + sampleRateStreamName, + runner.getPacketCreator().createFloat64(audioClip.getFormat().getSampleRate())); + return runner.process(inputPackets); + } + + /** + * Checks or sets the audio sample rate in the audio stream mode. + * + * @param sampleRate the audio sample rate. + * @throws MediaPipeException if the task is not in the audio stream mode or the provided sample + * rate is inconsisent with the previously recevied. + */ + protected void checkOrSetSampleRate(double sampleRate) { + if (runningMode != RunningMode.AUDIO_STREAM) { + throw new MediaPipeException( + MediaPipeException.StatusCode.FAILED_PRECONDITION.ordinal(), + "Task is not initialized with the audio stream mode. Current running mode:" + + runningMode.name()); + } + if (defaultSampleRate > 0) { + if (Double.compare(sampleRate, defaultSampleRate) != 0) { + throw new MediaPipeException( + MediaPipeException.StatusCode.INVALID_ARGUMENT.ordinal(), + "The input audio sample rate: " + + sampleRate + + " is inconsistent with the previously provided: " + + defaultSampleRate); + } + } else { + Map inputPackets = new HashMap<>(); + inputPackets.put(sampleRateStreamName, runner.getPacketCreator().createFloat64(sampleRate)); + runner.send(inputPackets, PRESTREAM_TIMESTAMP); + defaultSampleRate = sampleRate; + } + } + /** + * An asynchronous method to send audio stream data to the {@link TaskRunner}. The results will be + * available in the user-defined result listener. + * + * @param audioClip a MediaPipe {@link AudioDatra} object for processing. + * @param timestampMs the corresponding timestamp of the input image in milliseconds. + * @throws MediaPipeException if the task is not in the stream mode. + */ + protected void sendAudioStreamData(AudioData audioClip, long timestampMs) { + if (runningMode != RunningMode.AUDIO_STREAM) { + throw new MediaPipeException( + MediaPipeException.StatusCode.FAILED_PRECONDITION.ordinal(), + "Task is not initialized with the audio stream mode. Current running mode:" + + runningMode.name()); + } + Map inputPackets = new HashMap<>(); + inputPackets.put( + audioStreamName, + runner + .getPacketCreator() + .createMatrix( + audioClip.getFormat().getNumOfChannels(), + audioClip.getBufferLength(), + audioClip.getBuffer())); + runner.send(inputPackets, timestampMs * MICROSECONDS_PER_MILLISECOND); + } + + /** Closes and cleans up the MediaPipe audio task. */ + @Override + public void close() { + runner.close(); + } +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/RunningMode.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/RunningMode.java new file mode 100644 index 000000000..f0a123810 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/core/RunningMode.java @@ -0,0 +1,29 @@ +// 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. + +package com.google.mediapipe.tasks.audio.core; + +/** + * MediaPipe audio task running mode. A MediaPipe audio task can be run with two different modes: + * + *
    + *
  • AUDIO_CLIPS: The mode for running a mediapipe audio task on independent audio clips. + *
  • AUDIO_STREAM: The mode for running a mediapipe audio task on an audio stream, such as from + * microphone. + *
+ */ +public enum RunningMode { + AUDIO_CLIPS, + AUDIO_STREAM, +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/AudioData.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/AudioData.java new file mode 100644 index 000000000..40a05b1e4 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/AudioData.java @@ -0,0 +1,334 @@ +// 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. + +package com.google.mediapipe.tasks.components.containers; + +import static java.lang.System.arraycopy; + +import android.media.AudioFormat; +import android.media.AudioRecord; +import com.google.auto.value.AutoValue; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.nio.FloatBuffer; + +/** + * Defines a ring buffer and some utility functions to prepare the input audio samples. + * + *

It maintains a Ring Buffer to hold + * input audio data. Clients could feed input audio data via `load` methods and access the + * aggregated audio samples via `getTensorBuffer` method. + * + *

Note that this class can only handle input audio in Float (in {@link + * android.media.AudioFormat#ENCODING_PCM_16BIT}) or Short (in {@link + * android.media.AudioFormat#ENCODING_PCM_FLOAT}). Internally it converts and stores all the audio + * samples in PCM Float encoding. + * + *

Typical usage in Kotlin + * + * 

+ *   val audioData = AudioData.create(format, modelInputLength)
+ *   audioData.load(newData)
+ *
+ * + *

Another sample usage with {@link android.media.AudioRecord} + * + * 

+ *   val audioData = AudioData.create(format, modelInputLength)
+ *   Timer().scheduleAtFixedRate(delay, period) {
+ *     audioData.load(audioRecord)
+ *   }
+ *
+ */ +public class AudioData { + + private static final String TAG = AudioData.class.getSimpleName(); + private final FloatRingBuffer buffer; + private final AudioDataFormat format; + + /** + * Creates a {@link android.media.AudioRecord} instance with a ring buffer whose size is {@code + * sampleCounts} * {@code format.getNumOfChannels()}. + * + * @param format the expected {@link AudioDataFormat} of audio data loaded into this class. + * @param sampleCounts the number of samples. + */ + public static AudioData create(AudioDataFormat format, int sampleCounts) { + return new AudioData(format, sampleCounts); + } + + /** + * Creates a {@link AudioData} instance with a ring buffer whose size is {@code sampleCounts} * + * {@code format.getChannelCount()}. + * + * @param format the {@link android.media.AudioFormat} required by the TFLite model. It defines + * the number of channels and sample rate. + * @param sampleCounts the number of samples to be fed into the model + */ + public static AudioData create(AudioFormat format, int sampleCounts) { + return new AudioData(AudioDataFormat.create(format), sampleCounts); + } + + /** + * Wraps a few constants describing the format of the incoming audio samples, namely number of + * channels and the sample rate. By default, num of channels is set to 1. + */ + @AutoValue + public abstract static class AudioDataFormat { + private static final int DEFAULT_NUM_OF_CHANNELS = 1; + + /** Creates a {@link AudioFormat} instance from Android AudioFormat class. */ + public static AudioDataFormat create(AudioFormat format) { + return AudioDataFormat.builder() + .setNumOfChannels(format.getChannelCount()) + .setSampleRate(format.getSampleRate()) + .build(); + } + + public abstract int getNumOfChannels(); + + public abstract float getSampleRate(); + + public static Builder builder() { + return new AutoValue_AudioData_AudioDataFormat.Builder() + .setNumOfChannels(DEFAULT_NUM_OF_CHANNELS); + } + + /** Builder for {@link AudioDataFormat} */ + @AutoValue.Builder + public abstract static class Builder { + + /* By default, it's set to have 1 channel. */ + public abstract Builder setNumOfChannels(int value); + + public abstract Builder setSampleRate(float value); + + abstract AudioDataFormat autoBuild(); + + public AudioDataFormat build() { + AudioDataFormat format = autoBuild(); + if (format.getNumOfChannels() <= 0) { + throw new IllegalArgumentException("Number of channels should be greater than 0"); + } + if (format.getSampleRate() <= 0) { + throw new IllegalArgumentException("Sample rate should be greater than 0"); + } + return format; + } + } + } + + /** + * Stores the input audio samples {@code src} in the ring buffer. + * + * @param src input audio samples in {@link android.media.AudioFormat#ENCODING_PCM_FLOAT}. For + * multi-channel input, the array is interleaved. + */ + public void load(float[] src) { + load(src, 0, src.length); + } + + /** + * Stores the input audio samples {@code src} in the ring buffer. + * + * @param src input audio samples in {@link android.media.AudioFormat#ENCODING_PCM_FLOAT}. For + * multi-channel input, the array is interleaved. + * @param offsetInFloat starting position in the {@code src} array + * @param sizeInFloat the number of float values to be copied + * @throws IllegalArgumentException for incompatible audio format or incorrect input size + */ + public void load(float[] src, int offsetInFloat, int sizeInFloat) { + if (sizeInFloat % format.getNumOfChannels() != 0) { + throw new IllegalArgumentException( + String.format( + "Size (%d) needs to be a multiplier of the number of channels (%d)", + sizeInFloat, format.getNumOfChannels())); + } + buffer.load(src, offsetInFloat, sizeInFloat); + } + + /** + * Converts the input audio samples {@code src} to ENCODING_PCM_FLOAT, then stores it in the ring + * buffer. + * + * @param src input audio samples in {@link android.media.AudioFormat#ENCODING_PCM_16BIT}. For + * multi-channel input, the array is interleaved. + */ + public void load(short[] src) { + load(src, 0, src.length); + } + + /** + * Converts the input audio samples {@code src} to ENCODING_PCM_FLOAT, then stores it in the ring + * buffer. + * + * @param src input audio samples in {@link android.media.AudioFormat#ENCODING_PCM_16BIT}. For + * multi-channel input, the array is interleaved. + * @param offsetInShort starting position in the src array + * @param sizeInShort the number of short values to be copied + * @throws IllegalArgumentException if the source array can't be copied + */ + public void load(short[] src, int offsetInShort, int sizeInShort) { + if (offsetInShort + sizeInShort > src.length) { + throw new IllegalArgumentException( + String.format( + "Index out of range. offset (%d) + size (%d) should <= newData.length (%d)", + offsetInShort, sizeInShort, src.length)); + } + float[] floatData = new float[sizeInShort]; + for (int i = 0; i < sizeInShort; i++) { + // Convert the data to PCM Float encoding i.e. values between -1 and 1 + floatData[i] = src[i + offsetInShort] * 1.f / Short.MAX_VALUE; + } + load(floatData); + } + + /** + * Loads latest data from the {@link android.media.AudioRecord} in a non-blocking way. Only + * supporting ENCODING_PCM_16BIT and ENCODING_PCM_FLOAT. + * + * @param record an instance of {@link android.media.AudioRecord} + * @return number of captured audio values whose size is {@code channelCount * sampleCount}. If + * there was no new data in the AudioRecord or an error occurred, this method will return 0. + * @throws IllegalArgumentException for unsupported audio encoding format + * @throws IllegalStateException if reading from AudioRecord failed + */ + public int load(AudioRecord record) { + if (!this.format.equals(AudioDataFormat.create(record.getFormat()))) { + throw new IllegalArgumentException("Incompatible audio format."); + } + int loadedValues = 0; + if (record.getAudioFormat() == AudioFormat.ENCODING_PCM_FLOAT) { + float[] newData = new float[record.getChannelCount() * record.getBufferSizeInFrames()]; + loadedValues = record.read(newData, 0, newData.length, AudioRecord.READ_NON_BLOCKING); + if (loadedValues > 0) { + load(newData, 0, loadedValues); + return loadedValues; + } + } else if (record.getAudioFormat() == AudioFormat.ENCODING_PCM_16BIT) { + short[] newData = new short[record.getChannelCount() * record.getBufferSizeInFrames()]; + loadedValues = record.read(newData, 0, newData.length, AudioRecord.READ_NON_BLOCKING); + if (loadedValues > 0) { + load(newData, 0, loadedValues); + return loadedValues; + } + } else { + throw new IllegalArgumentException( + "Unsupported encoding. Requires ENCODING_PCM_16BIT or ENCODING_PCM_FLOAT."); + } + + switch (loadedValues) { + case AudioRecord.ERROR_INVALID_OPERATION: + throw new IllegalStateException("AudioRecord.ERROR_INVALID_OPERATION"); + + case AudioRecord.ERROR_BAD_VALUE: + throw new IllegalStateException("AudioRecord.ERROR_BAD_VALUE"); + + case AudioRecord.ERROR_DEAD_OBJECT: + throw new IllegalStateException("AudioRecord.ERROR_DEAD_OBJECT"); + + case AudioRecord.ERROR: + throw new IllegalStateException("AudioRecord.ERROR"); + + default: + return 0; + } + } + + /** + * Returns a float array holding all the available audio samples in {@link + * android.media.AudioFormat#ENCODING_PCM_FLOAT} i.e. values are in the range of [-1, 1]. + */ + public float[] getBuffer() { + float[] bufferData = new float[buffer.getCapacity()]; + ByteBuffer byteBuffer = buffer.getBuffer(); + byteBuffer.asFloatBuffer().get(bufferData); + return bufferData; + } + + /* Returns the {@link AudioDataFormat} associated with the tensor. */ + public AudioDataFormat getFormat() { + return format; + } + + /* Returns the audio buffer length. */ + public int getBufferLength() { + return buffer.getCapacity() / format.getNumOfChannels(); + } + + private AudioData(AudioDataFormat format, int sampleCounts) { + this.format = format; + this.buffer = new FloatRingBuffer(sampleCounts * format.getNumOfChannels()); + } + + /** Actual implementation of the ring buffer. */ + private static class FloatRingBuffer { + + private final float[] buffer; + private int nextIndex = 0; + + public FloatRingBuffer(int flatSize) { + buffer = new float[flatSize]; + } + + /** + * Loads a slice of the float array to the ring buffer. If the float array is longer than ring + * buffer's capacity, samples with lower indices in the array will be ignored. + */ + public void load(float[] newData, int offset, int size) { + if (offset + size > newData.length) { + throw new IllegalArgumentException( + String.format( + "Index out of range. offset (%d) + size (%d) should <= newData.length (%d)", + offset, size, newData.length)); + } + // If buffer can't hold all the data, only keep the most recent data of size buffer.length + if (size > buffer.length) { + offset += (size - buffer.length); + size = buffer.length; + } + if (nextIndex + size < buffer.length) { + // No need to wrap nextIndex, just copy newData[offset:offset + size] + // to buffer[nextIndex:nextIndex+size] + arraycopy(newData, offset, buffer, nextIndex, size); + } else { + // Need to wrap nextIndex, perform copy in two chunks. + int firstChunkSize = buffer.length - nextIndex; + // First copy newData[offset:offset+firstChunkSize] to buffer[nextIndex:buffer.length] + arraycopy(newData, offset, buffer, nextIndex, firstChunkSize); + // Then copy newData[offset+firstChunkSize:offset+size] to buffer[0:size-firstChunkSize] + arraycopy(newData, offset + firstChunkSize, buffer, 0, size - firstChunkSize); + } + + nextIndex = (nextIndex + size) % buffer.length; + } + + public ByteBuffer getBuffer() { + // Create non-direct buffers. On Pixel 4, creating direct buffer costs around 0.1 ms, which + // can be 5x ~ 10x longer compared to non-direct buffer backed by arrays (around 0.01ms), so + // generally we don't create direct buffer for every invocation. + ByteBuffer byteBuffer = ByteBuffer.allocate(Float.SIZE / 8 * buffer.length); + byteBuffer.order(ByteOrder.nativeOrder()); + FloatBuffer result = byteBuffer.asFloatBuffer(); + result.put(buffer, nextIndex, buffer.length - nextIndex); + result.put(buffer, 0, nextIndex); + byteBuffer.rewind(); + return byteBuffer; + } + + public int getCapacity() { + return buffer.length; + } + } +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD index 63697229f..5c41edbad 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD @@ -16,6 +16,15 @@ package(default_visibility = ["//mediapipe/tasks:internal"]) licenses(["notice"]) +android_library( + name = "audio_data", + srcs = ["AudioData.java"], + deps = [ + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) + android_library( name = "category", srcs = ["Category.java"], diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java index d18f0e41e..49c459ef1 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/OutputHandler.java @@ -31,11 +31,22 @@ public class OutputHandler { InputT convertToTaskInput(List packets); } - /** Interface for the customizable MediaPipe task result listener. */ + /** + * Interface for the customizable MediaPipe task result listener that can reteive both task result + * objects and the correpsonding input data. + */ public interface ResultListener { void run(OutputT result, InputT input); } + /** + * Interface for the customizable MediaPipe task result listener that can only reteive task result + * objects. + */ + public interface PureResultListener { + void run(OutputT result); + } + private static final String TAG = "OutputHandler"; // A task-specific graph output packet converter that should be implemented per task. private OutputPacketConverter outputPacketConverter; @@ -45,6 +56,8 @@ public class OutputHandler { protected ErrorListener errorListener; // The cached task result for non latency sensitive use cases. protected OutputT cachedTaskResult; + // The latest output timestamp. + protected long latestOutputTimestamp = -1; // Whether the output handler should react to timestamp-bound changes by outputting empty packets. private boolean handleTimestampBoundChanges = false; @@ -98,6 +111,11 @@ public class OutputHandler { return taskResult; } + /* Returns the latest output timestamp. */ + public long getLatestOutputTimestamp() { + return latestOutputTimestamp; + } + /** * Handles a list of output {@link Packet}s. Invoked when a packet list become available. * @@ -109,6 +127,7 @@ public class OutputHandler { taskResult = outputPacketConverter.convertToTaskResult(packets); if (resultListener == null) { cachedTaskResult = taskResult; + latestOutputTimestamp = packets.get(0).getTimestamp(); } else { InputT taskInput = outputPacketConverter.convertToTaskInput(packets); resultListener.run(taskResult, taskInput); diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskRunner.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskRunner.java index 5739edebe..e6fc91cf6 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskRunner.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/TaskRunner.java @@ -93,6 +93,7 @@ public class TaskRunner implements AutoCloseable { public synchronized TaskResult process(Map inputs) { addPackets(inputs, generateSyntheticTimestamp()); graph.waitUntilGraphIdle(); + lastSeenTimestamp = outputHandler.getLatestOutputTimestamp(); return outputHandler.retrieveCachedTaskResult(); } From d5552c4bd9c884bf82f73991e25ff0219d5de3c3 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 11:19:43 -0800 Subject: [PATCH 06/21] Internal Changes PiperOrigin-RevId: 487591065 --- third_party/external_files.bzl | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/third_party/external_files.bzl b/third_party/external_files.bzl index e55b88131..a89a192ee 100644 --- a/third_party/external_files.bzl +++ b/third_party/external_files.bzl @@ -286,6 +286,12 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/general_meta.json?generation=1665422822603848"], ) + http_file( + name = "com_google_mediapipe_gesture_recognizer_task", + sha256 = "a966b1d4e774e0423c19c8aa71f070e5a72fe7a03c2663dd2f3cb0b0095ee3e1", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_recognizer.task?generation=1668100501451433"], + ) + http_file( name = "com_google_mediapipe_golden_json_json", sha256 = "55c0c88748d099aa379930504df62c6c8f1d8874ea52d2f8a925f352c4c7f09c", From bb5782ee2ff572473428f53d8c74cf74b8638c33 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 11:44:51 -0800 Subject: [PATCH 07/21] Correct the timestamp in the vision tasks result objects. PiperOrigin-RevId: 487597999 --- .../tasks/vision/core/BaseVisionTaskApi.java | 14 ++++++++++++++ .../gesturerecognizer/GestureRecognizer.java | 3 ++- .../vision/handlandmarker/HandLandmarker.java | 3 ++- .../vision/imageclassifier/ImageClassifier.java | 3 ++- .../vision/objectdetector/ObjectDetector.java | 3 ++- 5 files changed, 22 insertions(+), 4 deletions(-) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core/BaseVisionTaskApi.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core/BaseVisionTaskApi.java index 0774b69a2..04158abf1 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core/BaseVisionTaskApi.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core/BaseVisionTaskApi.java @@ -160,4 +160,18 @@ public class BaseVisionTaskApi implements AutoCloseable { .setRotation(-(float) Math.PI * imageProcessingOptions.rotationDegrees() / 180.0f) .build(); } + + /** + * Generates the timestamp of a vision task result object from the vision task running mode and + * the output packet. + * + * @param runningMode MediaPipe Vision Tasks {@link RunningMode}. + * @param packet the output {@link Packet}. + */ + public static long generateResultTimestampMs(RunningMode runningMode, Packet packet) { + if (runningMode == RunningMode.IMAGE) { + return -1; + } + return packet.getTimestamp() / MICROSECONDS_PER_MILLISECOND; + } } diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java index d6faf5986..d1ba94a1f 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java @@ -175,7 +175,8 @@ public final class GestureRecognizer extends BaseVisionTaskApi { packets.get(HANDEDNESS_OUT_STREAM_INDEX), ClassificationList.parser()), PacketGetter.getProtoVector( packets.get(HAND_GESTURES_OUT_STREAM_INDEX), ClassificationList.parser()), - packets.get(HAND_GESTURES_OUT_STREAM_INDEX).getTimestamp()); + BaseVisionTaskApi.generateResultTimestampMs( + recognizerOptions.runningMode(), packets.get(HAND_GESTURES_OUT_STREAM_INDEX))); } @Override diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java index 91ec07f07..089e1c6e7 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java @@ -165,7 +165,8 @@ public final class HandLandmarker extends BaseVisionTaskApi { packets.get(WORLD_LANDMARKS_OUT_STREAM_INDEX), LandmarkList.parser()), PacketGetter.getProtoVector( packets.get(HANDEDNESS_OUT_STREAM_INDEX), ClassificationList.parser()), - packets.get(LANDMARKS_OUT_STREAM_INDEX).getTimestamp()); + BaseVisionTaskApi.generateResultTimestampMs( + landmarkerOptions.runningMode(), packets.get(LANDMARKS_OUT_STREAM_INDEX))); } @Override diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifier.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifier.java index f01546ffc..5e278804b 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifier.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifier.java @@ -176,7 +176,8 @@ public final class ImageClassifier extends BaseVisionTaskApi { PacketGetter.getProto( packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX), ClassificationsProto.ClassificationResult.getDefaultInstance())), - packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX).getTimestamp()); + BaseVisionTaskApi.generateResultTimestampMs( + options.runningMode(), packets.get(CLASSIFICATIONS_OUT_STREAM_INDEX))); } catch (IOException e) { throw new MediaPipeException( MediaPipeException.StatusCode.INTERNAL.ordinal(), e.getMessage()); diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector/ObjectDetector.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector/ObjectDetector.java index 3f944eaee..769b9137f 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector/ObjectDetector.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector/ObjectDetector.java @@ -173,7 +173,8 @@ public final class ObjectDetector extends BaseVisionTaskApi { return ObjectDetectionResult.create( PacketGetter.getProtoVector( packets.get(DETECTIONS_OUT_STREAM_INDEX), Detection.parser()), - packets.get(DETECTIONS_OUT_STREAM_INDEX).getTimestamp()); + BaseVisionTaskApi.generateResultTimestampMs( + detectorOptions.runningMode(), packets.get(DETECTIONS_OUT_STREAM_INDEX))); } @Override From aeb24668446f741b804039f3153a455d8bab8834 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidt Date: Thu, 10 Nov 2022 12:35:46 -0800 Subject: [PATCH 08/21] Pick TFLite delegate for Web CPU PiperOrigin-RevId: 487611446 --- .../web/components/processors/base_options.ts | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/mediapipe/tasks/web/components/processors/base_options.ts b/mediapipe/tasks/web/components/processors/base_options.ts index a7f7bd280..ac24a8db6 100644 --- a/mediapipe/tasks/web/components/processors/base_options.ts +++ b/mediapipe/tasks/web/components/processors/base_options.ts @@ -70,17 +70,11 @@ async function configureExternalFile( /** Configues the `acceleration` option. */ function configureAcceleration(options: BaseOptions, proto: BaseOptionsProto) { - if ('delegate' in options) { - const acceleration = new Acceleration(); - if (options.delegate === 'cpu') { - acceleration.setXnnpack( - new InferenceCalculatorOptions.Delegate.Xnnpack()); - proto.setAcceleration(acceleration); - } else if (options.delegate === 'gpu') { - acceleration.setGpu(new InferenceCalculatorOptions.Delegate.Gpu()); - proto.setAcceleration(acceleration); - } else { - proto.clearAcceleration(); - } + const acceleration = proto.getAcceleration() ?? new Acceleration(); + if (options.delegate === 'gpu') { + acceleration.setGpu(new InferenceCalculatorOptions.Delegate.Gpu()); + } else { + acceleration.setTflite(new InferenceCalculatorOptions.Delegate.TfLite()); } + proto.setAcceleration(acceleration); } From 8ec4427bd75b8d2b2ec2105a55ac5cfbce42da2b Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 12:51:34 -0800 Subject: [PATCH 09/21] Add option, result types and utils for Java embedders. PiperOrigin-RevId: 487615327 --- .../tasks/components/containers/BUILD | 27 ++++ .../components/containers/Embedding.java | 88 +++++++++++++ .../containers/EmbeddingResult.java | 69 +++++++++++ .../tasks/components/processors/BUILD | 13 ++ .../processors/EmbedderOptions.java | 68 ++++++++++ .../mediapipe/tasks/components/utils/BUILD | 26 ++++ .../components/utils/CosineSimilarity.java | 88 +++++++++++++ .../components/utils/AndroidManifest.xml | 24 ++++ .../mediapipe/tasks/components/utils/BUILD | 19 +++ .../utils/CosineSimilarityTest.java | 116 ++++++++++++++++++ 10 files changed, 538 insertions(+) create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/Embedding.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/EmbeddingResult.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/EmbedderOptions.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/CosineSimilarity.java create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/AndroidManifest.xml create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/BUILD create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/CosineSimilarityTest.java diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD index 5c41edbad..aa835894e 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD @@ -89,3 +89,30 @@ filegroup( srcs = glob(["*.java"]), visibility = ["//mediapipe/tasks/java/com/google/mediapipe/tasks/core:__subpackages__"], ) + +android_library( + name = "embedding", + srcs = ["Embedding.java"], + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + deps = [ + "//mediapipe/tasks/cc/components/containers/proto:embeddings_java_proto_lite", + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) + +android_library( + name = "embeddingresult", + srcs = ["EmbeddingResult.java"], + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + deps = [ + ":embedding", + "//mediapipe/tasks/cc/components/containers/proto:embeddings_java_proto_lite", + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/Embedding.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/Embedding.java new file mode 100644 index 000000000..6cb0e325b --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/Embedding.java @@ -0,0 +1,88 @@ +// 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. + +package com.google.mediapipe.tasks.components.containers; + +import com.google.auto.value.AutoValue; +import com.google.mediapipe.tasks.components.containers.proto.EmbeddingsProto; +import java.util.Optional; + +/** + * Represents the embedding for a given embedder head. Typically used in embedding tasks. + * + *

One and only one of the two 'floatEmbedding' and 'quantizedEmbedding' will contain data, based + * on whether or not the embedder was configured to perform scala quantization. + */ +@AutoValue +public abstract class Embedding { + + /** + * Creates an {@link Embedding} instance. + * + * @param floatEmbedding the floating-point embedding + * @param quantizedEmbedding the quantized embedding. + * @param headIndex the index of the embedder head. + * @param headName the optional name of the embedder head. + */ + public static Embedding create( + float[] floatEmbedding, byte[] quantizedEmbedding, int headIndex, Optional headName) { + return new AutoValue_Embedding(floatEmbedding, quantizedEmbedding, headIndex, headName); + } + + /** + * Creates an {@link Embedding} object from an {@link EmbeddingsProto.Embedding} protobuf message. + * + * @param proto the {@link EmbeddingsProto.Embedding} protobuf message to convert. + */ + public static Embedding createFromProto(EmbeddingsProto.Embedding proto) { + float[] floatEmbedding; + if (proto.hasFloatEmbedding()) { + floatEmbedding = new float[proto.getFloatEmbedding().getValuesCount()]; + for (int i = 0; i < floatEmbedding.length; i++) { + floatEmbedding[i] = proto.getFloatEmbedding().getValues(i); + } + } else { + floatEmbedding = new float[0]; + } + return Embedding.create( + floatEmbedding, + proto.hasQuantizedEmbedding() + ? proto.getQuantizedEmbedding().getValues().toByteArray() + : new byte[0], + proto.getHeadIndex(), + proto.hasHeadName() ? Optional.of(proto.getHeadName()) : Optional.empty()); + } + + /** + * Floating-point embedding. + * + *

Empty if the embedder was configured to perform scalar quantization. + */ + public abstract float[] floatEmbedding(); + + /** + * Quantized embedding. + * + *

Empty if the embedder was not configured to perform scalar quantization. + */ + public abstract byte[] quantizedEmbedding(); + + /** + * The index of the embedder head these entries refer to. This is useful for multi-head models. + */ + public abstract int headIndex(); + + /** The optional name of the embedder head, which is the corresponding tensor metadata name. */ + public abstract Optional headName(); +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/EmbeddingResult.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/EmbeddingResult.java new file mode 100644 index 000000000..32fa7b47d --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/EmbeddingResult.java @@ -0,0 +1,69 @@ +// 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. + +package com.google.mediapipe.tasks.components.containers; + +import com.google.auto.value.AutoValue; +import com.google.mediapipe.tasks.components.containers.proto.EmbeddingsProto; +import java.util.ArrayList; +import java.util.Collections; +import java.util.List; +import java.util.Optional; + +/** Represents the embedding results of a model. Typically used as a result for embedding tasks. */ +@AutoValue +public abstract class EmbeddingResult { + + /** + * Creates a {@link EmbeddingResult} instance. + * + * @param embeddings the list of {@link Embedding} objects containing the embedding for each head + * of the model. + * @param timestampMs the optional timestamp (in milliseconds) of the start of the chunk of data + * corresponding to these results. + */ + public static EmbeddingResult create(List embeddings, Optional timestampMs) { + return new AutoValue_EmbeddingResult(Collections.unmodifiableList(embeddings), timestampMs); + } + + /** + * Creates a {@link EmbeddingResult} object from a {@link EmbeddingsProto.EmbeddingResult} + * protobuf message. + * + * @param proto the {@link EmbeddingsProto.EmbeddingResult} protobuf message to convert. + */ + public static EmbeddingResult createFromProto(EmbeddingsProto.EmbeddingResult proto) { + List embeddings = new ArrayList<>(); + for (EmbeddingsProto.Embedding embeddingProto : proto.getEmbeddingsList()) { + embeddings.add(Embedding.createFromProto(embeddingProto)); + } + Optional timestampMs = + proto.hasTimestampMs() ? Optional.of(proto.getTimestampMs()) : Optional.empty(); + return create(embeddings, timestampMs); + } + + /** The embedding results for each head of the model. */ + public abstract List embeddings(); + + /** + * The optional timestamp (in milliseconds) of the start of the chunk of data corresponding to + * these results. + * + *

This is only used for embedding extraction on time series (e.g. audio embedder). In these + * use cases, the amount of data to process might exceed the maximum size that the model can + * process: to solve this, the input data is split into multiple chunks starting at different + * timestamps. + */ + public abstract Optional timestampMs(); +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/BUILD index 1f99f1612..e61e59390 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/BUILD @@ -29,6 +29,19 @@ android_library( ], ) +android_library( + name = "embedderoptions", + srcs = ["EmbedderOptions.java"], + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + deps = [ + "//mediapipe/tasks/cc/components/processors/proto:embedder_options_java_proto_lite", + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) + # Expose the java source files for building mediapipe tasks core AAR. filegroup( name = "java_src", diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/EmbedderOptions.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/EmbedderOptions.java new file mode 100644 index 000000000..3cd197234 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/EmbedderOptions.java @@ -0,0 +1,68 @@ +// 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. + +package com.google.mediapipe.tasks.components.processors; + +import com.google.auto.value.AutoValue; +import com.google.mediapipe.tasks.components.processors.proto.EmbedderOptionsProto; + +/** Embedder options shared across MediaPipe Java embedding tasks. */ +@AutoValue +public abstract class EmbedderOptions { + + /** Builder for {@link EmbedderOptions} */ + @AutoValue.Builder + public abstract static class Builder { + /** + * Sets whether L2 normalization should be performed on the returned embeddings. Use this option + * only if the model does not already contain a native L2_NORMALIZATION TF Lite Op. + * In most cases, this is already the case and L2 norm is thus achieved through TF Lite + * inference. + * + *

False by default. + */ + public abstract Builder setL2Normalize(boolean l2Normalize); + + /** + * Sets whether the returned embedding should be quantized to bytes via scalar quantization. + * Embeddings are implicitly assumed to be unit-norm and therefore any dimensions is guaranteed + * to have value in [-1.0, 1.0]. Use {@link #setL2Normalize(boolean)} if this is + * not the case. + * + *

False by default. + */ + public abstract Builder setQuantize(boolean quantize); + + public abstract EmbedderOptions build(); + } + + public abstract boolean l2Normalize(); + + public abstract boolean quantize(); + + public static Builder builder() { + return new AutoValue_EmbedderOptions.Builder().setL2Normalize(false).setQuantize(false); + } + + /** + * Converts an {@link EmbedderOptions} object to an {@link EmbedderOptionsProto.EmbedderOptions} + * protobuf message. + */ + public EmbedderOptionsProto.EmbedderOptions convertToProto() { + return EmbedderOptionsProto.EmbedderOptions.newBuilder() + .setL2Normalize(l2Normalize()) + .setQuantize(quantize()) + .build(); + } +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD new file mode 100644 index 000000000..cd2bbafc8 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD @@ -0,0 +1,26 @@ +# 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. + +package(default_visibility = ["//mediapipe/tasks:internal"]) + +licenses(["notice"]) + +android_library( + name = "cosinesimilarity", + srcs = ["CosineSimilarity.java"], + deps = [ + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:embedding", + "@maven//:com_google_guava_guava", + ], +) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/CosineSimilarity.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/CosineSimilarity.java new file mode 100644 index 000000000..6b995731f --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/CosineSimilarity.java @@ -0,0 +1,88 @@ +// 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. + +package com.google.mediapipe.tasks.components.utils; + +import com.google.mediapipe.tasks.components.containers.Embedding; + +/** Utility class for computing cosine similarity between {@link Embedding} objects. */ +public class CosineSimilarity { + + // Non-instantiable class. + private CosineSimilarity() {} + + /** + * Computes cosine similarity + * between two {@link Embedding} objects. + * + * @throws IllegalArgumentException if the embeddings are of different types (float vs. + * quantized), have different sizes, or have an L2-norm of 0. + */ + public static double compute(Embedding u, Embedding v) { + if (u.floatEmbedding().length > 0 && v.floatEmbedding().length > 0) { + return computeFloat(u.floatEmbedding(), v.floatEmbedding()); + } + if (u.quantizedEmbedding().length > 0 && v.quantizedEmbedding().length > 0) { + return computeQuantized(u.quantizedEmbedding(), v.quantizedEmbedding()); + } + throw new IllegalArgumentException( + "Cannot compute cosine similarity between quantized and float embeddings."); + } + + private static double computeFloat(float[] u, float[] v) { + if (u.length != v.length) { + throw new IllegalArgumentException( + String.format( + "Cannot compute cosine similarity between embeddings of different sizes (%d vs." + + " %d).", + u.length, v.length)); + } + double dotProduct = 0.0; + double normU = 0.0; + double normV = 0.0; + for (int i = 0; i < u.length; i++) { + dotProduct += u[i] * v[i]; + normU += u[i] * u[i]; + normV += v[i] * v[i]; + } + if (normU <= 0 || normV <= 0) { + throw new IllegalArgumentException( + "Cannot compute cosine similarity on embedding with 0 norm."); + } + return dotProduct / Math.sqrt(normU * normV); + } + + private static double computeQuantized(byte[] u, byte[] v) { + if (u.length != v.length) { + throw new IllegalArgumentException( + String.format( + "Cannot compute cosine similarity between embeddings of different sizes (%d vs." + + " %d).", + u.length, v.length)); + } + double dotProduct = 0.0; + double normU = 0.0; + double normV = 0.0; + for (int i = 0; i < u.length; i++) { + dotProduct += u[i] * v[i]; + normU += u[i] * u[i]; + normV += v[i] * v[i]; + } + if (normU <= 0 || normV <= 0) { + throw new IllegalArgumentException( + "Cannot compute cosine similarity on embedding with 0 norm."); + } + return dotProduct / Math.sqrt(normU * normV); + } +} diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/AndroidManifest.xml b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/AndroidManifest.xml new file mode 100644 index 000000000..d0c1ff91f --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/AndroidManifest.xml @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/BUILD b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/BUILD new file mode 100644 index 000000000..a7f804c64 --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/BUILD @@ -0,0 +1,19 @@ +# 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. + +package(default_visibility = ["//mediapipe/tasks:internal"]) + +licenses(["notice"]) + +# TODO: Enable this in OSS diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/CosineSimilarityTest.java b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/CosineSimilarityTest.java new file mode 100644 index 000000000..f7a1ae002 --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/components/utils/CosineSimilarityTest.java @@ -0,0 +1,116 @@ +// 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. + +package com.google.mediapipe.tasks.components.utils; + +import static com.google.common.truth.Truth.assertThat; +import static org.junit.Assert.assertThrows; + +import androidx.test.ext.junit.runners.AndroidJUnit4; +import com.google.mediapipe.tasks.components.containers.Embedding; +import java.util.Optional; +import org.junit.Test; +import org.junit.runner.RunWith; + +/** Tests for {@link CosineSimilarity}. */ +@RunWith(AndroidJUnit4.class) +public final class CosineSimilarityTest { + + @Test + public void failsWithQuantizedAndFloatEmbeddings() { + Embedding u = + Embedding.create( + new float[] {1.0f}, new byte[0], /*headIndex=*/ 0, /*headName=*/ Optional.empty()); + Embedding v = + Embedding.create( + new float[0], new byte[] {1}, /*headIndex=*/ 0, /*headName=*/ Optional.empty()); + + IllegalArgumentException exception = + assertThrows(IllegalArgumentException.class, () -> CosineSimilarity.compute(u, v)); + assertThat(exception) + .hasMessageThat() + .contains("Cannot compute cosine similarity between quantized and float embeddings"); + } + + @Test + public void failsWithZeroNorm() { + Embedding u = + Embedding.create( + new float[] {0.0f}, new byte[0], /*headIndex=*/ 0, /*headName=*/ Optional.empty()); + + IllegalArgumentException exception = + assertThrows(IllegalArgumentException.class, () -> CosineSimilarity.compute(u, u)); + assertThat(exception) + .hasMessageThat() + .contains("Cannot compute cosine similarity on embedding with 0 norm"); + } + + @Test + public void failsWithDifferentSizes() { + Embedding u = + Embedding.create( + new float[] {1.0f, 2.0f}, + new byte[0], + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + Embedding v = + Embedding.create( + new float[] {1.0f, 2.0f, 3.0f}, + new byte[0], + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + + IllegalArgumentException exception = + assertThrows(IllegalArgumentException.class, () -> CosineSimilarity.compute(u, v)); + assertThat(exception) + .hasMessageThat() + .contains("Cannot compute cosine similarity between embeddings of different sizes"); + } + + @Test + public void succeedsWithFloatEmbeddings() { + Embedding u = + Embedding.create( + new float[] {1.0f, 0.0f, 0.0f, 0.0f}, + new byte[0], + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + Embedding v = + Embedding.create( + new float[] {0.5f, 0.5f, 0.5f, 0.5f}, + new byte[0], + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + + assertThat(CosineSimilarity.compute(u, v)).isEqualTo(0.5); + } + + @Test + public void succeedsWithQuantizedEmbeddings() { + Embedding u = + Embedding.create( + new float[0], + new byte[] {127, 0, 0, 0}, + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + Embedding v = + Embedding.create( + new float[0], + new byte[] {-128, 0, 0, 0}, + /*headIndex=*/ 0, + /*headName=*/ Optional.empty()); + + assertThat(CosineSimilarity.compute(u, v)).isEqualTo(-1.0); + } +} From d93caf00defb91b0b553264d0dacca08f0748b82 Mon Sep 17 00:00:00 2001 From: Hadon Nash Date: Thu, 10 Nov 2022 14:03:49 -0800 Subject: [PATCH 10/21] Fixing a bug in the BypassCalculator "passthrough" output stream ids. PiperOrigin-RevId: 487633708 --- mediapipe/calculators/core/bypass_calculator.cc | 2 +- mediapipe/calculators/core/bypass_calculator_test.cc | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/mediapipe/calculators/core/bypass_calculator.cc b/mediapipe/calculators/core/bypass_calculator.cc index 86dcfc0e1..efc0612ec 100644 --- a/mediapipe/calculators/core/bypass_calculator.cc +++ b/mediapipe/calculators/core/bypass_calculator.cc @@ -130,7 +130,7 @@ class BypassCalculator : public Node { pass_out.insert(entry.second); auto& packet = cc->Inputs().Get(entry.first).Value(); if (packet.Timestamp() == cc->InputTimestamp()) { - cc->Outputs().Get(entry.first).AddPacket(packet); + cc->Outputs().Get(entry.second).AddPacket(packet); } } Timestamp bound = cc->InputTimestamp().NextAllowedInStream(); diff --git a/mediapipe/calculators/core/bypass_calculator_test.cc b/mediapipe/calculators/core/bypass_calculator_test.cc index 224742a13..ccad510c6 100644 --- a/mediapipe/calculators/core/bypass_calculator_test.cc +++ b/mediapipe/calculators/core/bypass_calculator_test.cc @@ -42,10 +42,10 @@ constexpr char kTestGraphConfig1[] = R"pb( node { calculator: "BypassCalculator" input_stream: "PASS:appearances" - input_stream: "TRUNCATE:0:video_frame" - input_stream: "TRUNCATE:1:feature_config" + input_stream: "IGNORE:0:video_frame" + input_stream: "IGNORE:1:feature_config" output_stream: "PASS:passthrough_appearances" - output_stream: "TRUNCATE:passthrough_federated_gaze_output" + output_stream: "IGNORE:passthrough_federated_gaze_output" node_options: { [type.googleapis.com/mediapipe.BypassCalculatorOptions] { pass_input_stream: "PASS" From d6ba5e4bbe822cf33fe9020284e7c9d71dd489c5 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 15:10:37 -0800 Subject: [PATCH 11/21] Map the "com_github_glog_glog" dependency of "com_google_audio_tools" to mediapipe's "com_github_glog_glog_no_gflags". PiperOrigin-RevId: 487651038 --- WORKSPACE | 1 + mediapipe/calculators/audio/BUILD | 3 +++ mediapipe/calculators/tensor/BUILD | 1 + 3 files changed, 5 insertions(+) diff --git a/WORKSPACE b/WORKSPACE index f5594a524..046da997b 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -155,6 +155,7 @@ http_archive( name = "com_google_audio_tools", strip_prefix = "multichannel-audio-tools-master", urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"], + repo_mapping = {"@com_github_glog_glog" : "@com_github_glog_glog_no_gflags"}, ) http_archive( diff --git a/mediapipe/calculators/audio/BUILD b/mediapipe/calculators/audio/BUILD index 3f7957552..ba461e4a7 100644 --- a/mediapipe/calculators/audio/BUILD +++ b/mediapipe/calculators/audio/BUILD @@ -277,6 +277,7 @@ cc_test( "//mediapipe/framework/port:gtest_main", "//mediapipe/framework/port:status", "//mediapipe/util:time_series_test_util", + "@com_google_audio_tools//audio/dsp/mfcc", "@eigen_archive//:eigen3", ], ) @@ -352,6 +353,8 @@ cc_test( "//mediapipe/framework/port:status", "//mediapipe/framework/tool:validate_type", "//mediapipe/util:time_series_test_util", + "@com_google_audio_tools//audio/dsp:resampler", + "@com_google_audio_tools//audio/dsp:resampler_q", "@com_google_audio_tools//audio/dsp:signal_vector_util", "@eigen_archive//:eigen3", ], diff --git a/mediapipe/calculators/tensor/BUILD b/mediapipe/calculators/tensor/BUILD index 1a2a6b342..3385bcf47 100644 --- a/mediapipe/calculators/tensor/BUILD +++ b/mediapipe/calculators/tensor/BUILD @@ -156,6 +156,7 @@ cc_test( "//mediapipe/framework/port:parse_text_proto", "@com_google_absl//absl/status", "@com_google_absl//absl/strings", + "@com_google_audio_tools//audio/dsp:window_functions", ], ) From b3ac1c138747fa6f4bcf86892fbf7f48af8d8229 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 15:53:34 -0800 Subject: [PATCH 12/21] Changes to create MediaPipe Tasks Audio AAR. PiperOrigin-RevId: 487661296 --- .../com/google/mediapipe/tasks/audio/BUILD | 12 ++++- .../audioclassifier/AudioClassifier.java | 2 +- .../tasks/components/containers/BUILD | 2 +- .../mediapipe/tasks/components/utils/BUILD | 7 +++ .../com/google/mediapipe/tasks/core/BUILD | 1 + .../mediapipe/tasks/mediapipe_tasks_aar.bzl | 48 ++++++++++++++++++- 6 files changed, 66 insertions(+), 6 deletions(-) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD index c5cb6f8a3..b162d7dac 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/BUILD @@ -25,7 +25,7 @@ android_library( deps = [ ":libmediapipe_tasks_audio_jni_lib", "//mediapipe/java/com/google/mediapipe/framework:android_framework_no_mff", - "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audio_data", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audiodata", "//mediapipe/tasks/java/com/google/mediapipe/tasks/core", "@maven//:com_google_guava_guava", ], @@ -66,7 +66,7 @@ android_library( "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_java_proto_lite", "//mediapipe/tasks/cc/components/containers/proto:classifications_java_proto_lite", "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite", - "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audio_data", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audiodata", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:classificationresult", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors:classifieroptions", "//mediapipe/tasks/java/com/google/mediapipe/tasks/core", @@ -74,3 +74,11 @@ android_library( "@maven//:com_google_guava_guava", ], ) + +load("//mediapipe/tasks/java/com/google/mediapipe/tasks:mediapipe_tasks_aar.bzl", "mediapipe_tasks_audio_aar") + +mediapipe_tasks_audio_aar( + name = "tasks_audio", + srcs = glob(["**/*.java"]), + native_library = ":libmediapipe_tasks_audio_jni_lib", +) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java index 88b6daf0a..5a82eecaa 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/audio/audioclassifier/AudioClassifier.java @@ -188,7 +188,7 @@ public final class AudioClassifier extends BaseAudioTaskApi { }); if (options.resultListener().isPresent()) { ResultListener resultListener = - new ResultListener<>() { + new ResultListener() { @Override public void run(AudioClassifierResult audioClassifierResult, Void input) { options.resultListener().get().run(audioClassifierResult); diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD index aa835894e..d6e6ac740 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD @@ -17,7 +17,7 @@ package(default_visibility = ["//mediapipe/tasks:internal"]) licenses(["notice"]) android_library( - name = "audio_data", + name = "audiodata", srcs = ["AudioData.java"], deps = [ "//third_party:autovalue", diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD index cd2bbafc8..b2d27bfa7 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils/BUILD @@ -24,3 +24,10 @@ android_library( "@maven//:com_google_guava_guava", ], ) + +# Expose the java source files for building mediapipe tasks core AAR. +filegroup( + name = "java_src", + srcs = glob(["*.java"]), + visibility = ["//mediapipe/tasks/java/com/google/mediapipe/tasks/core:__subpackages__"], +) diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD index cb9d67424..01b1f653a 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD @@ -44,6 +44,7 @@ mediapipe_tasks_core_aar( srcs = glob(["*.java"]) + [ "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:java_src", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors:java_src", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/utils:java_src", "//mediapipe/java/com/google/mediapipe/framework/image:java_src", ], manifest = "AndroidManifest.xml", diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl b/mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl index 62c424f66..f0c9f81c6 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/mediapipe_tasks_aar.bzl @@ -30,6 +30,10 @@ _CORE_TASKS_JAVA_PROTO_LITE_TARGETS = [ "//mediapipe/tasks/cc/core/proto:external_file_java_proto_lite", ] +_AUDIO_TASKS_JAVA_PROTO_LITE_TARGETS = [ + "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_java_proto_lite", +] + _VISION_TASKS_JAVA_PROTO_LITE_TARGETS = [ "//mediapipe/tasks/cc/vision/object_detector/proto:object_detector_options_java_proto_lite", "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_java_proto_lite", @@ -62,6 +66,11 @@ def mediapipe_tasks_core_aar(name, srcs, manifest): _mediapipe_tasks_java_proto_src_extractor(target = target), ) + for target in _AUDIO_TASKS_JAVA_PROTO_LITE_TARGETS: + mediapipe_tasks_java_proto_srcs.append( + _mediapipe_tasks_java_proto_src_extractor(target = target), + ) + for target in _VISION_TASKS_JAVA_PROTO_LITE_TARGETS: mediapipe_tasks_java_proto_srcs.append( _mediapipe_tasks_java_proto_src_extractor(target = target), @@ -119,9 +128,43 @@ def mediapipe_tasks_core_aar(name, srcs, manifest): "@maven//:com_google_flogger_flogger_system_backend", "@maven//:com_google_code_findbugs_jsr305", ] + + _AUDIO_TASKS_JAVA_PROTO_LITE_TARGETS + _CORE_TASKS_JAVA_PROTO_LITE_TARGETS + - _VISION_TASKS_JAVA_PROTO_LITE_TARGETS + - _TEXT_TASKS_JAVA_PROTO_LITE_TARGETS, + _TEXT_TASKS_JAVA_PROTO_LITE_TARGETS + + _VISION_TASKS_JAVA_PROTO_LITE_TARGETS, + ) + +def mediapipe_tasks_audio_aar(name, srcs, native_library): + """Builds medaipipe tasks audio AAR. + + Args: + name: The bazel target name. + srcs: MediaPipe Audio Tasks' source files. + native_library: The native library that contains audio tasks' graph and calculators. + """ + + native.genrule( + name = name + "tasks_manifest_generator", + outs = ["AndroidManifest.xml"], + cmd = """ +cat > $(OUTS) < + + + +EOF +""", + ) + + _mediapipe_tasks_aar( + name = name, + srcs = srcs, + manifest = "AndroidManifest.xml", + java_proto_lite_targets = _CORE_TASKS_JAVA_PROTO_LITE_TARGETS + _AUDIO_TASKS_JAVA_PROTO_LITE_TARGETS, + native_library = native_library, ) def mediapipe_tasks_vision_aar(name, srcs, native_library): @@ -232,6 +275,7 @@ def _mediapipe_tasks_aar(name, srcs, manifest, java_proto_lite_targets, native_l "//mediapipe/framework/formats:landmark_java_proto_lite", "//mediapipe/framework/formats:location_data_java_proto_lite", "//mediapipe/framework/formats:rect_java_proto_lite", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:audiodata", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:detection", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category", "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:classificationresult", From 1a59f0a217261a643c2b654ccf9db59f8bd9b831 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidt Date: Thu, 10 Nov 2022 15:54:50 -0800 Subject: [PATCH 13/21] Prevent renaming of properties used in API PiperOrigin-RevId: 487661625 --- mediapipe/tasks/web/components/containers/category.d.ts | 2 +- .../web/components/containers/classification_result.d.ts | 2 +- mediapipe/tasks/web/components/containers/landmark.d.ts | 2 +- mediapipe/tasks/web/core/base_options.d.ts | 2 +- mediapipe/tasks/web/core/classifier_options.d.ts | 2 +- mediapipe/tasks/web/core/wasm_loader_options.d.ts | 2 +- .../vision/gesture_recognizer/gesture_recognizer_options.ts | 2 +- .../vision/gesture_recognizer/gesture_recognizer_result.ts | 2 +- .../web/vision/object_detector/object_detector_result.ts | 4 ++-- 9 files changed, 10 insertions(+), 10 deletions(-) diff --git a/mediapipe/tasks/web/components/containers/category.d.ts b/mediapipe/tasks/web/components/containers/category.d.ts index 0da8a061f..8ab8e6b84 100644 --- a/mediapipe/tasks/web/components/containers/category.d.ts +++ b/mediapipe/tasks/web/components/containers/category.d.ts @@ -15,7 +15,7 @@ */ /** A classification category. */ -export interface Category { +export declare interface Category { /** The probability score of this label category. */ score: number; diff --git a/mediapipe/tasks/web/components/containers/classification_result.d.ts b/mediapipe/tasks/web/components/containers/classification_result.d.ts index 33632f925..30590ab43 100644 --- a/mediapipe/tasks/web/components/containers/classification_result.d.ts +++ b/mediapipe/tasks/web/components/containers/classification_result.d.ts @@ -17,7 +17,7 @@ import {Category} from '../../../../tasks/web/components/containers/category'; /** Classification results for a given classifier head. */ -export interface Classifications { +export declare interface Classifications { /** * The array of predicted categories, usually sorted by descending scores, * e.g., from high to low probability. diff --git a/mediapipe/tasks/web/components/containers/landmark.d.ts b/mediapipe/tasks/web/components/containers/landmark.d.ts index 9a1badbba..f790d8a0b 100644 --- a/mediapipe/tasks/web/components/containers/landmark.d.ts +++ b/mediapipe/tasks/web/components/containers/landmark.d.ts @@ -20,7 +20,7 @@ * dimension of image, and the coordinates values are in the range of [0,1]. * Otherwise, it represenet a point in world coordinates. */ -export class Landmark { +export declare class Landmark { /** The x coordinates of the landmark. */ x: number; diff --git a/mediapipe/tasks/web/core/base_options.d.ts b/mediapipe/tasks/web/core/base_options.d.ts index 54a59a21b..86635b8c7 100644 --- a/mediapipe/tasks/web/core/base_options.d.ts +++ b/mediapipe/tasks/web/core/base_options.d.ts @@ -17,7 +17,7 @@ // Placeholder for internal dependency on trusted resource url /** Options to configure MediaPipe Tasks in general. */ -export interface BaseOptions { +export declare interface BaseOptions { /** * The model path to the model asset file. Only one of `modelAssetPath` or * `modelAssetBuffer` can be set. diff --git a/mediapipe/tasks/web/core/classifier_options.d.ts b/mediapipe/tasks/web/core/classifier_options.d.ts index 356643c55..3dec8d27e 100644 --- a/mediapipe/tasks/web/core/classifier_options.d.ts +++ b/mediapipe/tasks/web/core/classifier_options.d.ts @@ -17,7 +17,7 @@ import {BaseOptions} from '../../../tasks/web/core/base_options'; /** Options to configure the Mediapipe Classifier Task. */ -export interface ClassifierOptions { +export declare interface ClassifierOptions { /** Options to configure the loading of the model assets. */ baseOptions?: BaseOptions; diff --git a/mediapipe/tasks/web/core/wasm_loader_options.d.ts b/mediapipe/tasks/web/core/wasm_loader_options.d.ts index 1925aaf06..74436583d 100644 --- a/mediapipe/tasks/web/core/wasm_loader_options.d.ts +++ b/mediapipe/tasks/web/core/wasm_loader_options.d.ts @@ -17,7 +17,7 @@ // Placeholder for internal dependency on trusted resource url /** An object containing the locations of all Wasm assets */ -export interface WasmLoaderOptions { +export declare interface WasmLoaderOptions { /** The path to the Wasm loader script. */ wasmLoaderPath: string; /** The path to the Wasm binary. */ diff --git a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.ts b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.ts index 16169a93f..45601a74c 100644 --- a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.ts +++ b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.ts @@ -18,7 +18,7 @@ import {BaseOptions} from '../../../../tasks/web/core/base_options'; import {ClassifierOptions} from '../../../../tasks/web/core/classifier_options'; /** Options to configure the MediaPipe Gesture Recognizer Task */ -export interface GestureRecognizerOptions { +export declare interface GestureRecognizerOptions { /** Options to configure the loading of the model assets. */ baseOptions?: BaseOptions; diff --git a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts index cccdfaf68..08c8c2931 100644 --- a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts +++ b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts @@ -20,7 +20,7 @@ import {Landmark} from '../../../../tasks/web/components/containers/landmark'; /** * Represents the gesture recognition results generated by `GestureRecognizer`. */ -export interface GestureRecognitionResult { +export declare interface GestureRecognitionResult { /** Hand landmarks of detected hands. */ landmarks: Landmark[][]; diff --git a/mediapipe/tasks/web/vision/object_detector/object_detector_result.ts b/mediapipe/tasks/web/vision/object_detector/object_detector_result.ts index 7b2621134..e9e3843bc 100644 --- a/mediapipe/tasks/web/vision/object_detector/object_detector_result.ts +++ b/mediapipe/tasks/web/vision/object_detector/object_detector_result.ts @@ -17,7 +17,7 @@ import {Category} from '../../../../tasks/web/components/containers/category'; /** An integer bounding box, axis aligned. */ -export interface BoundingBox { +export declare interface BoundingBox { /** The X coordinate of the top-left corner, in pixels. */ originX: number; /** The Y coordinate of the top-left corner, in pixels. */ @@ -29,7 +29,7 @@ export interface BoundingBox { } /** Represents one object detected by the `ObjectDetector`. */ -export interface Detection { +export declare interface Detection { /** A list of `Category` objects. */ categories: Category[]; From 2ea5184c51918bbf4617d682423dcd0e78afa457 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 16:02:04 -0800 Subject: [PATCH 14/21] Internal change PiperOrigin-RevId: 487663454 --- .../hand_landmarker/hand_landmarker_graph.cc | 49 ++++++++++++------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_graph.cc b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_graph.cc index e610a412e..2c4133eb1 100644 --- a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_graph.cc +++ b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_graph.cc @@ -276,33 +276,44 @@ class HandLandmarkerGraph : public core::ModelTaskGraph { .set_min_size(max_num_hands); auto has_enough_hands = min_size_node.Out("").Cast(); - auto image_for_hand_detector = - DisallowIf(image_in, has_enough_hands, graph); - auto norm_rect_in_for_hand_detector = - DisallowIf(norm_rect_in, has_enough_hands, graph); - auto& hand_detector = graph.AddNode("mediapipe.tasks.vision.hand_detector.HandDetectorGraph"); hand_detector.GetOptions().CopyFrom( tasks_options.hand_detector_graph_options()); - image_for_hand_detector >> hand_detector.In("IMAGE"); - norm_rect_in_for_hand_detector >> hand_detector.In("NORM_RECT"); - auto hand_rects_from_hand_detector = hand_detector.Out("HAND_RECTS"); - - auto& hand_association = graph.AddNode("HandAssociationCalculator"); - hand_association.GetOptions() - .set_min_similarity_threshold(tasks_options.min_tracking_confidence()); - prev_hand_rects_from_landmarks >> - hand_association[Input>::Multiple("")][0]; - hand_rects_from_hand_detector >> - hand_association[Input>::Multiple("")][1]; - auto hand_rects = hand_association.Out(""); - auto& clip_hand_rects = graph.AddNode("ClipNormalizedRectVectorSizeCalculator"); clip_hand_rects.GetOptions() .set_max_vec_size(max_num_hands); - hand_rects >> clip_hand_rects.In(""); + + if (tasks_options.base_options().use_stream_mode()) { + // While in stream mode, skip hand detector graph when we successfully + // track the hands from the last frame. + auto image_for_hand_detector = + DisallowIf(image_in, has_enough_hands, graph); + auto norm_rect_in_for_hand_detector = + DisallowIf(norm_rect_in, has_enough_hands, graph); + image_for_hand_detector >> hand_detector.In("IMAGE"); + norm_rect_in_for_hand_detector >> hand_detector.In("NORM_RECT"); + auto hand_rects_from_hand_detector = hand_detector.Out("HAND_RECTS"); + auto& hand_association = graph.AddNode("HandAssociationCalculator"); + hand_association.GetOptions() + .set_min_similarity_threshold( + tasks_options.min_tracking_confidence()); + prev_hand_rects_from_landmarks >> + hand_association[Input>::Multiple("")][0]; + hand_rects_from_hand_detector >> + hand_association[Input>::Multiple("")][1]; + auto hand_rects = hand_association.Out(""); + hand_rects >> clip_hand_rects.In(""); + } else { + // While not in stream mode, the input images are not guaranteed to be in + // series, and we don't want to enable the tracking and hand associations + // between input images. Always use the hand detector graph. + image_in >> hand_detector.In("IMAGE"); + norm_rect_in >> hand_detector.In("NORM_RECT"); + auto hand_rects_from_hand_detector = hand_detector.Out("HAND_RECTS"); + hand_rects_from_hand_detector >> clip_hand_rects.In(""); + } auto clipped_hand_rects = clip_hand_rects.Out(""); auto& hand_landmarks_detector_graph = graph.AddNode( From ec327cedcbf1df64bcff6a10252506b7840715e5 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 16:46:12 -0800 Subject: [PATCH 15/21] Internal Changes PiperOrigin-RevId: 487673720 --- mediapipe/gpu/BUILD | 9 +- .../models/gesture_recognizer/BUILD | 51 ++++ .../python/vision/gesture_recognizer/BUILD | 165 ++++++++++++ .../vision/gesture_recognizer/__init__.py | 27 ++ .../vision/gesture_recognizer/constants.py | 20 ++ .../vision/gesture_recognizer/dataset.py | 238 +++++++++++++++++ .../vision/gesture_recognizer/dataset_test.py | 161 ++++++++++++ .../gesture_recognizer/gesture_recognizer.py | 239 ++++++++++++++++++ .../gesture_recognizer_demo.py | 78 ++++++ .../gesture_recognizer_options.py | 32 +++ .../gesture_recognizer_test.py | 132 ++++++++++ .../gesture_recognizer/hyperparameters.py | 40 +++ .../gesture_recognizer/metadata_writer.py | 193 ++++++++++++++ .../metadata_writer_test.py | 90 +++++++ .../gesture_recognizer/model_options.py | 27 ++ .../metadata/custom_gesture_classifier.tflite | Bin 0 -> 6872 bytes .../custom_gesture_classifier_meta.json | 56 ++++ .../0413d5c5-f5ba-476f-a921-ea5e967692a9.jpg | Bin 0 -> 28379 bytes .../044768ad-1709-44ba-b041-c2f8cbe4c166.jpg | Bin 0 -> 27793 bytes .../0e022ee9-74fd-44fe-adad-60c11835e44f.jpg | Bin 0 -> 41322 bytes .../143f8b21-1dc3-4383-bf36-0a54244dfbc0.jpg | Bin 0 -> 15411 bytes .../172ba7f6-c6ba-4398-89a2-25375dccfefa.jpg | Bin 0 -> 21653 bytes .../17b3aa02-dc4d-448d-8601-e2b67193d436.jpg | Bin 0 -> 18798 bytes .../17d804b5-7118-462d-8191-58d764f591b8.jpg | Bin 0 -> 17372 bytes .../1d65a858-623a-4984-9420-958c7e870c3e.jpg | Bin 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b/mediapipe/gpu/BUILD @@ -750,19 +750,12 @@ objc_library( ], ) -proto_library( +mediapipe_proto_library( name = "scale_mode_proto", srcs = ["scale_mode.proto"], visibility = ["//visibility:public"], ) -mediapipe_cc_proto_library( - name = "scale_mode_cc_proto", - srcs = ["scale_mode.proto"], - visibility = ["//visibility:public"], - deps = [":scale_mode_proto"], -) - cc_library( name = "gl_quad_renderer", srcs = ["gl_quad_renderer.cc"], diff --git a/mediapipe/model_maker/models/gesture_recognizer/BUILD b/mediapipe/model_maker/models/gesture_recognizer/BUILD new file mode 100644 index 000000000..f8e5cdd21 --- /dev/null +++ b/mediapipe/model_maker/models/gesture_recognizer/BUILD @@ -0,0 +1,51 @@ +# 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. + +load( + "//mediapipe/framework/tool:mediapipe_files.bzl", + "mediapipe_files", +) + +licenses(["notice"]) + +package( + default_visibility = ["//mediapipe/model_maker/python/vision/gesture_recognizer:__subpackages__"], +) + +mediapipe_files( + srcs = [ + "canned_gesture_classifier.tflite", + "gesture_embedder.tflite", + "gesture_embedder/keras_metadata.pb", + "gesture_embedder/saved_model.pb", + "gesture_embedder/variables/variables.data-00000-of-00001", + "gesture_embedder/variables/variables.index", + "hand_landmark_full.tflite", + "palm_detection_full.tflite", + ], +) + +filegroup( + name = "models", + srcs = [ + "canned_gesture_classifier.tflite", + "gesture_embedder.tflite", + "gesture_embedder/keras_metadata.pb", + "gesture_embedder/saved_model.pb", + "gesture_embedder/variables/variables.data-00000-of-00001", + "gesture_embedder/variables/variables.index", + "hand_landmark_full.tflite", + "palm_detection_full.tflite", + ], +) diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/BUILD b/mediapipe/model_maker/python/vision/gesture_recognizer/BUILD new file mode 100644 index 000000000..b7d334d9c --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/BUILD @@ -0,0 +1,165 @@ +# 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. + +# Placeholder for internal Python strict test compatibility macro. +# Placeholder for internal Python strict library and test compatibility macro. + +licenses(["notice"]) + +package( + default_visibility = ["//mediapipe:__subpackages__"], +) + +# TODO: Remove the unncessary test data once the demo data are moved to an open-sourced +# directory. +filegroup( + name = "test_data", + srcs = glob([ + "test_data/**", + ]), +) + +py_library( + name = "constants", + srcs = ["constants.py"], +) + +# TODO: Change to py_library after migrating the MediaPipe hand solution +# library to MediaPipe hand task library. +py_library( + name = "dataset", + srcs = ["dataset.py"], + deps = [ + ":constants", + "//mediapipe/model_maker/python/core/data:classification_dataset", + "//mediapipe/model_maker/python/core/data:data_util", + "//mediapipe/model_maker/python/core/utils:model_util", + "//mediapipe/python/solutions:hands", + ], +) + +py_test( + name = "dataset_test", + srcs = ["dataset_test.py"], + data = [ + ":test_data", + "//mediapipe/model_maker/models/gesture_recognizer:models", + ], + deps = [ + ":dataset", + "//mediapipe/python/solutions:hands", + "//mediapipe/tasks/python/test:test_utils", + ], +) + +py_library( + name = "hyperparameters", + srcs = ["hyperparameters.py"], + deps = [ + "//mediapipe/model_maker/python/core:hyperparameters", + ], +) + +py_library( + name = "model_options", + srcs = ["model_options.py"], +) + +py_library( + name = "gesture_recognizer_options", + srcs = ["gesture_recognizer_options.py"], + deps = [ + ":hyperparameters", + ":model_options", + ], +) + +py_library( + name = "gesture_recognizer", + srcs = ["gesture_recognizer.py"], + data = ["//mediapipe/model_maker/models/gesture_recognizer:models"], + deps = [ + ":constants", + ":gesture_recognizer_options", + ":hyperparameters", + ":metadata_writer", + ":model_options", + "//mediapipe/model_maker/python/core/data:classification_dataset", + "//mediapipe/model_maker/python/core/tasks:classifier", + "//mediapipe/model_maker/python/core/utils:loss_functions", + "//mediapipe/model_maker/python/core/utils:model_util", + "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer", + ], +) + +py_library( + name = "gesture_recognizer_import", + srcs = ["__init__.py"], + deps = [ + ":dataset", + ":gesture_recognizer", + ":gesture_recognizer_options", + ":hyperparameters", + ":model_options", + ], +) + +py_library( + name = "metadata_writer", + srcs = ["metadata_writer.py"], + deps = [ + "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer", + "//mediapipe/tasks/python/metadata/metadata_writers:writer_utils", + ], +) + +py_test( + name = "gesture_recognizer_test", + size = "large", + srcs = ["gesture_recognizer_test.py"], + data = [ + ":test_data", + "//mediapipe/model_maker/models/gesture_recognizer:models", + ], + shard_count = 2, + deps = [ + ":gesture_recognizer_import", + "//mediapipe/model_maker/python/core/utils:test_util", + "//mediapipe/tasks/python/test:test_utils", + ], +) + +py_test( + name = "metadata_writer_test", + srcs = ["metadata_writer_test.py"], + data = [ + ":test_data", + ], + deps = [ + ":metadata_writer", + "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer", + "//mediapipe/tasks/python/test:test_utils", + ], +) + +py_binary( + name = "gesture_recognizer_demo", + srcs = ["gesture_recognizer_demo.py"], + data = [ + ":test_data", + "//mediapipe/model_maker/models/gesture_recognizer:models", + ], + python_version = "PY3", + deps = [":gesture_recognizer_import"], +) diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/__init__.py b/mediapipe/model_maker/python/vision/gesture_recognizer/__init__.py new file mode 100644 index 000000000..dc6923fac --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/__init__.py @@ -0,0 +1,27 @@ +# 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. +"""MediaPipe Model Maker Python Public API For Gesture Recognizer.""" + +from mediapipe.model_maker.python.vision.gesture_recognizer import dataset +from mediapipe.model_maker.python.vision.gesture_recognizer import gesture_recognizer +from mediapipe.model_maker.python.vision.gesture_recognizer import gesture_recognizer_options +from mediapipe.model_maker.python.vision.gesture_recognizer import hyperparameters +from mediapipe.model_maker.python.vision.gesture_recognizer import model_options + +GestureRecognizer = gesture_recognizer.GestureRecognizer +ModelOptions = model_options.GestureRecognizerModelOptions +HParams = hyperparameters.HParams +Dataset = dataset.Dataset +HandDataPreprocessingParams = dataset.HandDataPreprocessingParams +GestureRecognizerOptions = gesture_recognizer_options.GestureRecognizerOptions diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/constants.py b/mediapipe/model_maker/python/vision/gesture_recognizer/constants.py new file mode 100644 index 000000000..ac9bba12a --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/constants.py @@ -0,0 +1,20 @@ +# 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. +"""Gesture recognition constants.""" + +GESTURE_EMBEDDER_KERAS_MODEL_PATH = 'mediapipe/model_maker/models/gesture_recognizer/gesture_embedder' +GESTURE_EMBEDDER_TFLITE_MODEL_FILE = 'mediapipe/model_maker/models/gesture_recognizer/gesture_embedder.tflite' +HAND_DETECTOR_TFLITE_MODEL_FILE = 'mediapipe/model_maker/models/gesture_recognizer/palm_detection_full.tflite' +HAND_LANDMARKS_DETECTOR_TFLITE_MODEL_FILE = 'mediapipe/model_maker/models/gesture_recognizer/hand_landmark_full.tflite' +CANNED_GESTURE_CLASSIFIER_TFLITE_MODEL_FILE = 'mediapipe/model_maker/models/gesture_recognizer/canned_gesture_classifier.tflite' diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/dataset.py b/mediapipe/model_maker/python/vision/gesture_recognizer/dataset.py new file mode 100644 index 000000000..256f26fd6 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/dataset.py @@ -0,0 +1,238 @@ +# 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. +"""Gesture recognition dataset library.""" + +import dataclasses +import os +import random +from typing import List, NamedTuple, Optional + +import cv2 +import tensorflow as tf + +from mediapipe.model_maker.python.core.data import classification_dataset +from mediapipe.model_maker.python.core.data import data_util +from mediapipe.model_maker.python.core.utils import model_util +from mediapipe.model_maker.python.vision.gesture_recognizer import constants +from mediapipe.python.solutions import hands as mp_hands + + +@dataclasses.dataclass +class HandDataPreprocessingParams: + """A dataclass wraps the hand data preprocessing hyperparameters. + + Attributes: + shuffle: A boolean controlling if shuffle the dataset. Default to true. + min_detection_confidence: confidence threshold for hand detection. + """ + shuffle: bool = True + min_detection_confidence: float = 0.7 + + +@dataclasses.dataclass +class HandData: + """A dataclass represents hand data for training gesture recognizer model. + + See https://google.github.io/mediapipe/solutions/hands#mediapipe-hands for + more details of the hand gesture data API. + + Attributes: + hand: normalized hand landmarks of shape 21x3 from the screen based + hand-landmark model. + world_hand: hand landmarks of shape 21x3 in world coordinates. + handedness: Collection of handedness confidence of the detected hands (i.e. + is it a left or right hand). + """ + hand: List[List[float]] + world_hand: List[List[float]] + handedness: List[float] + + +def _validate_data_sample(data: NamedTuple) -> bool: + """Validates the input hand data sample. + + Args: + data: input hand data sample. + + Returns: + False if the input data namedtuple does not contain the fields including + 'multi_hand_landmarks' or 'multi_hand_world_landmarks' or 'multi_handedness' + or any of these attributes' values are none. Otherwise, True. + """ + if (not hasattr(data, 'multi_hand_landmarks') or + data.multi_hand_landmarks is None): + return False + if (not hasattr(data, 'multi_hand_world_landmarks') or + data.multi_hand_world_landmarks is None): + return False + if not hasattr(data, 'multi_handedness') or data.multi_handedness is None: + return False + return True + + +def _get_hand_data(all_image_paths: List[str], + min_detection_confidence: float) -> Optional[HandData]: + """Computes hand data (landmarks and handedness) in the input image. + + Args: + all_image_paths: all input image paths. + min_detection_confidence: hand detection confidence threshold + + Returns: + A HandData object. Returns None if no hand is detected. + """ + hand_data_result = [] + with mp_hands.Hands( + static_image_mode=True, + max_num_hands=1, + min_detection_confidence=min_detection_confidence) as hands: + for path in all_image_paths: + tf.compat.v1.logging.info('Loading image %s', path) + image = data_util.load_image(path) + # Flip image around y-axis for correct handedness output + image = cv2.flip(image, 1) + data = hands.process(image) + if not _validate_data_sample(data): + hand_data_result.append(None) + continue + hand_landmarks = [[ + hand_landmark.x, hand_landmark.y, hand_landmark.z + ] for hand_landmark in data.multi_hand_landmarks[0].landmark] + hand_world_landmarks = [[ + hand_landmark.x, hand_landmark.y, hand_landmark.z + ] for hand_landmark in data.multi_hand_world_landmarks[0].landmark] + handedness_scores = [ + handedness.score + for handedness in data.multi_handedness[0].classification + ] + hand_data_result.append( + HandData( + hand=hand_landmarks, + world_hand=hand_world_landmarks, + handedness=handedness_scores)) + return hand_data_result + + +class Dataset(classification_dataset.ClassificationDataset): + """Dataset library for hand gesture recognizer.""" + + @classmethod + def from_folder( + cls, + dirname: str, + hparams: Optional[HandDataPreprocessingParams] = None + ) -> classification_dataset.ClassificationDataset: + """Loads images and labels from the given directory. + + Directory contents are expected to be in the format: + //*.jpg". One of the `gesture_name` must be `none` + (case insensitive). The `none` sub-directory is expected to contain images + of hands that don't belong to other gesture classes in . Assumes + the image data of the same label are in the same subdirectory. + + Args: + dirname: Name of the directory containing the data files. + hparams: Optional hyperparameters for processing input hand gesture + images. + + Returns: + Dataset containing landmarks, labels, and other related info. + + Raises: + ValueError: if the input data directory is empty or the label set does not + contain label 'none' (case insensitive). + """ + data_root = os.path.abspath(dirname) + + # Assumes the image data of the same label are in the same subdirectory, + # gets image path and label names. + all_image_paths = list(tf.io.gfile.glob(data_root + r'/*/*')) + if not all_image_paths: + raise ValueError('Image dataset directory is empty.') + + if not hparams: + hparams = HandDataPreprocessingParams() + + if hparams.shuffle: + # Random shuffle data. + random.shuffle(all_image_paths) + + label_names = sorted( + name for name in os.listdir(data_root) + if os.path.isdir(os.path.join(data_root, name))) + if 'none' not in [v.lower() for v in label_names]: + raise ValueError('Label set does not contain label "None".') + # Move label 'none' to the front of label list. + none_idx = [v.lower() for v in label_names].index('none') + none_value = label_names.pop(none_idx) + label_names.insert(0, none_value) + + index_by_label = dict( + (name, index) for index, name in enumerate(label_names)) + all_gesture_indices = [ + index_by_label[os.path.basename(os.path.dirname(path))] + for path in all_image_paths + ] + + # Compute hand data (including local hand landmark, world hand landmark, and + # handedness) for all the input images. + hand_data = _get_hand_data( + all_image_paths=all_image_paths, + min_detection_confidence=hparams.min_detection_confidence) + + # Get a list of the valid hand landmark sample in the hand data list. + valid_indices = [ + i for i in range(len(hand_data)) if hand_data[i] is not None + ] + # Remove 'None' element from the hand data and label list. + valid_hand_data = [dataclasses.asdict(hand_data[i]) for i in valid_indices] + if not valid_hand_data: + raise ValueError('No valid hand is detected.') + + valid_label = [all_gesture_indices[i] for i in valid_indices] + + # Convert list of dictionaries to dictionary of lists. + hand_data_dict = { + k: [lm[k] for lm in valid_hand_data] for k in valid_hand_data[0] + } + hand_ds = tf.data.Dataset.from_tensor_slices(hand_data_dict) + + embedder_model = model_util.load_keras_model( + constants.GESTURE_EMBEDDER_KERAS_MODEL_PATH) + + hand_ds = hand_ds.batch(batch_size=1) + hand_embedding_ds = hand_ds.map( + map_func=lambda feature: embedder_model(dict(feature)), + num_parallel_calls=tf.data.experimental.AUTOTUNE) + hand_embedding_ds = hand_embedding_ds.unbatch() + + # Create label dataset + label_ds = tf.data.Dataset.from_tensor_slices( + tf.cast(valid_label, tf.int64)) + + label_one_hot_ds = label_ds.map( + map_func=lambda index: tf.one_hot(index, len(label_names)), + num_parallel_calls=tf.data.experimental.AUTOTUNE) + + # Create a dataset with (hand_embedding, one_hot_label) pairs + hand_embedding_label_ds = tf.data.Dataset.zip( + (hand_embedding_ds, label_one_hot_ds)) + + tf.compat.v1.logging.info( + 'Load valid hands with size: {}, num_label: {}, labels: {}.'.format( + len(valid_hand_data), len(label_names), ','.join(label_names))) + return Dataset( + dataset=hand_embedding_label_ds, + size=len(valid_hand_data), + label_names=label_names) diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/dataset_test.py b/mediapipe/model_maker/python/vision/gesture_recognizer/dataset_test.py new file mode 100644 index 000000000..76e70a58d --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/dataset_test.py @@ -0,0 +1,161 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved.s +# +# 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. + +import collections +import os +import shutil +from typing import NamedTuple +import unittest + +from absl import flags +from absl.testing import parameterized +import tensorflow as tf + +from mediapipe.model_maker.python.vision.gesture_recognizer import dataset +from mediapipe.python.solutions import hands as mp_hands +from mediapipe.tasks.python.test import test_utils + +FLAGS = flags.FLAGS + +_TEST_DATA_DIRNAME = 'raw_data' + + +class DatasetTest(tf.test.TestCase, parameterized.TestCase): + + def test_split(self): + input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME) + data = dataset.Dataset.from_folder( + dirname=input_data_dir, hparams=dataset.HandDataPreprocessingParams()) + train_data, test_data = data.split(0.5) + + self.assertLen(train_data, 17) + for _, elem in enumerate(train_data.gen_tf_dataset(is_training=True)): + self.assertEqual(elem[0].shape, (1, 128)) + self.assertEqual(elem[1].shape, ([1, 4])) + self.assertEqual(train_data.num_classes, 4) + self.assertEqual(train_data.label_names, ['none', 'call', 'four', 'rock']) + + self.assertLen(test_data, 18) + for _, elem in enumerate(test_data.gen_tf_dataset(is_training=True)): + self.assertEqual(elem[0].shape, (1, 128)) + self.assertEqual(elem[1].shape, ([1, 4])) + self.assertEqual(test_data.num_classes, 4) + self.assertEqual(test_data.label_names, ['none', 'call', 'four', 'rock']) + + def test_from_folder(self): + input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME) + data = dataset.Dataset.from_folder( + dirname=input_data_dir, hparams=dataset.HandDataPreprocessingParams()) + for _, elem in enumerate(data.gen_tf_dataset(is_training=True)): + self.assertEqual(elem[0].shape, (1, 128)) + self.assertEqual(elem[1].shape, ([1, 4])) + self.assertLen(data, 35) + self.assertEqual(data.num_classes, 4) + self.assertEqual(data.label_names, ['none', 'call', 'four', 'rock']) + + def test_create_dataset_from_empty_folder_raise_value_error(self): + with self.assertRaisesRegex(ValueError, 'Image dataset directory is empty'): + dataset.Dataset.from_folder( + dirname=self.get_temp_dir(), + hparams=dataset.HandDataPreprocessingParams()) + + def test_create_dataset_from_folder_without_none_raise_value_error(self): + input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME) + tmp_dir = self.create_tempdir() + # Copy input dataset to a temporary directory and skip 'None' directory. + for name in os.listdir(input_data_dir): + if name == 'none': + continue + src_dir = os.path.join(input_data_dir, name) + dst_dir = os.path.join(tmp_dir, name) + shutil.copytree(src_dir, dst_dir) + + with self.assertRaisesRegex(ValueError, + 'Label set does not contain label "None"'): + dataset.Dataset.from_folder( + dirname=tmp_dir, hparams=dataset.HandDataPreprocessingParams()) + + def test_create_dataset_from_folder_with_capital_letter_in_folder_name(self): + input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME) + tmp_dir = self.create_tempdir() + # Copy input dataset to a temporary directory and change the base folder + # name to upper case letter, e.g. 'none' -> 'NONE' + for name in os.listdir(input_data_dir): + src_dir = os.path.join(input_data_dir, name) + dst_dir = os.path.join(tmp_dir, name.upper()) + shutil.copytree(src_dir, dst_dir) + + upper_base_folder_name = list(os.listdir(tmp_dir)) + self.assertCountEqual(upper_base_folder_name, + ['CALL', 'FOUR', 'NONE', 'ROCK']) + + data = dataset.Dataset.from_folder( + dirname=tmp_dir, hparams=dataset.HandDataPreprocessingParams()) + for _, elem in enumerate(data.gen_tf_dataset(is_training=True)): + self.assertEqual(elem[0].shape, (1, 128)) + self.assertEqual(elem[1].shape, ([1, 4])) + self.assertLen(data, 35) + self.assertEqual(data.num_classes, 4) + self.assertEqual(data.label_names, ['NONE', 'CALL', 'FOUR', 'ROCK']) + + @parameterized.named_parameters( + dict( + testcase_name='invalid_field_name_multi_hand_landmark', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmark', 'multi_hand_world_landmarks', + 'multi_handedness' + ])(1, 2, 3)), + dict( + testcase_name='invalid_field_name_multi_hand_world_landmarks', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmarks', 'multi_hand_world_landmark', + 'multi_handedness' + ])(1, 2, 3)), + dict( + testcase_name='invalid_field_name_multi_handed', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmarks', 'multi_hand_world_landmarks', + 'multi_handed' + ])(1, 2, 3)), + dict( + testcase_name='multi_hand_landmarks_is_none', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmarks', 'multi_hand_world_landmarks', + 'multi_handedness' + ])(None, 2, 3)), + dict( + testcase_name='multi_hand_world_landmarks_is_none', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmarks', 'multi_hand_world_landmarks', + 'multi_handedness' + ])(1, None, 3)), + dict( + testcase_name='multi_handedness_is_none', + hand=collections.namedtuple('Hand', [ + 'multi_hand_landmarks', 'multi_hand_world_landmarks', + 'multi_handedness' + ])(1, 2, None)), + ) + def test_create_dataset_from_invalid_hand_data(self, hand: NamedTuple): + with unittest.mock.patch.object( + mp_hands.Hands, 'process', return_value=hand): + input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME) + with self.assertRaisesRegex(ValueError, 'No valid hand is detected'): + dataset.Dataset.from_folder( + dirname=input_data_dir, + hparams=dataset.HandDataPreprocessingParams()) + + +if __name__ == '__main__': + tf.test.main() diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer.py b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer.py new file mode 100644 index 000000000..f297d8640 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer.py @@ -0,0 +1,239 @@ +# 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. +"""APIs to train gesture recognizer model.""" + +import os +from typing import List + +import tensorflow as tf + +from mediapipe.model_maker.python.core.data import classification_dataset as classification_ds +from mediapipe.model_maker.python.core.tasks import classifier +from mediapipe.model_maker.python.core.utils import loss_functions +from mediapipe.model_maker.python.core.utils import model_util +from mediapipe.model_maker.python.vision.gesture_recognizer import constants +from mediapipe.model_maker.python.vision.gesture_recognizer import gesture_recognizer_options +from mediapipe.model_maker.python.vision.gesture_recognizer import hyperparameters as hp +from mediapipe.model_maker.python.vision.gesture_recognizer import metadata_writer +from mediapipe.model_maker.python.vision.gesture_recognizer import model_options as model_opt +from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer as base_metadata_writer + +_EMBEDDING_SIZE = 128 + + +class GestureRecognizer(classifier.Classifier): + """GestureRecognizer for building hand gesture recognizer model. + + Attributes: + embedding_size: Size of the input gesture embedding vector. + """ + + def __init__(self, label_names: List[str], + model_options: model_opt.GestureRecognizerModelOptions, + hparams: hp.HParams): + """Initializes GestureRecognizer class. + + Args: + label_names: A list of label names for the classes. + model_options: options to create gesture recognizer model. + hparams: The hyperparameters for training hand gesture recognizer model. + """ + super().__init__( + model_spec=None, label_names=label_names, shuffle=hparams.shuffle) + self._model_options = model_options + self._hparams = hparams + self._history = None + self.embedding_size = _EMBEDDING_SIZE + + @classmethod + def create( + cls, + train_data: classification_ds.ClassificationDataset, + validation_data: classification_ds.ClassificationDataset, + options: gesture_recognizer_options.GestureRecognizerOptions, + ) -> 'GestureRecognizer': + """Creates and trains a hand gesture recognizer with input datasets. + + If a checkpoint file exists in the {options.hparams.export_dir}/checkpoint/ + directory, the training process will load the weight from the checkpoint + file for continual training. + + Args: + train_data: Training data. + validation_data: Validation data. If None, skips validation process. + options: options for creating and training gesture recognizer model. + + Returns: + An instance of GestureRecognizer. + """ + if options.model_options is None: + options.model_options = model_opt.GestureRecognizerModelOptions() + + if options.hparams is None: + options.hparams = hp.HParams() + + gesture_recognizer = cls( + label_names=train_data.label_names, + model_options=options.model_options, + hparams=options.hparams) + + gesture_recognizer._create_model() + + train_dataset = train_data.gen_tf_dataset( + batch_size=options.hparams.batch_size, + is_training=True, + shuffle=options.hparams.shuffle) + options.hparams.steps_per_epoch = model_util.get_steps_per_epoch( + steps_per_epoch=options.hparams.steps_per_epoch, + batch_size=options.hparams.batch_size, + train_data=train_data) + train_dataset = train_dataset.take(count=options.hparams.steps_per_epoch) + + validation_dataset = validation_data.gen_tf_dataset( + batch_size=options.hparams.batch_size, is_training=False) + + tf.compat.v1.logging.info('Training the gesture recognizer model...') + gesture_recognizer._train( + train_data=train_dataset, validation_data=validation_dataset) + + return gesture_recognizer + + def _train(self, train_data: tf.data.Dataset, + validation_data: tf.data.Dataset): + """Trains the model with input train_data. + + The training results are recorded by a self.History object returned by + tf.keras.Model.fit(). + + Args: + train_data: Training data. + validation_data: Validation data. + """ + hparams = self._hparams + + scheduler = lambda epoch: hparams.learning_rate * (hparams.lr_decay**epoch) + scheduler_callback = tf.keras.callbacks.LearningRateScheduler(scheduler) + + job_dir = hparams.export_dir + checkpoint_path = os.path.join(job_dir, 'epoch_models') + checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( + os.path.join(checkpoint_path, 'model-{epoch:04d}'), + save_weights_only=True) + + best_model_path = os.path.join(job_dir, 'best_model_weights') + best_model_callback = tf.keras.callbacks.ModelCheckpoint( + best_model_path, + monitor='val_loss', + mode='min', + save_best_only=True, + save_weights_only=True) + + tensorboard_callback = tf.keras.callbacks.TensorBoard( + log_dir=os.path.join(job_dir, 'logs')) + + self._model.compile( + optimizer='adam', + loss=loss_functions.FocalLoss(gamma=self._hparams.gamma), + metrics=['categorical_accuracy']) + + latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path) + if latest_checkpoint: + print(f'Resuming from {latest_checkpoint}') + self._model.load_weights(latest_checkpoint) + + self._history = self._model.fit( + x=train_data, + epochs=hparams.epochs, + validation_data=validation_data, + validation_freq=1, + callbacks=[ + checkpoint_callback, best_model_callback, scheduler_callback, + tensorboard_callback + ], + ) + + def _create_model(self): + """Creates the hand gesture recognizer model. + + The gesture embedding model is pretrained and loaded from a tf.saved_model. + """ + inputs = tf.keras.Input( + shape=[self.embedding_size], + batch_size=None, + dtype=tf.float32, + name='hand_embedding') + + x = tf.keras.layers.BatchNormalization()(inputs) + x = tf.keras.layers.ReLU()(x) + dropout_rate = self._model_options.dropout_rate + x = tf.keras.layers.Dropout(rate=dropout_rate, name='dropout')(x) + outputs = tf.keras.layers.Dense( + self._num_classes, + activation='softmax', + name='custom_gesture_recognizer')( + x) + + self._model = tf.keras.Model(inputs=inputs, outputs=outputs) + + print(self._model.summary()) + + def export_model(self, model_name: str = 'gesture_recognizer.task'): + """Converts the model to TFLite and exports as a model bundle file. + + Saves a model bundle file and metadata json file to hparams.export_dir. The + resulting model bundle file will contain necessary models for hand + detection, canned gesture classification, and customized gesture + classification. Only the model bundle file is needed for the downstream + gesture recognition task. The metadata.json file is saved only to + interpret the contents of the model bundle file. + + The customized gesture model is in float without quantization. The model is + lightweight and there is no need to balance performance and efficiency by + quantization. The default score_thresholding is set to 0.5 as it can be + adjusted during inference. + + Args: + model_name: File name to save model bundle file. The full export path is + {export_dir}/{model_name}. + """ + # TODO: Convert keras embedder model instead of using tflite + gesture_embedding_model_buffer = model_util.load_tflite_model_buffer( + constants.GESTURE_EMBEDDER_TFLITE_MODEL_FILE) + hand_detector_model_buffer = model_util.load_tflite_model_buffer( + constants.HAND_DETECTOR_TFLITE_MODEL_FILE) + hand_landmarks_detector_model_buffer = model_util.load_tflite_model_buffer( + constants.HAND_LANDMARKS_DETECTOR_TFLITE_MODEL_FILE) + canned_gesture_model_buffer = model_util.load_tflite_model_buffer( + constants.CANNED_GESTURE_CLASSIFIER_TFLITE_MODEL_FILE) + + if not tf.io.gfile.exists(self._hparams.export_dir): + tf.io.gfile.makedirs(self._hparams.export_dir) + model_bundle_file = os.path.join(self._hparams.export_dir, model_name) + metadata_file = os.path.join(self._hparams.export_dir, 'metadata.json') + + gesture_classifier_options = metadata_writer.GestureClassifierOptions( + model_buffer=model_util.convert_to_tflite(self._model), + labels=base_metadata_writer.Labels().add(list(self._label_names)), + score_thresholding=base_metadata_writer.ScoreThresholding( + global_score_threshold=0.5)) + + writer = metadata_writer.MetadataWriter.create( + hand_detector_model_buffer, hand_landmarks_detector_model_buffer, + gesture_embedding_model_buffer, canned_gesture_model_buffer, + gesture_classifier_options) + model_bundle_content, metadata_json = writer.populate() + with open(model_bundle_file, 'wb') as f: + f.write(model_bundle_content) + with open(metadata_file, 'w') as f: + f.write(metadata_json) diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_demo.py b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_demo.py new file mode 100644 index 000000000..06075fbc6 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_demo.py @@ -0,0 +1,78 @@ +# 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. +"""Demo for making an gesture recognizer model by Mediapipe Model Maker.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +# Dependency imports + +from absl import app +from absl import flags +from absl import logging + +from mediapipe.model_maker.python.vision import gesture_recognizer + +FLAGS = flags.FLAGS + +# TODO: Move hand gesture recognizer demo dataset to an +# open-sourced directory. +TEST_DATA_DIR = 'mediapipe/model_maker/python/vision/gesture_recognizer/test_data/raw_data' + + +def define_flags(): + flags.DEFINE_string('export_dir', None, + 'The directory to save exported files.') + flags.DEFINE_string('input_data_dir', None, + 'The directory with input training data.') + flags.mark_flag_as_required('export_dir') + + +def run(data_dir: str, export_dir: str): + """Runs demo.""" + data = gesture_recognizer.Dataset.from_folder(dirname=data_dir) + train_data, rest_data = data.split(0.8) + validation_data, test_data = rest_data.split(0.5) + + model = gesture_recognizer.GestureRecognizer.create( + train_data=train_data, + validation_data=validation_data, + options=gesture_recognizer.GestureRecognizerOptions( + hparams=gesture_recognizer.HParams(export_dir=export_dir))) + + metric = model.evaluate(test_data, batch_size=2) + print('Evaluation metric') + print(metric) + + model.export_model() + + +def main(_): + logging.set_verbosity(logging.INFO) + + if FLAGS.input_data_dir is None: + data_dir = os.path.join(FLAGS.test_srcdir, TEST_DATA_DIR) + else: + data_dir = FLAGS.input_data_dir + + export_dir = os.path.expanduser(FLAGS.export_dir) + run(data_dir=data_dir, export_dir=export_dir) + + +if __name__ == '__main__': + define_flags() + app.run(main) diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_options.py b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_options.py new file mode 100644 index 000000000..da9e2d647 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_options.py @@ -0,0 +1,32 @@ +# 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. +"""Options for building gesture recognizer.""" + +import dataclasses +from typing import Optional + +from mediapipe.model_maker.python.vision.gesture_recognizer import hyperparameters +from mediapipe.model_maker.python.vision.gesture_recognizer import model_options as model_opt + + +@dataclasses.dataclass +class GestureRecognizerOptions: + """Configurable options for building gesture recognizer. + + Attributes: + model_options: A set of options for configuring the selected model. + hparams: A set of hyperparameters used to train the gesture recognizer. + """ + model_options: Optional[model_opt.GestureRecognizerModelOptions] = None + hparams: Optional[hyperparameters.HParams] = None diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_test.py b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_test.py new file mode 100644 index 000000000..eb2b1d171 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/gesture_recognizer_test.py @@ -0,0 +1,132 @@ +# 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. + +import io +import os +from unittest import mock as unittest_mock +import zipfile + +import mock +import tensorflow as tf + +from mediapipe.model_maker.python.core.utils import test_util +from mediapipe.model_maker.python.vision import gesture_recognizer +from mediapipe.tasks.python.test import test_utils + +_TEST_DATA_DIR = 'mediapipe/model_maker/python/vision/gesture_recognizer/test_data' + + +class GestureRecognizerTest(tf.test.TestCase): + + def _load_data(self): + input_data_dir = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, 'raw_data')) + + data = gesture_recognizer.Dataset.from_folder( + dirname=input_data_dir, + hparams=gesture_recognizer.HandDataPreprocessingParams(shuffle=True)) + return data + + def setUp(self): + super().setUp() + self._model_options = gesture_recognizer.ModelOptions() + self._hparams = gesture_recognizer.HParams(epochs=2) + self._gesture_recognizer_options = ( + gesture_recognizer.GestureRecognizerOptions( + model_options=self._model_options, hparams=self._hparams)) + all_data = self._load_data() + # Splits data, 90% data for training, 10% for testing + self._train_data, self._test_data = all_data.split(0.9) + + def test_gesture_recognizer_model(self): + model = gesture_recognizer.GestureRecognizer.create( + train_data=self._train_data, + validation_data=self._test_data, + options=self._gesture_recognizer_options) + + self._test_accuracy(model) + + def test_export_gesture_recognizer_model(self): + model = gesture_recognizer.GestureRecognizer.create( + train_data=self._train_data, + validation_data=self._test_data, + options=self._gesture_recognizer_options) + model.export_model() + model_bundle_file = os.path.join(self._hparams.export_dir, + 'gesture_recognizer.task') + with zipfile.ZipFile(model_bundle_file) as zf: + self.assertEqual( + set(zf.namelist()), + set(['hand_landmarker.task', 'hand_gesture_recognizer.task'])) + zf.extractall(self.get_temp_dir()) + hand_gesture_recognizer_bundle_file = os.path.join( + self.get_temp_dir(), 'hand_gesture_recognizer.task') + with zipfile.ZipFile(hand_gesture_recognizer_bundle_file) as zf: + self.assertEqual( + set(zf.namelist()), + set([ + 'canned_gesture_classifier.tflite', + 'custom_gesture_classifier.tflite', 'gesture_embedder.tflite' + ])) + zf.extractall(self.get_temp_dir()) + gesture_classifier_tflite_file = os.path.join( + self.get_temp_dir(), 'custom_gesture_classifier.tflite') + test_util.test_tflite_file( + keras_model=model._model, + tflite_file=gesture_classifier_tflite_file, + size=[1, model.embedding_size]) + + def _test_accuracy(self, model, threshold=0.5): + _, accuracy = model.evaluate(self._test_data) + tf.compat.v1.logging.info(f'accuracy: {accuracy}') + self.assertGreaterEqual(accuracy, threshold) + + @unittest_mock.patch.object( + gesture_recognizer.hyperparameters, + 'HParams', + autospec=True, + return_value=gesture_recognizer.HParams(epochs=1)) + @unittest_mock.patch.object( + gesture_recognizer.model_options, + 'GestureRecognizerModelOptions', + autospec=True, + return_value=gesture_recognizer.ModelOptions()) + def test_create_hparams_and_model_options_if_none_in_image_classifier_options( + self, mock_hparams, mock_model_options): + options = gesture_recognizer.GestureRecognizerOptions() + gesture_recognizer.GestureRecognizer.create( + train_data=self._train_data, + validation_data=self._test_data, + options=options) + mock_hparams.assert_called_once() + mock_model_options.assert_called_once() + + def test_continual_training_by_loading_checkpoint(self): + mock_stdout = io.StringIO() + with mock.patch('sys.stdout', mock_stdout): + model = gesture_recognizer.GestureRecognizer.create( + train_data=self._train_data, + validation_data=self._test_data, + options=self._gesture_recognizer_options) + model = gesture_recognizer.GestureRecognizer.create( + train_data=self._train_data, + validation_data=self._test_data, + options=self._gesture_recognizer_options) + self._test_accuracy(model) + + self.assertRegex(mock_stdout.getvalue(), 'Resuming from') + + +if __name__ == '__main__': + tf.test.main() diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/hyperparameters.py b/mediapipe/model_maker/python/vision/gesture_recognizer/hyperparameters.py new file mode 100644 index 000000000..fed62453b --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/hyperparameters.py @@ -0,0 +1,40 @@ +# 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. +"""Hyperparameters for training customized gesture recognizer models.""" + +import dataclasses + +from mediapipe.model_maker.python.core import hyperparameters as hp + + +@dataclasses.dataclass +class HParams(hp.BaseHParams): + """The hyperparameters for training gesture recognizer. + + Attributes: + learning_rate: Learning rate to use for gradient descent training. + batch_size: Batch size for training. + epochs: Number of training iterations over the dataset. + lr_decay: Learning rate decay to use for gradient descent training. + gamma: Gamma parameter for focal loss. + """ + # Parameters from BaseHParams class. + learning_rate: float = 0.001 + batch_size: int = 2 + epochs: int = 10 + + # Parameters about training configuration + # TODO: Move lr_decay to hp.baseHParams. + lr_decay: float = 0.99 + gamma: int = 2 diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer.py b/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer.py new file mode 100644 index 000000000..58b67e072 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer.py @@ -0,0 +1,193 @@ +# 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. +# ============================================================================== +"""Writes metadata and creates model asset bundle for gesture recognizer.""" + +import dataclasses +import os +import tempfile +from typing import Union + +import tensorflow as tf +from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer +from mediapipe.tasks.python.metadata.metadata_writers import writer_utils + +_HAND_DETECTOR_TFLITE_NAME = "hand_detector.tflite" +_HAND_LANDMARKS_DETECTOR_TFLITE_NAME = "hand_landmarks_detector.tflite" +_HAND_LANDMARKER_BUNDLE_NAME = "hand_landmarker.task" +_HAND_GESTURE_RECOGNIZER_BUNDLE_NAME = "hand_gesture_recognizer.task" +_GESTURE_EMBEDDER_TFLITE_NAME = "gesture_embedder.tflite" +_CANNED_GESTURE_CLASSIFIER_TFLITE_NAME = "canned_gesture_classifier.tflite" +_CUSTOM_GESTURE_CLASSIFIER_TFLITE_NAME = "custom_gesture_classifier.tflite" + +_MODEL_NAME = "HandGestureRecognition" +_MODEL_DESCRIPTION = "Recognize the hand gesture in the image." + +_INPUT_NAME = "embedding" +_INPUT_DESCRIPTION = "Embedding feature vector from gesture embedder." +_OUTPUT_NAME = "scores" +_OUTPUT_DESCRIPTION = "Hand gesture category scores." + + +@dataclasses.dataclass +class GestureClassifierOptions: + """Options to write metadata for gesture classifier. + + Attributes: + model_buffer: Gesture classifier TFLite model buffer. + labels: Labels for the gesture classifier. + score_thresholding: Parameters to performs thresholding on output tensor + values [1]. + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L468 + """ + model_buffer: bytearray + labels: metadata_writer.Labels + score_thresholding: metadata_writer.ScoreThresholding + + +def read_file(file_path: str, mode: str = "rb") -> Union[str, bytes]: + with tf.io.gfile.GFile(file_path, mode) as f: + return f.read() + + +class MetadataWriter: + """MetadataWriter to write the metadata and the model asset bundle.""" + + def __init__( + self, hand_detector_model_buffer: bytearray, + hand_landmarks_detector_model_buffer: bytearray, + gesture_embedder_model_buffer: bytearray, + canned_gesture_classifier_model_buffer: bytearray, + custom_gesture_classifier_metadata_writer: metadata_writer.MetadataWriter + ) -> None: + """Initialize MetadataWriter to write the metadata and model asset bundle. + + Args: + hand_detector_model_buffer: A valid flatbuffer *with* metadata loaded from + the TFLite hand detector model file. + hand_landmarks_detector_model_buffer: A valid flatbuffer *with* metadata + loaded from the TFLite hand landmarks detector model file. + gesture_embedder_model_buffer: A valid flatbuffer *with* metadata loaded + from the TFLite gesture embedder model file. + canned_gesture_classifier_model_buffer: A valid flatbuffer *with* metadata + loaded from the TFLite canned gesture classifier model file. + custom_gesture_classifier_metadata_writer: Metadata writer to write custom + gesture classifier metadata into the TFLite file. + """ + self._hand_detector_model_buffer = hand_detector_model_buffer + self._hand_landmarks_detector_model_buffer = hand_landmarks_detector_model_buffer + self._gesture_embedder_model_buffer = gesture_embedder_model_buffer + self._canned_gesture_classifier_model_buffer = canned_gesture_classifier_model_buffer + self._custom_gesture_classifier_metadata_writer = custom_gesture_classifier_metadata_writer + self._temp_folder = tempfile.TemporaryDirectory() + + def __del__(self): + if os.path.exists(self._temp_folder.name): + self._temp_folder.cleanup() + + @classmethod + def create( + cls, + hand_detector_model_buffer: bytearray, + hand_landmarks_detector_model_buffer: bytearray, + gesture_embedder_model_buffer: bytearray, + canned_gesture_classifier_model_buffer: bytearray, + custom_gesture_classifier_options: GestureClassifierOptions, + ) -> "MetadataWriter": + """Creates MetadataWriter to write the metadata for gesture recognizer. + + Args: + hand_detector_model_buffer: A valid flatbuffer *with* metadata loaded from + the TFLite hand detector model file. + hand_landmarks_detector_model_buffer: A valid flatbuffer *with* metadata + loaded from the TFLite hand landmarks detector model file. + gesture_embedder_model_buffer: A valid flatbuffer *with* metadata loaded + from the TFLite gesture embedder model file. + canned_gesture_classifier_model_buffer: A valid flatbuffer *with* metadata + loaded from the TFLite canned gesture classifier model file. + custom_gesture_classifier_options: Custom gesture classifier options to + write custom gesture classifier metadata into the TFLite file. + + Returns: + An MetadataWrite object. + """ + writer = metadata_writer.MetadataWriter.create( + custom_gesture_classifier_options.model_buffer) + writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION) + writer.add_feature_input(name=_INPUT_NAME, description=_INPUT_DESCRIPTION) + writer.add_classification_output( + labels=custom_gesture_classifier_options.labels, + score_thresholding=custom_gesture_classifier_options.score_thresholding, + name=_OUTPUT_NAME, + description=_OUTPUT_DESCRIPTION) + return cls(hand_detector_model_buffer, hand_landmarks_detector_model_buffer, + gesture_embedder_model_buffer, + canned_gesture_classifier_model_buffer, writer) + + def populate(self): + """Populates the metadata and creates model asset bundle. + + Note that only the output model asset bundle is used for deployment. + The output JSON content is used to interpret the custom gesture classifier + metadata content. + + Returns: + A tuple of (model_asset_bundle_in_bytes, metadata_json_content) + """ + # Creates the model asset bundle for hand landmarker task. + landmark_models = { + _HAND_DETECTOR_TFLITE_NAME: + self._hand_detector_model_buffer, + _HAND_LANDMARKS_DETECTOR_TFLITE_NAME: + self._hand_landmarks_detector_model_buffer + } + output_hand_landmarker_path = os.path.join(self._temp_folder.name, + _HAND_LANDMARKER_BUNDLE_NAME) + writer_utils.create_model_asset_bundle(landmark_models, + output_hand_landmarker_path) + + # Write metadata into custom gesture classifier model. + self._custom_gesture_classifier_model_buffer, custom_gesture_classifier_metadata_json = self._custom_gesture_classifier_metadata_writer.populate( + ) + # Creates the model asset bundle for hand gesture recognizer sub graph. + hand_gesture_recognizer_models = { + _GESTURE_EMBEDDER_TFLITE_NAME: + self._gesture_embedder_model_buffer, + _CANNED_GESTURE_CLASSIFIER_TFLITE_NAME: + self._canned_gesture_classifier_model_buffer, + _CUSTOM_GESTURE_CLASSIFIER_TFLITE_NAME: + self._custom_gesture_classifier_model_buffer + } + output_hand_gesture_recognizer_path = os.path.join( + self._temp_folder.name, _HAND_GESTURE_RECOGNIZER_BUNDLE_NAME) + writer_utils.create_model_asset_bundle(hand_gesture_recognizer_models, + output_hand_gesture_recognizer_path) + + # Creates the model asset bundle for end-to-end hand gesture recognizer + # graph. + gesture_recognizer_models = { + _HAND_LANDMARKER_BUNDLE_NAME: + read_file(output_hand_landmarker_path), + _HAND_GESTURE_RECOGNIZER_BUNDLE_NAME: + read_file(output_hand_gesture_recognizer_path), + } + + output_file_path = os.path.join(self._temp_folder.name, + "gesture_recognizer.task") + writer_utils.create_model_asset_bundle(gesture_recognizer_models, + output_file_path) + with open(output_file_path, "rb") as f: + gesture_recognizer_model_buffer = f.read() + return gesture_recognizer_model_buffer, custom_gesture_classifier_metadata_json diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer_test.py b/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer_test.py new file mode 100644 index 000000000..e1101e066 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer_test.py @@ -0,0 +1,90 @@ +# 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. +# ============================================================================== +"""Tests for metadata_writer.""" + +import os +import zipfile + +import tensorflow as tf + +from mediapipe.model_maker.python.vision.gesture_recognizer import metadata_writer +from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer as base_metadata_writer +from mediapipe.tasks.python.test import test_utils + +_TEST_DATA_DIR = "mediapipe/model_maker/python/vision/gesture_recognizer/test_data/metadata" + +_EXPECTED_JSON = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, "custom_gesture_classifier_meta.json")) +_CUSTOM_GESTURE_CLASSIFIER_PATH = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, "custom_gesture_classifier.tflite")) + + +class MetadataWriterTest(tf.test.TestCase): + + def test_write_metadata_and_create_model_asset_bundle_successful(self): + # Use dummy model buffer for unit test only. + hand_detector_model_buffer = b"\x11\x12" + hand_landmarks_detector_model_buffer = b"\x22" + gesture_embedder_model_buffer = b"\x33" + canned_gesture_classifier_model_buffer = b"\x44" + custom_gesture_classifier_metadata_writer = metadata_writer.GestureClassifierOptions( + model_buffer=metadata_writer.read_file(_CUSTOM_GESTURE_CLASSIFIER_PATH), + labels=base_metadata_writer.Labels().add( + ["None", "Paper", "Rock", "Scissors"]), + score_thresholding=base_metadata_writer.ScoreThresholding( + global_score_threshold=0.5)) + writer = metadata_writer.MetadataWriter.create( + hand_detector_model_buffer, hand_landmarks_detector_model_buffer, + gesture_embedder_model_buffer, canned_gesture_classifier_model_buffer, + custom_gesture_classifier_metadata_writer) + model_bundle_content, metadata_json = writer.populate() + with open(_EXPECTED_JSON, "r") as f: + expected_json = f.read() + self.assertEqual(metadata_json, expected_json) + + # Checks the top-level model bundle can be extracted successfully. + model_bundle_filepath = os.path.join(self.get_temp_dir(), + "gesture_recognition.task") + + with open(model_bundle_filepath, "wb") as f: + f.write(model_bundle_content) + + with zipfile.ZipFile(model_bundle_filepath) as zf: + self.assertEqual( + set(zf.namelist()), + set(["hand_landmarker.task", "hand_gesture_recognizer.task"])) + zf.extractall(self.get_temp_dir()) + + # Checks the model bundles for sub-task can be extracted successfully. + hand_landmarker_bundle_filepath = os.path.join(self.get_temp_dir(), + "hand_landmarker.task") + with zipfile.ZipFile(hand_landmarker_bundle_filepath) as zf: + self.assertEqual( + set(zf.namelist()), + set(["hand_landmarks_detector.tflite", "hand_detector.tflite"])) + + hand_gesture_recognizer_bundle_filepath = os.path.join( + self.get_temp_dir(), "hand_gesture_recognizer.task") + with zipfile.ZipFile(hand_gesture_recognizer_bundle_filepath) as zf: + self.assertEqual( + set(zf.namelist()), + set([ + "canned_gesture_classifier.tflite", + "custom_gesture_classifier.tflite", "gesture_embedder.tflite" + ])) + + +if __name__ == "__main__": + tf.test.main() diff --git a/mediapipe/model_maker/python/vision/gesture_recognizer/model_options.py b/mediapipe/model_maker/python/vision/gesture_recognizer/model_options.py new file mode 100644 index 000000000..79a84c792 --- /dev/null +++ b/mediapipe/model_maker/python/vision/gesture_recognizer/model_options.py @@ -0,0 +1,27 @@ +# 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. +"""Configurable model options for gesture recognizer models.""" + +import dataclasses + + +@dataclasses.dataclass +class GestureRecognizerModelOptions: + """Configurable options for gesture recognizer model. + + Attributes: + dropout_rate: The fraction of the input units to drop, used in dropout + layer. + """ + dropout_rate: float = 0.05 diff --git 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z(RKv^X#W7-6u)P2{6enrMg}>e;87OtM+6#CYbG)N+6TdKZRY|bHy$QyW;p<8>-iv`r xZQLGDA4+$U+~)$W0^H(|l6`*~E(Kp9yOEEjDT#uTIX;-HAoj%sV0WMo|JhTEHNyY^ literal 0 HcmV?d00001 diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index 8548a60d8..debe5404a 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -120,6 +120,34 @@ py_library( ], ) +py_library( + name = "solution_base", + srcs = ["solution_base.py"], + srcs_version = "PY3", + visibility = [ + "//mediapipe/python:__subpackages__", + ], + deps = [ + ":_framework_bindings", + ":packet_creator", + ":packet_getter", + "//mediapipe/calculators/core:constant_side_packet_calculator_py_pb2", + "//mediapipe/calculators/image:image_transformation_calculator_py_pb2", + "//mediapipe/calculators/tensor:tensors_to_detections_calculator_py_pb2", + "//mediapipe/calculators/util:landmarks_smoothing_calculator_py_pb2", + "//mediapipe/calculators/util:logic_calculator_py_pb2", + "//mediapipe/calculators/util:thresholding_calculator_py_pb2", + "//mediapipe/framework:calculator_py_pb2", + "//mediapipe/framework/formats:classification_py_pb2", + "//mediapipe/framework/formats:detection_py_pb2", + "//mediapipe/framework/formats:landmark_py_pb2", + "//mediapipe/framework/formats:rect_py_pb2", + "//mediapipe/modules/objectron/calculators:annotation_py_pb2", + "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator_py_pb2", + "@com_google_protobuf//:protobuf_python", + ], +) + py_test( name = "calculator_graph_test", srcs = ["calculator_graph_test.py"], @@ -175,3 +203,24 @@ py_test( ":_framework_bindings", ], ) + +py_test( + name = "solution_base_test", + srcs = ["solution_base_test.py"], + python_version = "PY3", + srcs_version = "PY3", + deps = [ + ":solution_base", + "//file/google_src", + "//file/localfile", + "//mediapipe/calculators/core:gate_calculator", + "//mediapipe/calculators/core:pass_through_calculator", + "//mediapipe/calculators/core:side_packet_to_stream_calculator", + "//mediapipe/calculators/image:image_transformation_calculator", + "//mediapipe/calculators/util:detection_unique_id_calculator", + "//mediapipe/calculators/util:to_image_calculator", + "//mediapipe/framework:calculator_py_pb2", + "//mediapipe/framework/formats:detection_py_pb2", + "@com_google_protobuf//:protobuf_python", + ], +) diff --git a/mediapipe/python/solution_base.py b/mediapipe/python/solution_base.py index a482e6f6a..020d16371 100644 --- a/mediapipe/python/solution_base.py +++ b/mediapipe/python/solution_base.py @@ -40,7 +40,6 @@ from mediapipe.calculators.util import landmarks_smoothing_calculator_pb2 from mediapipe.calculators.util import logic_calculator_pb2 from mediapipe.calculators.util import thresholding_calculator_pb2 from mediapipe.framework import calculator_pb2 -from mediapipe.framework.formats import body_rig_pb2 from mediapipe.framework.formats import classification_pb2 from mediapipe.framework.formats import detection_pb2 from mediapipe.framework.formats import landmark_pb2 diff --git a/mediapipe/python/solutions/BUILD b/mediapipe/python/solutions/BUILD new file mode 100644 index 000000000..2ca0ba9ff --- /dev/null +++ b/mediapipe/python/solutions/BUILD @@ -0,0 +1,105 @@ +# Copyright 2020 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. + +licenses(["notice"]) + +package(default_visibility = ["//visibility:public"]) + +py_library( + name = "hands", + srcs = [ + "hands.py", + "hands_connections.py", + ], + data = [ + "//mediapipe/modules/hand_landmark:hand_landmark_full.tflite", + "//mediapipe/modules/hand_landmark:hand_landmark_lite.tflite", + "//mediapipe/modules/hand_landmark:hand_landmark_tracking_cpu_graph", + "//mediapipe/modules/hand_landmark:handedness.txt", + "//mediapipe/modules/palm_detection:palm_detection_full.tflite", + "//mediapipe/modules/palm_detection:palm_detection_lite.tflite", + ], + srcs_version = "PY3", + deps = [ + "//mediapipe/calculators/core:constant_side_packet_calculator_py_pb2", + "//mediapipe/calculators/core:gate_calculator_py_pb2", + "//mediapipe/calculators/core:split_vector_calculator_py_pb2", + "//mediapipe/calculators/tensor:image_to_tensor_calculator_py_pb2", + "//mediapipe/calculators/tensor:inference_calculator_py_pb2", + "//mediapipe/calculators/tensor:tensors_to_classification_calculator_py_pb2", + "//mediapipe/calculators/tensor:tensors_to_detections_calculator_py_pb2", + "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator_py_pb2", + "//mediapipe/calculators/tflite:ssd_anchors_calculator_py_pb2", + "//mediapipe/calculators/util:association_calculator_py_pb2", + "//mediapipe/calculators/util:detections_to_rects_calculator_py_pb2", + "//mediapipe/calculators/util:logic_calculator_py_pb2", + "//mediapipe/calculators/util:non_max_suppression_calculator_py_pb2", + "//mediapipe/calculators/util:rect_transformation_calculator_py_pb2", + "//mediapipe/calculators/util:thresholding_calculator_py_pb2", + "//mediapipe/python:solution_base", + ], +) + +py_library( + name = "drawing_styles", + srcs = ["drawing_styles.py"], + srcs_version = "PY3", + deps = [ + "drawing_utils", + "face_mesh", + "hands", + "pose", + ], +) + +py_library( + name = "drawing_utils", + srcs = ["drawing_utils.py"], + srcs_version = "PY3", + deps = [ + "//mediapipe/framework/formats:detection_py_pb2", + "//mediapipe/framework/formats:landmark_py_pb2", + "//mediapipe/framework/formats:location_data_py_pb2", + ], +) + +py_test( + name = "drawing_utils_test", + srcs = ["drawing_utils_test.py"], + python_version = "PY3", + srcs_version = "PY3", + deps = [ + ":drawing_utils", + "//mediapipe/framework/formats:detection_py_pb2", + "//mediapipe/framework/formats:landmark_py_pb2", + "@com_google_protobuf//:protobuf_python", + ], +) + +py_test( + name = "hands_test", + srcs = ["hands_test.py"], + data = [ + ":testdata/asl_hand.25fps.mp4", + ":testdata/asl_hand.full.npz", + ":testdata/hands.jpg", + ], + python_version = "PY3", + srcs_version = "PY3", + deps = [ + ":drawing_styles", + ":drawing_utils", + ":hands", + ], +) diff --git a/third_party/external_files.bzl b/third_party/external_files.bzl index a89a192ee..b4ec3b36c 100644 --- a/third_party/external_files.bzl +++ b/third_party/external_files.bzl @@ -88,6 +88,12 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/burger_rotated.jpg?generation=1665065843774448"], ) + http_file( + name = "com_google_mediapipe_canned_gesture_classifier_tflite", + sha256 = "2fc7e279966a7a9e15fc869223793e390791fc61fdc0062f9bc7d0eef6be98a2", + urls = ["https://storage.googleapis.com/mediapipe-assets/canned_gesture_classifier.tflite?generation=1668124189331326"], + ) + http_file( name = "com_google_mediapipe_cat_jpg", sha256 = "2533197401eebe9410ea4d063f86c43fbd2666f3e8165a38aca155c0d09c21be", @@ -286,6 +292,12 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/general_meta.json?generation=1665422822603848"], ) + http_file( + name = "com_google_mediapipe_gesture_embedder_tflite", + sha256 = "54abe78de1d1cd5e3cdaa0dab01db18e3ec7e09a76e7c3b5fa278572f7a60977", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder.tflite?generation=1668124192126494"], + ) + http_file( name = "com_google_mediapipe_gesture_recognizer_task", sha256 = "a966b1d4e774e0423c19c8aa71f070e5a72fe7a03c2663dd2f3cb0b0095ee3e1", @@ -721,7 +733,7 @@ def external_files(): http_file( name = "com_google_mediapipe_README_md", sha256 = "a96d08c9c70cd9717207ed72c926e02e5eada751f00bdc5d3a7e82e3492b72cb", - urls = ["https://storage.googleapis.com/mediapipe-assets/README.md?generation=1661875904887163"], + urls = ["https://storage.googleapis.com/mediapipe-assets/README.md?generation=1668124179156767"], ) http_file( @@ -970,6 +982,18 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/yamnet_audio_classifier_with_metadata.tflite?generation=1661875980774466"], ) + http_file( + name = "com_google_mediapipe_gesture_embedder_keras_metadata_pb", + sha256 = "24268b69429be4e307f9ab099ba20d1de7c40e4191a53f6a92dcbbd97a7047d3", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/keras_metadata.pb?generation=1668124196996131"], + ) + + http_file( + name = "com_google_mediapipe_gesture_embedder_saved_model_pb", + sha256 = "f3a2870ba3ef537a4f6a5889ffc5b7061ad98f9fd96ec431a62116892f100659", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/saved_model.pb?generation=1668124199460071"], + ) + http_file( name = "com_google_mediapipe_object_detection_saved_model_model_ckpt_data-00000-of-00001", sha256 = "ad2f733f271dd5000a8c7f926bfea1083e6408b34d4f3b60679e5a6f96251c97", @@ -1005,3 +1029,15 @@ def external_files(): sha256 = "f29606cf218397d5580c496e50fd28cddf66e2f59b819ab9c761b72270a5adf3", urls = ["https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/saved_model.pb?generation=1661875999264354"], ) + + http_file( + name = "com_google_mediapipe_gesture_embedder_variables_variables_data-00000-of-00001", + sha256 = "9fdb750c4bac67afb9c0f61916510930b496cc47e7f89449aee2bec6b6ed0af8", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/variables/variables.data-00000-of-00001?generation=1668124201918980"], + ) + + http_file( + name = "com_google_mediapipe_gesture_embedder_variables_variables_index", + sha256 = "3ccbcee9488fec4627d496abd9837997276b32b839a4d0ae434bd806fe380b86", + urls = ["https://storage.googleapis.com/mediapipe-assets/gesture_embedder/variables/variables.index?generation=1668124204353848"], + ) From 9f681d20e86e9b038cb327956b501ab5e76942eb Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Thu, 10 Nov 2022 17:00:00 -0800 Subject: [PATCH 16/21] Internal change PiperOrigin-RevId: 487676791 --- docs/BUILD | 18 +++++++++++++ docs/build_java_api_docs.py | 51 +++++++++++++++++++++++++++++++++++++ docs/build_py_api_docs.py | 2 +- 3 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 docs/build_java_api_docs.py diff --git a/docs/BUILD b/docs/BUILD index cb8794dab..ad08df66a 100644 --- a/docs/BUILD +++ b/docs/BUILD @@ -12,3 +12,21 @@ py_binary( "//third_party/py/tensorflow_docs/api_generator:public_api", ], ) + +py_binary( + name = "build_java_api_docs", + srcs = ["build_java_api_docs.py"], + data = [ + "//third_party/java/doclava/current:doclava.jar", + "//third_party/java/jsilver:jsilver_jar", + ], + env = { + "DOCLAVA_JAR": "$(location //third_party/java/doclava/current:doclava.jar)", + "JSILVER_JAR": "$(location //third_party/java/jsilver:jsilver_jar)", + }, + deps = [ + "//third_party/py/absl:app", + "//third_party/py/absl/flags", + "//third_party/py/tensorflow_docs/api_generator/gen_java", + ], +) diff --git a/docs/build_java_api_docs.py b/docs/build_java_api_docs.py new file mode 100644 index 000000000..54e96aae1 --- /dev/null +++ b/docs/build_java_api_docs.py @@ -0,0 +1,51 @@ +# 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. +# ============================================================================== +"""Generate Java reference docs for MediaPipe.""" +import pathlib + +from absl import app +from absl import flags + +from tensorflow_docs.api_generator import gen_java + +_OUT_DIR = flags.DEFINE_string('output_dir', '/tmp/mp_java/', + 'Write docs here.') + +_SITE_PATH = flags.DEFINE_string('site_path', '/mediapipe/api_docs/java', + 'Path prefix in the _toc.yaml') + +_ = flags.DEFINE_string('code_url_prefix', None, + '[UNUSED] The url prefix for links to code.') + +_ = flags.DEFINE_bool( + 'search_hints', True, + '[UNUSED] Include metadata search hints in the generated files') + + +def main(_) -> None: + mp_root = pathlib.Path(__file__) + while (mp_root := mp_root.parent).name != 'mediapipe': + # Find the nearest `mediapipe` dir. + pass + + gen_java.gen_java_docs( + package='com.google.mediapipe', + source_path=mp_root / 'java', + output_dir=pathlib.Path(_OUT_DIR.value), + site_path=pathlib.Path(_SITE_PATH.value)) + + +if __name__ == '__main__': + app.run(main) diff --git a/docs/build_py_api_docs.py b/docs/build_py_api_docs.py index 9911d0736..fa1e4314f 100644 --- a/docs/build_py_api_docs.py +++ b/docs/build_py_api_docs.py @@ -1,4 +1,4 @@ -# Copyright 2022 The TensorFlow Authors. All Rights Reserved. +# 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. From 849134cfce463da7a93c795dc9502c1b838c6021 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Thu, 10 Nov 2022 17:46:57 -0800 Subject: [PATCH 17/21] Copy metatdata_schema.fbs into PyPI packages. PiperOrigin-RevId: 487685505 --- setup.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/setup.py b/setup.py index 40c4a7361..b072a850e 100644 --- a/setup.py +++ b/setup.py @@ -307,6 +307,9 @@ class GenerateMetadataSchema(build_ext.build_ext): _copy_to_build_lib_dir( self.build_lib, 'mediapipe/tasks/metadata/' + target + '_generated.py') + schema_file = 'mediapipe/tasks/metadata/metadata_schema.fbs' + shutil.copyfile(schema_file, + os.path.join(self.build_lib + '/', schema_file)) class BazelExtension(setuptools.Extension): From 8c8b668ba3684c3e756bf9fccafbd1bd8e1a7cfe Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Thu, 10 Nov 2022 18:04:42 -0800 Subject: [PATCH 18/21] Fix comment for `mediapipe` license. The `"""` comment indicates a public docstring for the module, and will end up in the generated docs. By using a "private" comment (`#`) we will not document the license as part of the API. The Apache license is noted in the footer of generated docs, and this is sufficient. PiperOrigin-RevId: 487688719 --- mediapipe/__init__.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/mediapipe/__init__.py b/mediapipe/__init__.py index 69d7dfc6f..bf29fed39 100644 --- a/mediapipe/__init__.py +++ b/mediapipe/__init__.py @@ -1,14 +1,13 @@ -"""Copyright 2019 - 2020 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. -""" +# 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. +# 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. From d2284083b35cb9b8c2387e6f7365aa73004cbc96 Mon Sep 17 00:00:00 2001 From: Mark McDonald Date: Thu, 10 Nov 2022 19:36:37 -0800 Subject: [PATCH 19/21] Generate docs from `mediapipe/tasks/java`. The last CL used `mediapipe/java`, which is an old API. I've also added a flag to override the Java source location. This shouldn't be needed normally but makes local testing easier for both internal and external users. PiperOrigin-RevId: 487704937 --- docs/build_java_api_docs.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/build_java_api_docs.py b/docs/build_java_api_docs.py index 54e96aae1..7b44e79f4 100644 --- a/docs/build_java_api_docs.py +++ b/docs/build_java_api_docs.py @@ -20,6 +20,10 @@ from absl import flags from tensorflow_docs.api_generator import gen_java +_JAVA_ROOT = flags.DEFINE_string('java_src', None, + 'Override the Java source path.', + required=False) + _OUT_DIR = flags.DEFINE_string('output_dir', '/tmp/mp_java/', 'Write docs here.') @@ -35,14 +39,17 @@ _ = flags.DEFINE_bool( def main(_) -> None: - mp_root = pathlib.Path(__file__) - while (mp_root := mp_root.parent).name != 'mediapipe': - # Find the nearest `mediapipe` dir. - pass + if not (java_root := _JAVA_ROOT.value): + # Default to using a relative path to find the Java source. + mp_root = pathlib.Path(__file__) + while (mp_root := mp_root.parent).name != 'mediapipe': + # Find the nearest `mediapipe` dir. + pass + java_root = mp_root / 'tasks/java' gen_java.gen_java_docs( package='com.google.mediapipe', - source_path=mp_root / 'java', + source_path=pathlib.Path(java_root), output_dir=pathlib.Path(_OUT_DIR.value), site_path=pathlib.Path(_SITE_PATH.value)) From 3e05871f980a7d78c9dc48c308890cbe82e27599 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 19:51:08 -0800 Subject: [PATCH 20/21] Open source Model Maker text tasks. PiperOrigin-RevId: 487706929 --- mediapipe/model_maker/python/text/core/BUILD | 35 ++ .../model_maker/python/text/core/__init__.py | 13 + .../python/text/core/bert_model_options.py | 33 ++ .../python/text/core/bert_model_spec.py | 58 +++ .../python/text/text_classifier/BUILD | 146 ++++++ .../python/text/text_classifier/__init__.py | 13 + .../python/text/text_classifier/dataset.py | 88 ++++ .../text/text_classifier/dataset_test.py | 75 +++ .../text/text_classifier/model_options.py | 45 ++ .../python/text/text_classifier/model_spec.py | 70 +++ .../text/text_classifier/model_spec_test.py | 118 +++++ .../text/text_classifier/preprocessor.py | 285 ++++++++++++ .../text/text_classifier/preprocessor_test.py | 96 ++++ .../text/text_classifier/testdata/BUILD | 23 + .../average_word_embedding_metadata.json | 63 +++ .../text/text_classifier/text_classifier.py | 437 ++++++++++++++++++ .../text_classifier/text_classifier_demo.py | 108 +++++ .../text_classifier_options.py | 38 ++ .../text_classifier/text_classifier_test.py | 138 ++++++ 19 files changed, 1882 insertions(+) create mode 100644 mediapipe/model_maker/python/text/core/BUILD create mode 100644 mediapipe/model_maker/python/text/core/__init__.py create mode 100644 mediapipe/model_maker/python/text/core/bert_model_options.py create mode 100644 mediapipe/model_maker/python/text/core/bert_model_spec.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/BUILD create mode 100644 mediapipe/model_maker/python/text/text_classifier/__init__.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/dataset.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/dataset_test.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/model_options.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/model_spec.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/model_spec_test.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/preprocessor.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/preprocessor_test.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/testdata/BUILD create mode 100644 mediapipe/model_maker/python/text/text_classifier/testdata/average_word_embedding_metadata.json create mode 100644 mediapipe/model_maker/python/text/text_classifier/text_classifier.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/text_classifier_options.py create mode 100644 mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py diff --git a/mediapipe/model_maker/python/text/core/BUILD b/mediapipe/model_maker/python/text/core/BUILD new file mode 100644 index 000000000..db06c3a75 --- /dev/null +++ b/mediapipe/model_maker/python/text/core/BUILD @@ -0,0 +1,35 @@ +# 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. + +# Placeholder for internal Python strict library and test compatibility macro. + +package( + default_visibility = ["//mediapipe:__subpackages__"], +) + +licenses(["notice"]) + +py_library( + name = "bert_model_options", + srcs = ["bert_model_options.py"], +) + +py_library( + name = "bert_model_spec", + srcs = ["bert_model_spec.py"], + deps = [ + ":bert_model_options", + "//mediapipe/model_maker/python/core:hyperparameters", + ], +) diff --git a/mediapipe/model_maker/python/text/core/__init__.py b/mediapipe/model_maker/python/text/core/__init__.py new file mode 100644 index 000000000..7ca2f9216 --- /dev/null +++ b/mediapipe/model_maker/python/text/core/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/mediapipe/model_maker/python/text/core/bert_model_options.py b/mediapipe/model_maker/python/text/core/bert_model_options.py new file mode 100644 index 000000000..ce5ef6af4 --- /dev/null +++ b/mediapipe/model_maker/python/text/core/bert_model_options.py @@ -0,0 +1,33 @@ +# 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. +"""Configurable model options for a BERT model.""" + +import dataclasses + + +@dataclasses.dataclass +class BertModelOptions: + """Configurable model options for a BERT model. + + See https://arxiv.org/abs/1810.04805 (BERT: Pre-training of Deep Bidirectional + Transformers for Language Understanding) for more details. + + Attributes: + seq_len: Length of the sequence to feed into the model. + do_fine_tuning: If true, then the BERT model is not frozen for training. + dropout_rate: The rate for dropout. + """ + seq_len: int = 128 + do_fine_tuning: bool = True + dropout_rate: float = 0.1 diff --git a/mediapipe/model_maker/python/text/core/bert_model_spec.py b/mediapipe/model_maker/python/text/core/bert_model_spec.py new file mode 100644 index 000000000..6c0085617 --- /dev/null +++ b/mediapipe/model_maker/python/text/core/bert_model_spec.py @@ -0,0 +1,58 @@ +# 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. +"""Specification for a BERT model.""" + +import dataclasses +from typing import Dict + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.text.core import bert_model_options + +_DEFAULT_TFLITE_INPUT_NAME = { + 'ids': 'serving_default_input_word_ids:0', + 'mask': 'serving_default_input_mask:0', + 'segment_ids': 'serving_default_input_type_ids:0' +} + + +@dataclasses.dataclass +class BertModelSpec: + """Specification for a BERT model. + + See https://arxiv.org/abs/1810.04805 (BERT: Pre-training of Deep Bidirectional + Transformers for Language Understanding) for more details. + + Attributes: + hparams: Hyperparameters used for training. + model_options: Configurable options for a BERT model. + do_lower_case: boolean, whether to lower case the input text. Should be + True / False for uncased / cased models respectively, where the models + are specified by the `uri`. + tflite_input_name: Dict, input names for the TFLite model. + uri: URI for the BERT module. + name: The name of the object. + """ + + hparams: hp.BaseHParams = hp.BaseHParams( + epochs=3, + batch_size=32, + learning_rate=3e-5, + distribution_strategy='mirrored') + model_options: bert_model_options.BertModelOptions = ( + bert_model_options.BertModelOptions()) + do_lower_case: bool = True + tflite_input_name: Dict[str, str] = dataclasses.field( + default_factory=lambda: _DEFAULT_TFLITE_INPUT_NAME) + uri: str = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1' + name: str = 'Bert' diff --git a/mediapipe/model_maker/python/text/text_classifier/BUILD b/mediapipe/model_maker/python/text/text_classifier/BUILD new file mode 100644 index 000000000..357263678 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/BUILD @@ -0,0 +1,146 @@ +# 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. + +# Placeholder for internal Python strict library and test compatibility macro. +# Placeholder for internal Python strict test compatibility macro. + +package( + default_visibility = ["//mediapipe:__subpackages__"], +) + +licenses(["notice"]) + +py_library( + name = "model_options", + srcs = ["model_options.py"], + deps = ["//mediapipe/model_maker/python/text/core:bert_model_options"], +) + +py_library( + name = "model_spec", + srcs = ["model_spec.py"], + deps = [ + ":model_options", + "//mediapipe/model_maker/python/core:hyperparameters", + "//mediapipe/model_maker/python/text/core:bert_model_spec", + ], +) + +py_test( + name = "model_spec_test", + srcs = ["model_spec_test.py"], + deps = [ + ":model_options", + ":model_spec", + "//mediapipe/model_maker/python/core:hyperparameters", + ], +) + +py_library( + name = "dataset", + srcs = ["dataset.py"], + deps = ["//mediapipe/model_maker/python/core/data:classification_dataset"], +) + +py_test( + name = "dataset_test", + srcs = ["dataset_test.py"], + deps = [":dataset"], +) + +py_library( + name = "preprocessor", + srcs = ["preprocessor.py"], + deps = [":dataset"], +) + +py_test( + name = "preprocessor_test", + srcs = ["preprocessor_test.py"], + tags = ["requires-net:external"], + deps = [ + ":dataset", + ":model_spec", + ":preprocessor", + ], +) + +py_library( + name = "text_classifier_options", + srcs = ["text_classifier_options.py"], + deps = [ + ":model_options", + ":model_spec", + "//mediapipe/model_maker/python/core:hyperparameters", + ], +) + +py_library( + name = "text_classifier", + srcs = ["text_classifier.py"], + deps = [ + ":dataset", + ":model_options", + ":model_spec", + ":preprocessor", + ":text_classifier_options", + "//mediapipe/model_maker/python/core:hyperparameters", + "//mediapipe/model_maker/python/core/data:dataset", + "//mediapipe/model_maker/python/core/tasks:classifier", + "//mediapipe/model_maker/python/core/utils:model_util", + "//mediapipe/model_maker/python/core/utils:quantization", + "//mediapipe/tasks/python/metadata/metadata_writers:metadata_writer", + "//mediapipe/tasks/python/metadata/metadata_writers:text_classifier", + ], +) + +py_test( + name = "text_classifier_test", + size = "large", + srcs = ["text_classifier_test.py"], + data = [ + "//mediapipe/model_maker/python/text/text_classifier/testdata", + ], + tags = ["requires-net:external"], + deps = [ + ":dataset", + ":model_options", + ":model_spec", + ":text_classifier", + ":text_classifier_options", + "//mediapipe/model_maker/python/core:hyperparameters", + "//mediapipe/tasks/python/test:test_utils", + ], +) + +py_library( + name = "text_classifier_demo_lib", + srcs = ["text_classifier_demo.py"], + deps = [ + ":dataset", + ":model_spec", + ":text_classifier", + ":text_classifier_options", + "//mediapipe/model_maker/python/core:hyperparameters", + "//mediapipe/model_maker/python/core/utils:quantization", + ], +) + +py_binary( + name = "text_classifier_demo", + srcs = ["text_classifier_demo.py"], + deps = [ + ":text_classifier_demo_lib", + ], +) diff --git a/mediapipe/model_maker/python/text/text_classifier/__init__.py b/mediapipe/model_maker/python/text/text_classifier/__init__.py new file mode 100644 index 000000000..7ca2f9216 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/mediapipe/model_maker/python/text/text_classifier/dataset.py b/mediapipe/model_maker/python/text/text_classifier/dataset.py new file mode 100644 index 000000000..3679b67ae --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/dataset.py @@ -0,0 +1,88 @@ +# 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. +"""Text classifier dataset library.""" + +import csv +import dataclasses +import random + +from typing import Optional, Sequence +import tensorflow as tf + +from mediapipe.model_maker.python.core.data import classification_dataset + + +@dataclasses.dataclass +class CSVParameters: + """Parameters used when reading a CSV file. + + Attributes: + text_column: Column name for the input text. + label_column: Column name for the labels. + fieldnames: Sequence of keys for the CSV columns. If None, the first row of + the CSV file is used as the keys. + delimiter: Character that separates fields. + quotechar: Character used to quote fields that contain special characters + like the `delimiter`. + """ + text_column: str + label_column: str + fieldnames: Optional[Sequence[str]] = None + delimiter: str = "," + quotechar: str = '"' + + +class Dataset(classification_dataset.ClassificationDataset): + """Dataset library for text classifier.""" + + @classmethod + def from_csv(cls, + filename: str, + csv_params: CSVParameters, + shuffle: bool = True) -> "Dataset": + """Loads text with labels from a CSV file. + + Args: + filename: Name of the CSV file. + csv_params: Parameters used for reading the CSV file. + shuffle: If True, randomly shuffle the data. + + Returns: + Dataset containing (text, label) pairs and other related info. + """ + with tf.io.gfile.GFile(filename, "r") as f: + reader = csv.DictReader( + f, + fieldnames=csv_params.fieldnames, + delimiter=csv_params.delimiter, + quotechar=csv_params.quotechar) + + lines = list(reader) + if shuffle: + random.shuffle(lines) + + label_names = sorted(set([line[csv_params.label_column] for line in lines])) + index_by_label = {label: index for index, label in enumerate(label_names)} + + texts = [line[csv_params.text_column] for line in lines] + text_ds = tf.data.Dataset.from_tensor_slices(tf.cast(texts, tf.string)) + label_indices = [ + index_by_label[line[csv_params.label_column]] for line in lines + ] + label_index_ds = tf.data.Dataset.from_tensor_slices( + tf.cast(label_indices, tf.int64)) + text_label_ds = tf.data.Dataset.zip((text_ds, label_index_ds)) + + return Dataset( + dataset=text_label_ds, size=len(texts), label_names=label_names) diff --git a/mediapipe/model_maker/python/text/text_classifier/dataset_test.py b/mediapipe/model_maker/python/text/text_classifier/dataset_test.py new file mode 100644 index 000000000..ec9e8fa2d --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/dataset_test.py @@ -0,0 +1,75 @@ +# 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. + +import csv +import os + +import tensorflow as tf + +from mediapipe.model_maker.python.text.text_classifier import dataset + + +class DatasetTest(tf.test.TestCase): + + def _get_csv_file(self): + labels_and_text = (('neutral', 'indifferent'), ('pos', 'extremely great'), + ('neg', 'totally awful'), ('pos', 'super good'), + ('neg', 'really bad')) + csv_file = os.path.join(self.get_temp_dir(), 'data.csv') + if os.path.exists(csv_file): + return csv_file + fieldnames = ['text', 'label'] + with open(csv_file, 'w') as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + for label, text in labels_and_text: + writer.writerow({'text': text, 'label': label}) + return csv_file + + def test_from_csv(self): + csv_file = self._get_csv_file() + csv_params = dataset.CSVParameters(text_column='text', label_column='label') + data = dataset.Dataset.from_csv(filename=csv_file, csv_params=csv_params) + self.assertLen(data, 5) + self.assertEqual(data.num_classes, 3) + self.assertEqual(data.label_names, ['neg', 'neutral', 'pos']) + data_values = set([(text.numpy()[0], label.numpy()[0]) + for text, label in data.gen_tf_dataset()]) + expected_data_values = set([(b'indifferent', 1), (b'extremely great', 2), + (b'totally awful', 0), (b'super good', 2), + (b'really bad', 0)]) + self.assertEqual(data_values, expected_data_values) + + def test_split(self): + ds = tf.data.Dataset.from_tensor_slices(['good', 'bad', 'neutral', 'odd']) + data = dataset.Dataset(ds, 4, ['pos', 'neg']) + train_data, test_data = data.split(0.5) + expected_train_data = [b'good', b'bad'] + expected_test_data = [b'neutral', b'odd'] + + self.assertLen(train_data, 2) + train_data_values = [elem.numpy() for elem in train_data._dataset] + self.assertEqual(train_data_values, expected_train_data) + self.assertEqual(train_data.num_classes, 2) + self.assertEqual(train_data.label_names, ['pos', 'neg']) + + self.assertLen(test_data, 2) + test_data_values = [elem.numpy() for elem in test_data._dataset] + self.assertEqual(test_data_values, expected_test_data) + self.assertEqual(test_data.num_classes, 2) + self.assertEqual(test_data.label_names, ['pos', 'neg']) + + +if __name__ == '__main__': + tf.test.main() diff --git a/mediapipe/model_maker/python/text/text_classifier/model_options.py b/mediapipe/model_maker/python/text/text_classifier/model_options.py new file mode 100644 index 000000000..b48e38da1 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/model_options.py @@ -0,0 +1,45 @@ +# 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. +"""Configurable model options for text classifier models.""" + +import dataclasses +from typing import Union + +from mediapipe.model_maker.python.text.core import bert_model_options + +# BERT text classifier options inherited from BertModelOptions. +BertClassifierOptions = bert_model_options.BertModelOptions + + +@dataclasses.dataclass +class AverageWordEmbeddingClassifierOptions: + """Configurable model options for an Average Word Embedding classifier. + + Attributes: + seq_len: Length of the sequence to feed into the model. + wordvec_dim: Dimension of the word embedding. + do_lower_case: Whether to convert all uppercase characters to lowercase + during preprocessing. + vocab_size: Number of words to generate the vocabulary from data. + dropout_rate: The rate for dropout. + """ + seq_len: int = 256 + wordvec_dim: int = 16 + do_lower_case: bool = True + vocab_size: int = 10000 + dropout_rate: float = 0.2 + + +TextClassifierModelOptions = Union[AverageWordEmbeddingClassifierOptions, + BertClassifierOptions] diff --git a/mediapipe/model_maker/python/text/text_classifier/model_spec.py b/mediapipe/model_maker/python/text/text_classifier/model_spec.py new file mode 100644 index 000000000..c2694786c --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/model_spec.py @@ -0,0 +1,70 @@ +# 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. +"""Specifications for text classifier models.""" + +import dataclasses +import enum +import functools + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.text.core import bert_model_spec +from mediapipe.model_maker.python.text.text_classifier import model_options as mo + +# BERT-based text classifier spec inherited from BertModelSpec +BertClassifierSpec = bert_model_spec.BertModelSpec + + +@dataclasses.dataclass +class AverageWordEmbeddingClassifierSpec: + """Specification for an average word embedding classifier model. + + Attributes: + hparams: Configurable hyperparameters for training. + model_options: Configurable options for the average word embedding model. + name: The name of the object. + """ + + # `learning_rate` is unused for the average word embedding model + hparams: hp.BaseHParams = hp.BaseHParams( + epochs=10, batch_size=32, learning_rate=0) + model_options: mo.AverageWordEmbeddingClassifierOptions = ( + mo.AverageWordEmbeddingClassifierOptions()) + name: str = 'AverageWordEmbedding' + + +average_word_embedding_classifier_spec = functools.partial( + AverageWordEmbeddingClassifierSpec) + +mobilebert_classifier_spec = functools.partial( + BertClassifierSpec, + hparams=hp.BaseHParams( + epochs=3, + batch_size=48, + learning_rate=3e-5, + distribution_strategy='off'), + name='MobileBert', + uri='https://tfhub.dev/tensorflow/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1', + tflite_input_name={ + 'ids': 'serving_default_input_1:0', + 'mask': 'serving_default_input_3:0', + 'segment_ids': 'serving_default_input_2:0' + }, +) + + +@enum.unique +class SupportedModels(enum.Enum): + """Predefined text classifier model specs supported by Model Maker.""" + AVERAGE_WORD_EMBEDDING_CLASSIFIER = average_word_embedding_classifier_spec + MOBILEBERT_CLASSIFIER = mobilebert_classifier_spec diff --git a/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py b/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py new file mode 100644 index 000000000..118b84fdc --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/model_spec_test.py @@ -0,0 +1,118 @@ +# 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. +"""Tests for model_spec.""" + +import os + +import tensorflow as tf + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.text.text_classifier import model_options as classifier_model_options +from mediapipe.model_maker.python.text.text_classifier import model_spec as ms + + +class ModelSpecTest(tf.test.TestCase): + + def test_predefined_bert_spec(self): + model_spec_obj = ms.SupportedModels.MOBILEBERT_CLASSIFIER.value() + self.assertIsInstance(model_spec_obj, ms.BertClassifierSpec) + self.assertEqual(model_spec_obj.name, 'MobileBert') + self.assertEqual( + model_spec_obj.uri, 'https://tfhub.dev/tensorflow/' + 'mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1') + self.assertTrue(model_spec_obj.do_lower_case) + self.assertEqual( + model_spec_obj.tflite_input_name, { + 'ids': 'serving_default_input_1:0', + 'mask': 'serving_default_input_3:0', + 'segment_ids': 'serving_default_input_2:0' + }) + self.assertEqual( + model_spec_obj.model_options, + classifier_model_options.BertClassifierOptions( + seq_len=128, do_fine_tuning=True, dropout_rate=0.1)) + self.assertEqual( + model_spec_obj.hparams, + hp.BaseHParams( + epochs=3, + batch_size=48, + learning_rate=3e-5, + distribution_strategy='off')) + + def test_predefined_average_word_embedding_spec(self): + model_spec_obj = ( + ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER.value()) + self.assertIsInstance(model_spec_obj, ms.AverageWordEmbeddingClassifierSpec) + self.assertEqual(model_spec_obj.name, 'AverageWordEmbedding') + self.assertEqual( + model_spec_obj.model_options, + classifier_model_options.AverageWordEmbeddingClassifierOptions( + seq_len=256, + wordvec_dim=16, + do_lower_case=True, + vocab_size=10000, + dropout_rate=0.2)) + self.assertEqual( + model_spec_obj.hparams, + hp.BaseHParams( + epochs=10, + batch_size=32, + learning_rate=0, + steps_per_epoch=None, + shuffle=False, + distribution_strategy='off', + num_gpus=-1, + tpu='')) + + def test_custom_bert_spec(self): + custom_bert_classifier_options = ( + classifier_model_options.BertClassifierOptions( + seq_len=512, do_fine_tuning=False, dropout_rate=0.3)) + model_spec_obj = ( + ms.SupportedModels.MOBILEBERT_CLASSIFIER.value( + model_options=custom_bert_classifier_options)) + self.assertEqual(model_spec_obj.model_options, + custom_bert_classifier_options) + + def test_custom_average_word_embedding_spec(self): + custom_hparams = hp.BaseHParams( + learning_rate=0.4, + batch_size=64, + epochs=10, + steps_per_epoch=10, + shuffle=True, + export_dir='foo/bar', + distribution_strategy='mirrored', + num_gpus=3, + tpu='tpu/address') + custom_average_word_embedding_model_options = ( + classifier_model_options.AverageWordEmbeddingClassifierOptions( + seq_len=512, + wordvec_dim=32, + do_lower_case=False, + vocab_size=5000, + dropout_rate=0.5)) + model_spec_obj = ( + ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER.value( + model_options=custom_average_word_embedding_model_options, + hparams=custom_hparams)) + self.assertEqual(model_spec_obj.model_options, + custom_average_word_embedding_model_options) + self.assertEqual(model_spec_obj.hparams, custom_hparams) + + +if __name__ == '__main__': + # Load compressed models from tensorflow_hub + os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED' + tf.test.main() diff --git a/mediapipe/model_maker/python/text/text_classifier/preprocessor.py b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py new file mode 100644 index 000000000..0a48f459c --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py @@ -0,0 +1,285 @@ +# 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. +# ============================================================================== +"""Preprocessors for text classification.""" + +import collections +import os +import re +import tempfile +from typing import Mapping, Sequence, Tuple, Union + +import tensorflow as tf +import tensorflow_hub + +from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds +from official.nlp.data import classifier_data_lib +from official.nlp.tools import tokenization + + +def _validate_text_and_label(text: tf.Tensor, label: tf.Tensor) -> None: + """Validates the shape and type of `text` and `label`. + + Args: + text: Stores text data. Should have shape [1] and dtype tf.string. + label: Stores the label for the corresponding `text`. Should have shape [1] + and dtype tf.int64. + + Raises: + ValueError: If either tensor has the wrong shape or type. + """ + if text.shape != [1]: + raise ValueError(f"`text` should have shape [1], got {text.shape}") + if text.dtype != tf.string: + raise ValueError(f"Expected dtype string for `text`, got {text.dtype}") + if label.shape != [1]: + raise ValueError(f"`label` should have shape [1], got {text.shape}") + if label.dtype != tf.int64: + raise ValueError(f"Expected dtype int64 for `label`, got {label.dtype}") + + +def _decode_record( + record: tf.Tensor, name_to_features: Mapping[str, tf.io.FixedLenFeature] +) -> Tuple[Mapping[str, tf.Tensor], tf.Tensor]: + """Decodes a record into input for a BERT model. + + Args: + record: Stores serialized example. + name_to_features: Maps record keys to feature types. + + Returns: + BERT model input features and label for the record. + """ + example = tf.io.parse_single_example(record, name_to_features) + + # tf.Example only supports tf.int64, but the TPU only supports tf.int32. + for name in list(example.keys()): + example[name] = tf.cast(example[name], tf.int32) + + bert_features = { + "input_word_ids": example["input_ids"], + "input_mask": example["input_mask"], + "input_type_ids": example["segment_ids"] + } + return bert_features, example["label_ids"] + + +def _single_file_dataset( + input_file: str, name_to_features: Mapping[str, tf.io.FixedLenFeature] +) -> tf.data.TFRecordDataset: + """Creates a single-file dataset to be passed for BERT custom training. + + Args: + input_file: Filepath for the dataset. + name_to_features: Maps record keys to feature types. + + Returns: + Dataset containing BERT model input features and labels. + """ + d = tf.data.TFRecordDataset(input_file) + d = d.map( + lambda record: _decode_record(record, name_to_features), + num_parallel_calls=tf.data.AUTOTUNE) + return d + + +class AverageWordEmbeddingClassifierPreprocessor: + """Preprocessor for an Average Word Embedding model. + + Takes (text, label) data and applies regex tokenization and padding to the + text to generate (token IDs, label) data. + + Attributes: + seq_len: Length of the input sequence to the model. + do_lower_case: Whether text inputs should be converted to lower-case. + vocab: Vocabulary of tokens used by the model. + """ + + PAD: str = "" # Index: 0 + START: str = "" # Index: 1 + UNKNOWN: str = "" # Index: 2 + + def __init__(self, seq_len: int, do_lower_case: bool, texts: Sequence[str], + vocab_size: int): + self._seq_len = seq_len + self._do_lower_case = do_lower_case + self._vocab = self._gen_vocab(texts, vocab_size) + + def _gen_vocab(self, texts: Sequence[str], + vocab_size: int) -> Mapping[str, int]: + """Generates vocabulary list in `texts` with size `vocab_size`. + + Args: + texts: All texts (across training and validation data) that will be + preprocessed by the model. + vocab_size: Size of the vocab. + + Returns: + The vocab mapping tokens to IDs. + """ + vocab_counter = collections.Counter() + + for text in texts: + tokens = self._regex_tokenize(text) + for token in tokens: + vocab_counter[token] += 1 + + vocab_freq = vocab_counter.most_common(vocab_size) + vocab_list = [self.PAD, self.START, self.UNKNOWN + ] + [word for word, _ in vocab_freq] + return collections.OrderedDict(((v, i) for i, v in enumerate(vocab_list))) + + def get_vocab(self) -> Mapping[str, int]: + """Returns the vocab of the AverageWordEmbeddingClassifierPreprocessor.""" + return self._vocab + + # TODO: Align with MediaPipe's RegexTokenizer. + def _regex_tokenize(self, text: str) -> Sequence[str]: + """Splits `text` by words but does not split on single quotes. + + Args: + text: Text to be tokenized. + + Returns: + List of tokens. + """ + text = tf.compat.as_text(text) + if self._do_lower_case: + text = text.lower() + tokens = re.compile(r"[^\w\']+").split(text.strip()) + # Filters out any empty strings in `tokens`. + return list(filter(None, tokens)) + + def _tokenize_and_pad(self, text: str) -> Sequence[int]: + """Tokenizes `text` and pads the tokens to `seq_len`. + + Args: + text: Text to be tokenized and padded. + + Returns: + List of token IDs padded to have length `seq_len`. + """ + tokens = self._regex_tokenize(text) + + # Gets ids for START, PAD and UNKNOWN tokens. + start_id = self._vocab[self.START] + pad_id = self._vocab[self.PAD] + unknown_id = self._vocab[self.UNKNOWN] + + token_ids = [self._vocab.get(token, unknown_id) for token in tokens] + token_ids = [start_id] + token_ids + + if len(token_ids) < self._seq_len: + pad_length = self._seq_len - len(token_ids) + token_ids = token_ids + pad_length * [pad_id] + else: + token_ids = token_ids[:self._seq_len] + return token_ids + + def preprocess( + self, dataset: text_classifier_ds.Dataset) -> text_classifier_ds.Dataset: + """Preprocesses data into input for an Average Word Embedding model. + + Args: + dataset: Stores (text, label) data. + + Returns: + Dataset containing (token IDs, label) data. + """ + token_ids_list = [] + labels_list = [] + for text, label in dataset.gen_tf_dataset(): + _validate_text_and_label(text, label) + token_ids = self._tokenize_and_pad(text.numpy()[0].decode("utf-8")) + token_ids_list.append(token_ids) + labels_list.append(label.numpy()[0]) + + token_ids_ds = tf.data.Dataset.from_tensor_slices(token_ids_list) + labels_ds = tf.data.Dataset.from_tensor_slices(labels_list) + preprocessed_ds = tf.data.Dataset.zip((token_ids_ds, labels_ds)) + return text_classifier_ds.Dataset( + dataset=preprocessed_ds, + size=dataset.size, + label_names=dataset.label_names) + + +class BertClassifierPreprocessor: + """Preprocessor for a BERT-based classifier. + + Attributes: + seq_len: Length of the input sequence to the model. + vocab_file: File containing the BERT vocab. + tokenizer: BERT tokenizer. + """ + + def __init__(self, seq_len: int, do_lower_case: bool, uri: str): + self._seq_len = seq_len + # Vocab filepath is tied to the BERT module's URI. + self._vocab_file = os.path.join( + tensorflow_hub.resolve(uri), "assets", "vocab.txt") + self._tokenizer = tokenization.FullTokenizer(self._vocab_file, + do_lower_case) + + def _get_name_to_features(self): + """Gets the dictionary mapping record keys to feature types.""" + return { + "input_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64), + "input_mask": tf.io.FixedLenFeature([self._seq_len], tf.int64), + "segment_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64), + "label_ids": tf.io.FixedLenFeature([], tf.int64), + } + + def get_vocab_file(self) -> str: + """Returns the vocab file of the BertClassifierPreprocessor.""" + return self._vocab_file + + def preprocess( + self, dataset: text_classifier_ds.Dataset) -> text_classifier_ds.Dataset: + """Preprocesses data into input for a BERT-based classifier. + + Args: + dataset: Stores (text, label) data. + + Returns: + Dataset containing (bert_features, label) data. + """ + examples = [] + for index, (text, label) in enumerate(dataset.gen_tf_dataset()): + _validate_text_and_label(text, label) + examples.append( + classifier_data_lib.InputExample( + guid=str(index), + text_a=text.numpy()[0].decode("utf-8"), + text_b=None, + # InputExample expects the label name rather than the int ID + label=dataset.label_names[label.numpy()[0]])) + + tfrecord_file = os.path.join(tempfile.mkdtemp(), "bert_features.tfrecord") + classifier_data_lib.file_based_convert_examples_to_features( + examples=examples, + label_list=dataset.label_names, + max_seq_length=self._seq_len, + tokenizer=self._tokenizer, + output_file=tfrecord_file) + preprocessed_ds = _single_file_dataset(tfrecord_file, + self._get_name_to_features()) + return text_classifier_ds.Dataset( + dataset=preprocessed_ds, + size=dataset.size, + label_names=dataset.label_names) + + +TextClassifierPreprocessor = ( + Union[BertClassifierPreprocessor, + AverageWordEmbeddingClassifierPreprocessor]) diff --git a/mediapipe/model_maker/python/text/text_classifier/preprocessor_test.py b/mediapipe/model_maker/python/text/text_classifier/preprocessor_test.py new file mode 100644 index 000000000..b9558b2b5 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/preprocessor_test.py @@ -0,0 +1,96 @@ +# 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. + +import csv +import os + +import numpy as np +import numpy.testing as npt +import tensorflow as tf + +from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds +from mediapipe.model_maker.python.text.text_classifier import model_spec +from mediapipe.model_maker.python.text.text_classifier import preprocessor + + +class PreprocessorTest(tf.test.TestCase): + CSV_PARAMS_ = text_classifier_ds.CSVParameters( + text_column='text', label_column='label') + + def _get_csv_file(self): + labels_and_text = (('pos', 'super super super super good'), + (('neg', 'really bad'))) + csv_file = os.path.join(self.get_temp_dir(), 'data.csv') + if os.path.exists(csv_file): + return csv_file + fieldnames = ['text', 'label'] + with open(csv_file, 'w') as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + for label, text in labels_and_text: + writer.writerow({'text': text, 'label': label}) + return csv_file + + def test_average_word_embedding_preprocessor(self): + csv_file = self._get_csv_file() + dataset = text_classifier_ds.Dataset.from_csv( + filename=csv_file, csv_params=self.CSV_PARAMS_) + average_word_embedding_preprocessor = ( + preprocessor.AverageWordEmbeddingClassifierPreprocessor( + seq_len=5, + do_lower_case=True, + texts=['super super super super good', 'really bad'], + vocab_size=7)) + preprocessed_dataset = ( + average_word_embedding_preprocessor.preprocess(dataset)) + labels = [] + features_list = [] + for features, label in preprocessed_dataset.gen_tf_dataset(): + self.assertEqual(label.shape, [1]) + labels.append(label.numpy()[0]) + self.assertEqual(features.shape, [1, 5]) + features_list.append(features.numpy()[0]) + self.assertEqual(labels, [1, 0]) + npt.assert_array_equal( + np.stack(features_list), np.array([[1, 3, 3, 3, 3], [1, 5, 6, 0, 0]])) + + def test_bert_preprocessor(self): + csv_file = self._get_csv_file() + dataset = text_classifier_ds.Dataset.from_csv( + filename=csv_file, csv_params=self.CSV_PARAMS_) + bert_spec = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value() + bert_preprocessor = preprocessor.BertClassifierPreprocessor( + seq_len=5, do_lower_case=bert_spec.do_lower_case, uri=bert_spec.uri) + preprocessed_dataset = bert_preprocessor.preprocess(dataset) + labels = [] + input_masks = [] + for features, label in preprocessed_dataset.gen_tf_dataset(): + self.assertEqual(label.shape, [1]) + labels.append(label.numpy()[0]) + self.assertSameElements( + features.keys(), ['input_word_ids', 'input_mask', 'input_type_ids']) + for feature in features.values(): + self.assertEqual(feature.shape, [1, 5]) + input_masks.append(features['input_mask'].numpy()[0]) + npt.assert_array_equal(features['input_type_ids'].numpy()[0], + [0, 0, 0, 0, 0]) + npt.assert_array_equal( + np.stack(input_masks), np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]])) + self.assertEqual(labels, [1, 0]) + + +if __name__ == '__main__': + # Load compressed models from tensorflow_hub + os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED' + tf.test.main() diff --git a/mediapipe/model_maker/python/text/text_classifier/testdata/BUILD b/mediapipe/model_maker/python/text/text_classifier/testdata/BUILD new file mode 100644 index 000000000..663c72082 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/testdata/BUILD @@ -0,0 +1,23 @@ +# 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. + +package( + default_visibility = ["//mediapipe/model_maker/python/text/text_classifier:__subpackages__"], + licenses = ["notice"], # Apache 2.0 +) + +filegroup( + name = "testdata", + srcs = ["average_word_embedding_metadata.json"], +) diff --git a/mediapipe/model_maker/python/text/text_classifier/testdata/average_word_embedding_metadata.json b/mediapipe/model_maker/python/text/text_classifier/testdata/average_word_embedding_metadata.json new file mode 100644 index 000000000..bf5cb3640 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/testdata/average_word_embedding_metadata.json @@ -0,0 +1,63 @@ +{ + "name": "TextClassifier", + "description": "Classify the input text into a set of known categories.", + "subgraph_metadata": [ + { + "input_tensor_metadata": [ + { + "name": "input_text", + "description": "Embedding vectors representing the input text to be processed.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "process_units": [ + { + "options_type": "RegexTokenizerOptions", + "options": { + "delim_regex_pattern": "[^\\w\\']+", + "vocab_file": [ + { + "name": "vocab.txt", + "description": "Vocabulary file to convert natural language words to embedding vectors.", + "type": "VOCABULARY" + } + ] + } + } + ], + "stats": { + } + } + ], + "output_tensor_metadata": [ + { + "name": "score", + "description": "Score of the labels respectively.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + "max": [ + 1.0 + ], + "min": [ + 0.0 + ] + }, + "associated_files": [ + { + "name": "labels.txt", + "description": "Labels for categories that the model can recognize.", + "type": "TENSOR_AXIS_LABELS" + } + ] + } + ] + } + ], + "min_parser_version": "1.2.1" +} diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py new file mode 100644 index 000000000..919277b8a --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py @@ -0,0 +1,437 @@ +# 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. +"""API for text classification.""" + +import abc +import os +import tempfile +from typing import Any, Optional, Sequence, Tuple + +import tensorflow as tf +import tensorflow_hub as hub + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.core.data import dataset as ds +from mediapipe.model_maker.python.core.tasks import classifier +from mediapipe.model_maker.python.core.utils import model_util +from mediapipe.model_maker.python.core.utils import quantization +from mediapipe.model_maker.python.text.text_classifier import dataset as text_ds +from mediapipe.model_maker.python.text.text_classifier import model_options as mo +from mediapipe.model_maker.python.text.text_classifier import model_spec as ms +from mediapipe.model_maker.python.text.text_classifier import preprocessor +from mediapipe.model_maker.python.text.text_classifier import text_classifier_options +from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer +from mediapipe.tasks.python.metadata.metadata_writers import text_classifier as text_classifier_writer +from official.nlp import optimization + + +def _validate(options: text_classifier_options.TextClassifierOptions): + """Validates that `model_options` and `supported_model` are compatible. + + Args: + options: Options for creating and training a text classifier. + + Raises: + ValueError if there is a mismatch between `model_options` and + `supported_model`. + """ + + if options.model_options is None: + return + + if (isinstance(options.model_options, + mo.AverageWordEmbeddingClassifierOptions) and + (options.supported_model != + ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER)): + raise ValueError("Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER," + f" got {options.supported_model}") + if (isinstance(options.model_options, mo.BertClassifierOptions) and + (options.supported_model != ms.SupportedModels.MOBILEBERT_CLASSIFIER)): + raise ValueError( + f"Expected MOBILEBERT_CLASSIFIER, got {options.supported_model}") + + +class TextClassifier(classifier.Classifier): + """API for creating and training a text classification model.""" + + def __init__(self, model_spec: Any, hparams: hp.BaseHParams, + label_names: Sequence[str]): + super().__init__( + model_spec=model_spec, label_names=label_names, shuffle=hparams.shuffle) + self._model_spec = model_spec + self._hparams = hparams + self._callbacks = model_util.get_default_callbacks(self._hparams.export_dir) + self._text_preprocessor: preprocessor.TextClassifierPreprocessor = None + + @classmethod + def create( + cls, train_data: text_ds.Dataset, validation_data: text_ds.Dataset, + options: text_classifier_options.TextClassifierOptions + ) -> "TextClassifier": + """Factory function that creates and trains a text classifier. + + Note that `train_data` and `validation_data` are expected to share the same + `label_names` since they should be split from the same dataset. + + Args: + train_data: Training data. + validation_data: Validation data. + options: Options for creating and training the text classifier. + + Returns: + A text classifier. + + Raises: + ValueError if `train_data` and `validation_data` do not have the + same label_names or `options` contains an unknown `supported_model` + """ + if train_data.label_names != validation_data.label_names: + raise ValueError( + f"Training data label names {train_data.label_names} not equal to " + f"validation data label names {validation_data.label_names}") + + _validate(options) + if options.model_options is None: + options.model_options = options.supported_model.value().model_options + + if options.hparams is None: + options.hparams = options.supported_model.value().hparams + + if options.supported_model == ms.SupportedModels.MOBILEBERT_CLASSIFIER: + text_classifier = ( + _BertClassifier.create_bert_classifier(train_data, validation_data, + options, + train_data.label_names)) + elif (options.supported_model == + ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER): + text_classifier = ( + _AverageWordEmbeddingClassifier + .create_average_word_embedding_classifier(train_data, validation_data, + options, + train_data.label_names)) + else: + raise ValueError(f"Unknown model {options.supported_model}") + + return text_classifier + + def evaluate(self, data: ds.Dataset, batch_size: int = 32) -> Any: + """Overrides Classifier.evaluate(). + + Args: + data: Evaluation dataset. Must be a TextClassifier Dataset. + batch_size: Number of samples per evaluation step. + + Returns: + The loss value and accuracy. + + Raises: + ValueError if `data` is not a TextClassifier Dataset. + """ + # This override is needed because TextClassifier preprocesses its data + # outside of the `gen_tf_dataset()` method. The preprocess call also + # requires a TextClassifier Dataset instead of a core Dataset. + if not isinstance(data, text_ds.Dataset): + raise ValueError("Need a TextClassifier Dataset.") + + processed_data = self._text_preprocessor.preprocess(data) + dataset = processed_data.gen_tf_dataset(batch_size, is_training=False) + return self._model.evaluate(dataset) + + def export_model( + self, + model_name: str = "model.tflite", + quantization_config: Optional[quantization.QuantizationConfig] = None): + """Converts and saves the model to a TFLite file with metadata included. + + Note that only the TFLite file is needed for deployment. This function also + saves a metadata.json file to the same directory as the TFLite file which + can be used to interpret the metadata content in the TFLite file. + + Args: + model_name: File name to save TFLite model with metadata. The full export + path is {self._hparams.export_dir}/{model_name}. + quantization_config: The configuration for model quantization. + """ + if not tf.io.gfile.exists(self._hparams.export_dir): + tf.io.gfile.makedirs(self._hparams.export_dir) + tflite_file = os.path.join(self._hparams.export_dir, model_name) + metadata_file = os.path.join(self._hparams.export_dir, "metadata.json") + + tflite_model = model_util.convert_to_tflite( + model=self._model, quantization_config=quantization_config) + vocab_filepath = os.path.join(tempfile.mkdtemp(), "vocab.txt") + self._save_vocab(vocab_filepath) + + writer = self._get_metadata_writer(tflite_model, vocab_filepath) + tflite_model_with_metadata, metadata_json = writer.populate() + model_util.save_tflite(tflite_model_with_metadata, tflite_file) + with open(metadata_file, "w") as f: + f.write(metadata_json) + + @abc.abstractmethod + def _save_vocab(self, vocab_filepath: str): + """Saves the preprocessor's vocab to `vocab_filepath`.""" + + @abc.abstractmethod + def _get_metadata_writer(self, tflite_model: bytearray, vocab_filepath: str): + """Gets the metadata writer for the text classifier TFLite model.""" + + +class _AverageWordEmbeddingClassifier(TextClassifier): + """APIs to help create and train an Average Word Embedding text classifier.""" + + _DELIM_REGEX_PATTERN = r"[^\w\']+" + + def __init__(self, model_spec: ms.AverageWordEmbeddingClassifierSpec, + model_options: mo.AverageWordEmbeddingClassifierOptions, + hparams: hp.BaseHParams, label_names: Sequence[str]): + super().__init__(model_spec, hparams, label_names) + self._model_options = model_options + self._loss_function = "sparse_categorical_crossentropy" + self._metric_function = "accuracy" + self._text_preprocessor: ( + preprocessor.AverageWordEmbeddingClassifierPreprocessor) = None + + @classmethod + def create_average_word_embedding_classifier( + cls, train_data: text_ds.Dataset, validation_data: text_ds.Dataset, + options: text_classifier_options.TextClassifierOptions, + label_names: Sequence[str]) -> "_AverageWordEmbeddingClassifier": + """Creates, trains, and returns an Average Word Embedding classifier. + + Args: + train_data: Training data. + validation_data: Validation data. + options: Options for creating and training the text classifier. + label_names: Label names used in the data. + + Returns: + An Average Word Embedding classifier. + """ + average_word_embedding_classifier = _AverageWordEmbeddingClassifier( + model_spec=options.supported_model.value(), + model_options=options.model_options, + hparams=options.hparams, + label_names=train_data.label_names) + average_word_embedding_classifier._create_and_train_model( + train_data, validation_data) + return average_word_embedding_classifier + + def _create_and_train_model(self, train_data: text_ds.Dataset, + validation_data: text_ds.Dataset): + """Creates the Average Word Embedding classifier keras model and trains it. + + Args: + train_data: Training data. + validation_data: Validation data. + """ + (processed_train_data, processed_validation_data) = ( + self._load_and_run_preprocessor(train_data, validation_data)) + self._create_model() + self._optimizer = "rmsprop" + self._train_model(processed_train_data, processed_validation_data) + + def _load_and_run_preprocessor( + self, train_data: text_ds.Dataset, validation_data: text_ds.Dataset + ) -> Tuple[text_ds.Dataset, text_ds.Dataset]: + """Runs an AverageWordEmbeddingClassifierPreprocessor on the data. + + Args: + train_data: Training data. + validation_data: Validation data. + + Returns: + Preprocessed training data and preprocessed validation data. + """ + train_texts = [text.numpy()[0] for text, _ in train_data.gen_tf_dataset()] + validation_texts = [ + text.numpy()[0] for text, _ in validation_data.gen_tf_dataset() + ] + self._text_preprocessor = ( + preprocessor.AverageWordEmbeddingClassifierPreprocessor( + seq_len=self._model_options.seq_len, + do_lower_case=self._model_options.do_lower_case, + texts=train_texts + validation_texts, + vocab_size=self._model_options.vocab_size)) + return self._text_preprocessor.preprocess( + train_data), self._text_preprocessor.preprocess(validation_data) + + def _create_model(self): + """Creates an Average Word Embedding model.""" + self._model = tf.keras.Sequential([ + tf.keras.layers.InputLayer( + input_shape=[self._model_options.seq_len], dtype=tf.int32), + tf.keras.layers.Embedding( + len(self._text_preprocessor.get_vocab()), + self._model_options.wordvec_dim, + input_length=self._model_options.seq_len), + tf.keras.layers.GlobalAveragePooling1D(), + tf.keras.layers.Dense( + self._model_options.wordvec_dim, activation=tf.nn.relu), + tf.keras.layers.Dropout(self._model_options.dropout_rate), + tf.keras.layers.Dense(self._num_classes, activation="softmax") + ]) + + def _save_vocab(self, vocab_filepath: str): + with tf.io.gfile.GFile(vocab_filepath, "w") as f: + for token, index in self._text_preprocessor.get_vocab().items(): + f.write(f"{token} {index}\n") + + def _get_metadata_writer(self, tflite_model: bytearray, vocab_filepath: str): + return text_classifier_writer.MetadataWriter.create_for_regex_model( + model_buffer=tflite_model, + regex_tokenizer=metadata_writer.RegexTokenizer( + # TODO: Align with MediaPipe's RegexTokenizer. + delim_regex_pattern=self._DELIM_REGEX_PATTERN, + vocab_file_path=vocab_filepath), + labels=metadata_writer.Labels().add(list(self._label_names))) + + +class _BertClassifier(TextClassifier): + """APIs to help create and train a BERT-based text classifier.""" + + _INITIALIZER_RANGE = 0.02 + + def __init__(self, model_spec: ms.BertClassifierSpec, + model_options: mo.BertClassifierOptions, hparams: hp.BaseHParams, + label_names: Sequence[str]): + super().__init__(model_spec, hparams, label_names) + self._model_options = model_options + self._loss_function = tf.keras.losses.SparseCategoricalCrossentropy() + self._metric_function = tf.keras.metrics.SparseCategoricalAccuracy( + "test_accuracy", dtype=tf.float32) + self._text_preprocessor: preprocessor.BertClassifierPreprocessor = None + + @classmethod + def create_bert_classifier( + cls, train_data: text_ds.Dataset, validation_data: text_ds.Dataset, + options: text_classifier_options.TextClassifierOptions, + label_names: Sequence[str]) -> "_BertClassifier": + """Creates, trains, and returns a BERT-based classifier. + + Args: + train_data: Training data. + validation_data: Validation data. + options: Options for creating and training the text classifier. + label_names: Label names used in the data. + + Returns: + A BERT-based classifier. + """ + bert_classifier = _BertClassifier( + model_spec=options.supported_model.value(), + model_options=options.model_options, + hparams=options.hparams, + label_names=train_data.label_names) + bert_classifier._create_and_train_model(train_data, validation_data) + return bert_classifier + + def _create_and_train_model(self, train_data: text_ds.Dataset, + validation_data: text_ds.Dataset): + """Creates the BERT-based classifier keras model and trains it. + + Args: + train_data: Training data. + validation_data: Validation data. + """ + (processed_train_data, processed_validation_data) = ( + self._load_and_run_preprocessor(train_data, validation_data)) + self._create_model() + self._create_optimizer(processed_train_data) + self._train_model(processed_train_data, processed_validation_data) + + def _load_and_run_preprocessor( + self, train_data: text_ds.Dataset, validation_data: text_ds.Dataset + ) -> Tuple[text_ds.Dataset, text_ds.Dataset]: + """Loads a BertClassifierPreprocessor and runs it on the data. + + Args: + train_data: Training data. + validation_data: Validation data. + + Returns: + Preprocessed training data and preprocessed validation data. + """ + self._text_preprocessor = preprocessor.BertClassifierPreprocessor( + seq_len=self._model_options.seq_len, + do_lower_case=self._model_spec.do_lower_case, + uri=self._model_spec.uri) + return (self._text_preprocessor.preprocess(train_data), + self._text_preprocessor.preprocess(validation_data)) + + def _create_model(self): + """Creates a BERT-based classifier model. + + The model architecture consists of stacking a dense classification layer and + dropout layer on top of the BERT encoder outputs. + """ + encoder_inputs = dict( + input_word_ids=tf.keras.layers.Input( + shape=(self._model_options.seq_len,), dtype=tf.int32), + input_mask=tf.keras.layers.Input( + shape=(self._model_options.seq_len,), dtype=tf.int32), + input_type_ids=tf.keras.layers.Input( + shape=(self._model_options.seq_len,), dtype=tf.int32), + ) + encoder = hub.KerasLayer( + self._model_spec.uri, trainable=self._model_options.do_fine_tuning) + encoder_outputs = encoder(encoder_inputs) + pooled_output = encoder_outputs["pooled_output"] + + output = tf.keras.layers.Dropout(rate=self._model_options.dropout_rate)( + pooled_output) + initializer = tf.keras.initializers.TruncatedNormal( + stddev=self._INITIALIZER_RANGE) + output = tf.keras.layers.Dense( + self._num_classes, + kernel_initializer=initializer, + name="output", + activation="softmax", + dtype=tf.float32)( + output) + self._model = tf.keras.Model(inputs=encoder_inputs, outputs=output) + + def _create_optimizer(self, train_data: text_ds.Dataset): + """Loads an optimizer with a learning rate schedule. + + The decay steps in the learning rate schedule depend on the + `steps_per_epoch` which may depend on the size of the training data. + + Args: + train_data: Training data. + """ + self._hparams.steps_per_epoch = model_util.get_steps_per_epoch( + steps_per_epoch=self._hparams.steps_per_epoch, + batch_size=self._hparams.batch_size, + train_data=train_data) + total_steps = self._hparams.steps_per_epoch * self._hparams.epochs + warmup_steps = int(total_steps * 0.1) + initial_lr = self._hparams.learning_rate + self._optimizer = optimization.create_optimizer(initial_lr, total_steps, + warmup_steps) + + def _save_vocab(self, vocab_filepath: str): + tf.io.gfile.copy( + self._text_preprocessor.get_vocab_file(), + vocab_filepath, + overwrite=True) + + def _get_metadata_writer(self, tflite_model: bytearray, vocab_filepath: str): + return text_classifier_writer.MetadataWriter.create_for_bert_model( + model_buffer=tflite_model, + tokenizer=metadata_writer.BertTokenizer(vocab_filepath), + labels=metadata_writer.Labels().add(list(self._label_names)), + ids_name=self._model_spec.tflite_input_name["ids"], + mask_name=self._model_spec.tflite_input_name["mask"], + segment_name=self._model_spec.tflite_input_name["segment_ids"]) diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py new file mode 100644 index 000000000..de6b85751 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier_demo.py @@ -0,0 +1,108 @@ +# 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. +"""Demo for making a text classifier model by MediaPipe Model Maker.""" + +import os +import tempfile + +# Dependency imports + +from absl import app +from absl import flags +from absl import logging +import tensorflow as tf + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.core.utils import quantization +from mediapipe.model_maker.python.text.text_classifier import dataset as text_ds +from mediapipe.model_maker.python.text.text_classifier import model_spec as ms +from mediapipe.model_maker.python.text.text_classifier import text_classifier +from mediapipe.model_maker.python.text.text_classifier import text_classifier_options + +FLAGS = flags.FLAGS + + +def define_flags(): + flags.DEFINE_string('export_dir', None, + 'The directory to save exported files.') + flags.DEFINE_enum('supported_model', 'average_word_embedding', + ['average_word_embedding', 'bert'], + 'The text classifier to run.') + flags.mark_flag_as_required('export_dir') + + +def download_demo_data(): + """Downloads demo data, and returns directory path.""" + data_path = tf.keras.utils.get_file( + fname='SST-2.zip', + origin='https://dl.fbaipublicfiles.com/glue/data/SST-2.zip', + extract=True) + return os.path.join(os.path.dirname(data_path), 'SST-2') # folder name + + +def run(data_dir, + export_dir=tempfile.mkdtemp(), + supported_model=ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER): + """Runs demo.""" + + # Gets training data and validation data. + csv_params = text_ds.CSVParameters( + text_column='sentence', label_column='label', delimiter='\t') + train_data = text_ds.Dataset.from_csv( + filename=os.path.join(os.path.join(data_dir, 'train.tsv')), + csv_params=csv_params) + validation_data = text_ds.Dataset.from_csv( + filename=os.path.join(os.path.join(data_dir, 'dev.tsv')), + csv_params=csv_params) + + quantization_config = None + if supported_model == ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER: + hparams = hp.BaseHParams( + epochs=10, batch_size=32, learning_rate=0, export_dir=export_dir) + # Warning: This takes extremely long to run on CPU + elif supported_model == ms.SupportedModels.MOBILEBERT_CLASSIFIER: + quantization_config = quantization.QuantizationConfig.for_dynamic() + hparams = hp.BaseHParams( + epochs=3, batch_size=48, learning_rate=3e-5, export_dir=export_dir) + + # Fine-tunes the model. + options = text_classifier_options.TextClassifierOptions( + supported_model=supported_model, hparams=hparams) + model = text_classifier.TextClassifier.create(train_data, validation_data, + options) + + # Gets evaluation results. + _, acc = model.evaluate(validation_data) + print('Eval accuracy: %f' % acc) + + model.export_model(quantization_config=quantization_config) + model.export_labels(export_dir=options.hparams.export_dir) + + +def main(_): + logging.set_verbosity(logging.INFO) + data_dir = download_demo_data() + export_dir = os.path.expanduser(FLAGS.export_dir) + + if FLAGS.supported_model == 'average_word_embedding': + supported_model = ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER + elif FLAGS.supported_model == 'bert': + supported_model = ms.SupportedModels.MOBILEBERT_CLASSIFIER + + run(data_dir, export_dir, supported_model) + + +if __name__ == '__main__': + define_flags() + app.run(main) diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier_options.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier_options.py new file mode 100644 index 000000000..a02f17347 --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier_options.py @@ -0,0 +1,38 @@ +# 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. +"""User-facing customization options to create and train a text classifier.""" + +import dataclasses +from typing import Optional + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.text.text_classifier import model_options as mo +from mediapipe.model_maker.python.text.text_classifier import model_spec as ms + + +@dataclasses.dataclass +class TextClassifierOptions: + """User-facing options for creating the text classifier. + + Attributes: + supported_model: A preconfigured model spec. + hparams: Training hyperparameters the user can set to override the ones in + `supported_model`. + model_options: Model options the user can set to override the ones in + `supported_model`. The model options type should be consistent with the + architecture of the `supported_model`. + """ + supported_model: ms.SupportedModels + hparams: Optional[hp.BaseHParams] = None + model_options: Optional[mo.TextClassifierModelOptions] = None diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py new file mode 100644 index 000000000..41dbb464a --- /dev/null +++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier_test.py @@ -0,0 +1,138 @@ +# 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. + +import csv +import filecmp +import os + +import tensorflow as tf + +from mediapipe.model_maker.python.core import hyperparameters as hp +from mediapipe.model_maker.python.text.text_classifier import dataset +from mediapipe.model_maker.python.text.text_classifier import model_options as mo +from mediapipe.model_maker.python.text.text_classifier import model_spec as ms +from mediapipe.model_maker.python.text.text_classifier import text_classifier +from mediapipe.model_maker.python.text.text_classifier import text_classifier_options +from mediapipe.tasks.python.test import test_utils + + +class TextClassifierTest(tf.test.TestCase): + + _AVERAGE_WORD_EMBEDDING_JSON_FILE = ( + test_utils.get_test_data_path('average_word_embedding_metadata.json')) + + def _get_data(self): + labels_and_text = (('pos', 'super good'), (('neg', 'really bad'))) + csv_file = os.path.join(self.get_temp_dir(), 'data.csv') + if os.path.exists(csv_file): + return csv_file + fieldnames = ['text', 'label'] + with open(csv_file, 'w') as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + for label, text in labels_and_text: + writer.writerow({'text': text, 'label': label}) + csv_params = dataset.CSVParameters(text_column='text', label_column='label') + all_data = dataset.Dataset.from_csv( + filename=csv_file, csv_params=csv_params) + return all_data.split(0.5) + + def test_create_and_train_average_word_embedding_model(self): + train_data, validation_data = self._get_data() + options = text_classifier_options.TextClassifierOptions( + supported_model=ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER, + hparams=hp.BaseHParams(epochs=1, batch_size=1, learning_rate=0)) + average_word_embedding_classifier = text_classifier.TextClassifier.create( + train_data, validation_data, options) + + _, accuracy = average_word_embedding_classifier.evaluate(validation_data) + self.assertGreaterEqual(accuracy, 0.0) + + # Test export_model + average_word_embedding_classifier.export_model() + output_metadata_file = os.path.join(options.hparams.export_dir, + 'metadata.json') + output_tflite_file = os.path.join(options.hparams.export_dir, + 'model.tflite') + + self.assertTrue(os.path.exists(output_tflite_file)) + self.assertGreater(os.path.getsize(output_tflite_file), 0) + + self.assertTrue(os.path.exists(output_metadata_file)) + self.assertGreater(os.path.getsize(output_metadata_file), 0) + self.assertTrue( + filecmp.cmp(output_metadata_file, + self._AVERAGE_WORD_EMBEDDING_JSON_FILE)) + + def test_create_and_train_bert(self): + train_data, validation_data = self._get_data() + options = text_classifier_options.TextClassifierOptions( + supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER, + model_options=mo.BertClassifierOptions(do_fine_tuning=False, seq_len=2), + hparams=hp.BaseHParams( + epochs=1, + batch_size=1, + learning_rate=3e-5, + distribution_strategy='off')) + bert_classifier = text_classifier.TextClassifier.create( + train_data, validation_data, options) + + _, accuracy = bert_classifier.evaluate(validation_data) + self.assertGreaterEqual(accuracy, 0.0) + # TODO: Add a unit test that does not run OOM. + + def test_label_mismatch(self): + options = ( + text_classifier_options.TextClassifierOptions( + supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER)) + train_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]]) + train_data = dataset.Dataset(train_tf_dataset, 1, ['foo']) + validation_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]]) + validation_data = dataset.Dataset(validation_tf_dataset, 1, ['bar']) + with self.assertRaisesRegex( + ValueError, + 'Training data label names .* not equal to validation data label names' + ): + text_classifier.TextClassifier.create(train_data, validation_data, + options) + + def test_options_mismatch(self): + train_data, validation_data = self._get_data() + + avg_options = ( + text_classifier_options.TextClassifierOptions( + supported_model=ms.SupportedModels.MOBILEBERT_CLASSIFIER, + model_options=mo.AverageWordEmbeddingClassifierOptions())) + with self.assertRaisesRegex( + ValueError, 'Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER, got' + ' SupportedModels.MOBILEBERT_CLASSIFIER'): + text_classifier.TextClassifier.create(train_data, validation_data, + avg_options) + + bert_options = ( + text_classifier_options.TextClassifierOptions( + supported_model=( + ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER), + model_options=mo.BertClassifierOptions())) + with self.assertRaisesRegex( + ValueError, 'Expected MOBILEBERT_CLASSIFIER, got' + ' SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER'): + text_classifier.TextClassifier.create(train_data, validation_data, + bert_options) + + +if __name__ == '__main__': + # Load compressed models from tensorflow_hub + os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED' + tf.test.main() From b4972ed4ae2f52da33e0ded75a8807293458aced Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 20:23:38 -0800 Subject: [PATCH 21/21] Update GestureRecognitionResult to GestureRecognizerResult. PiperOrigin-RevId: 487712873 --- .../tasks/cc/components/containers/BUILD | 9 --- .../tasks/cc/vision/gesture_recognizer/BUILD | 11 ++- .../gesture_recognizer/gesture_recognizer.cc | 6 +- .../gesture_recognizer/gesture_recognizer.h | 21 +++--- .../gesture_recognizer_result.h} | 16 ++--- .../com/google/mediapipe/tasks/vision/BUILD | 2 +- .../gesturerecognizer/GestureRecognizer.java | 30 ++++---- ...sult.java => GestureRecognizerResult.java} | 10 +-- .../vision/handlandmarker/HandLandmarker.java | 6 +- .../GestureRecognizerTest.java | 72 +++++++++---------- .../tasks/python/vision/gesture_recognizer.py | 6 +- .../gesture_recognizer/gesture_recognizer.ts | 4 +- .../gesture_recognizer_result.ts | 2 +- 13 files changed, 95 insertions(+), 100 deletions(-) rename mediapipe/tasks/cc/{components/containers/gesture_recognition_result.h => vision/gesture_recognizer/gesture_recognizer_result.h} (79%) rename mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/{GestureRecognitionResult.java => GestureRecognizerResult.java} (95%) diff --git a/mediapipe/tasks/cc/components/containers/BUILD b/mediapipe/tasks/cc/components/containers/BUILD index 5004383d2..bd66a0f28 100644 --- a/mediapipe/tasks/cc/components/containers/BUILD +++ b/mediapipe/tasks/cc/components/containers/BUILD @@ -21,15 +21,6 @@ cc_library( hdrs = ["rect.h"], ) -cc_library( - name = "gesture_recognition_result", - hdrs = ["gesture_recognition_result.h"], - deps = [ - "//mediapipe/framework/formats:classification_cc_proto", - "//mediapipe/framework/formats:landmark_cc_proto", - ], -) - cc_library( name = "category", srcs = ["category.cc"], diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD b/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD index f32d4cc58..75289b1e8 100644 --- a/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD +++ b/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD @@ -124,12 +124,22 @@ cc_library( alwayslink = 1, ) +cc_library( + name = "gesture_recognizer_result", + hdrs = ["gesture_recognizer_result.h"], + deps = [ + "//mediapipe/framework/formats:classification_cc_proto", + "//mediapipe/framework/formats:landmark_cc_proto", + ], +) + cc_library( name = "gesture_recognizer", srcs = ["gesture_recognizer.cc"], hdrs = ["gesture_recognizer.h"], deps = [ ":gesture_recognizer_graph", + ":gesture_recognizer_result", ":hand_gesture_recognizer_graph", "//mediapipe/framework:packet", "//mediapipe/framework/api2:builder", @@ -140,7 +150,6 @@ cc_library( "//mediapipe/framework/formats:rect_cc_proto", "//mediapipe/tasks/cc:common", "//mediapipe/tasks/cc/components:image_preprocessing", - "//mediapipe/tasks/cc/components/containers:gesture_recognition_result", "//mediapipe/tasks/cc/components/processors:classifier_options", "//mediapipe/tasks/cc/components/processors/proto:classifier_options_cc_proto", "//mediapipe/tasks/cc/core:base_options", diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc index 38cb5169d..8d555b12c 100644 --- a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc +++ b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc @@ -58,8 +58,6 @@ namespace { using GestureRecognizerGraphOptionsProto = ::mediapipe::tasks::vision:: gesture_recognizer::proto::GestureRecognizerGraphOptions; -using ::mediapipe::tasks::components::containers::GestureRecognitionResult; - constexpr char kHandGestureSubgraphTypeName[] = "mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph"; @@ -214,7 +212,7 @@ absl::StatusOr> GestureRecognizer::Create( std::move(packets_callback)); } -absl::StatusOr GestureRecognizer::Recognize( +absl::StatusOr GestureRecognizer::Recognize( mediapipe::Image image, std::optional image_processing_options) { if (image.UsesGpu()) { @@ -250,7 +248,7 @@ absl::StatusOr GestureRecognizer::Recognize( }; } -absl::StatusOr GestureRecognizer::RecognizeForVideo( +absl::StatusOr GestureRecognizer::RecognizeForVideo( mediapipe::Image image, int64 timestamp_ms, std::optional image_processing_options) { if (image.UsesGpu()) { diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.h b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.h index 3f3d7acfe..392aa586f 100644 --- a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.h +++ b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.h @@ -24,12 +24,12 @@ limitations under the License. #include "mediapipe/framework/formats/classification.pb.h" #include "mediapipe/framework/formats/image.h" #include "mediapipe/framework/formats/landmark.pb.h" -#include "mediapipe/tasks/cc/components/containers/gesture_recognition_result.h" #include "mediapipe/tasks/cc/components/processors/classifier_options.h" #include "mediapipe/tasks/cc/core/base_options.h" #include "mediapipe/tasks/cc/vision/core/base_vision_task_api.h" #include "mediapipe/tasks/cc/vision/core/image_processing_options.h" #include "mediapipe/tasks/cc/vision/core/running_mode.h" +#include "mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer_result.h" namespace mediapipe { namespace tasks { @@ -81,9 +81,8 @@ struct GestureRecognizerOptions { // The user-defined result callback for processing live stream data. // The result callback should only be specified when the running mode is set // to RunningMode::LIVE_STREAM. - std::function, - const Image&, int64)> + std::function, const Image&, + int64)> result_callback = nullptr; }; @@ -104,7 +103,7 @@ struct GestureRecognizerOptions { // 'width' and 'height' fields is NOT supported and will result in an // invalid argument error being returned. // Outputs: -// GestureRecognitionResult +// GestureRecognizerResult // - The hand gesture recognition results. class GestureRecognizer : tasks::vision::core::BaseVisionTaskApi { public: @@ -139,7 +138,7 @@ class GestureRecognizer : tasks::vision::core::BaseVisionTaskApi { // The image can be of any size with format RGB or RGBA. // TODO: Describes how the input image will be preprocessed // after the yuv support is implemented. - absl::StatusOr Recognize( + absl::StatusOr Recognize( Image image, std::optional image_processing_options = std::nullopt); @@ -157,10 +156,10 @@ class GestureRecognizer : tasks::vision::core::BaseVisionTaskApi { // The image can be of any size with format RGB or RGBA. It's required to // provide the video frame's timestamp (in milliseconds). The input timestamps // must be monotonically increasing. - absl::StatusOr - RecognizeForVideo(Image image, int64 timestamp_ms, - std::optional - image_processing_options = std::nullopt); + absl::StatusOr RecognizeForVideo( + Image image, int64 timestamp_ms, + std::optional image_processing_options = + std::nullopt); // Sends live image data to perform gesture recognition, and the results will // be available via the "result_callback" provided in the @@ -179,7 +178,7 @@ class GestureRecognizer : tasks::vision::core::BaseVisionTaskApi { // and will result in an invalid argument error being returned. // // The "result_callback" provides - // - A vector of GestureRecognitionResult, each is the recognized results + // - A vector of GestureRecognizerResult, each is the recognized results // for a input frame. // - The const reference to the corresponding input image that the gesture // recognizer runs on. Note that the const reference to the image will no diff --git a/mediapipe/tasks/cc/components/containers/gesture_recognition_result.h b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer_result.h similarity index 79% rename from mediapipe/tasks/cc/components/containers/gesture_recognition_result.h rename to mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer_result.h index 4e2e8d775..914217801 100644 --- a/mediapipe/tasks/cc/components/containers/gesture_recognition_result.h +++ b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer_result.h @@ -13,20 +13,20 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_GESTURE_RECOGNITION_RESULT_H_ -#define MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_GESTURE_RECOGNITION_RESULT_H_ +#ifndef MEDIAPIPE_TASKS_CC_VISION_GESTURE_RECOGNIZER_GESTURE_RECOGNIZER_RESULT_H_ +#define MEDIAPIPE_TASKS_CC_VISION_GESTURE_RECOGNIZER_GESTURE_RECOGNIZER_RESULT_H_ #include "mediapipe/framework/formats/classification.pb.h" #include "mediapipe/framework/formats/landmark.pb.h" namespace mediapipe { namespace tasks { -namespace components { -namespace containers { +namespace vision { +namespace gesture_recognizer { // The gesture recognition result from GestureRecognizer, where each vector // element represents a single hand detected in the image. -struct GestureRecognitionResult { +struct GestureRecognizerResult { // Recognized hand gestures with sorted order such that the winning label is // the first item in the list. std::vector gestures; @@ -38,9 +38,9 @@ struct GestureRecognitionResult { std::vector hand_world_landmarks; }; -} // namespace containers -} // namespace components +} // namespace gesture_recognizer +} // namespace vision } // namespace tasks } // namespace mediapipe -#endif // MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_GESTURE_RECOGNITION_RESULT_H_ +#endif // MEDIAPIPE_TASKS_CC_VISION_GESTURE_RECOGNIZER_GESTURE_RECOGNIZER_RESULT_H_ diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD index 2b71dcdd8..4dc4a547e 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD @@ -111,8 +111,8 @@ android_library( android_library( name = "gesturerecognizer", srcs = [ - "gesturerecognizer/GestureRecognitionResult.java", "gesturerecognizer/GestureRecognizer.java", + "gesturerecognizer/GestureRecognizerResult.java", ], javacopts = [ "-Xep:AndroidJdkLibsChecker:OFF", diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java index d1ba94a1f..7cbedb32e 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java @@ -64,9 +64,9 @@ import java.util.Optional; *

- *
  • Output GestureRecognitionResult {@link GestureRecognitionResult} + *
  • Output GestureRecognizerResult {@link GestureRecognizerResult} *
      - *
    • A GestureRecognitionResult containing hand landmarks and recognized hand gestures. + *
    • A GestureRecognizerResult containing hand landmarks and recognized hand gestures. *
    * */ @@ -152,21 +152,21 @@ public final class GestureRecognizer extends BaseVisionTaskApi { public static GestureRecognizer createFromOptions( Context context, GestureRecognizerOptions recognizerOptions) { // TODO: Consolidate OutputHandler and TaskRunner. - OutputHandler handler = new OutputHandler<>(); + OutputHandler handler = new OutputHandler<>(); handler.setOutputPacketConverter( - new OutputHandler.OutputPacketConverter() { + new OutputHandler.OutputPacketConverter() { @Override - public GestureRecognitionResult convertToTaskResult(List packets) { + public GestureRecognizerResult convertToTaskResult(List packets) { // If there is no hands detected in the image, just returns empty lists. if (packets.get(HAND_GESTURES_OUT_STREAM_INDEX).isEmpty()) { - return GestureRecognitionResult.create( + return GestureRecognizerResult.create( new ArrayList<>(), new ArrayList<>(), new ArrayList<>(), new ArrayList<>(), packets.get(HAND_GESTURES_OUT_STREAM_INDEX).getTimestamp()); } - return GestureRecognitionResult.create( + return GestureRecognizerResult.create( PacketGetter.getProtoVector( packets.get(LANDMARKS_OUT_STREAM_INDEX), NormalizedLandmarkList.parser()), PacketGetter.getProtoVector( @@ -228,7 +228,7 @@ public final class GestureRecognizer extends BaseVisionTaskApi { * @param image a MediaPipe {@link MPImage} object for processing. * @throws MediaPipeException if there is an internal error. */ - public GestureRecognitionResult recognize(MPImage image) { + public GestureRecognizerResult recognize(MPImage image) { return recognize(image, ImageProcessingOptions.builder().build()); } @@ -252,10 +252,10 @@ public final class GestureRecognizer extends BaseVisionTaskApi { * region-of-interest. * @throws MediaPipeException if there is an internal error. */ - public GestureRecognitionResult recognize( + public GestureRecognizerResult recognize( MPImage image, ImageProcessingOptions imageProcessingOptions) { validateImageProcessingOptions(imageProcessingOptions); - return (GestureRecognitionResult) processImageData(image, imageProcessingOptions); + return (GestureRecognizerResult) processImageData(image, imageProcessingOptions); } /** @@ -276,7 +276,7 @@ public final class GestureRecognizer extends BaseVisionTaskApi { * @param timestampMs the input timestamp (in milliseconds). * @throws MediaPipeException if there is an internal error. */ - public GestureRecognitionResult recognizeForVideo(MPImage image, long timestampMs) { + public GestureRecognizerResult recognizeForVideo(MPImage image, long timestampMs) { return recognizeForVideo(image, ImageProcessingOptions.builder().build(), timestampMs); } @@ -303,10 +303,10 @@ public final class GestureRecognizer extends BaseVisionTaskApi { * region-of-interest. * @throws MediaPipeException if there is an internal error. */ - public GestureRecognitionResult recognizeForVideo( + public GestureRecognizerResult recognizeForVideo( MPImage image, ImageProcessingOptions imageProcessingOptions, long timestampMs) { validateImageProcessingOptions(imageProcessingOptions); - return (GestureRecognitionResult) processVideoData(image, imageProcessingOptions, timestampMs); + return (GestureRecognizerResult) processVideoData(image, imageProcessingOptions, timestampMs); } /** @@ -425,7 +425,7 @@ public final class GestureRecognizer extends BaseVisionTaskApi { * recognizer is in the live stream mode. */ public abstract Builder setResultListener( - ResultListener value); + ResultListener value); /** Sets an optional error listener. */ public abstract Builder setErrorListener(ErrorListener value); @@ -472,7 +472,7 @@ public final class GestureRecognizer extends BaseVisionTaskApi { abstract Optional customGesturesClassifierOptions(); - abstract Optional> resultListener(); + abstract Optional> resultListener(); abstract Optional errorListener(); diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerResult.java similarity index 95% rename from mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java rename to mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerResult.java index e9e1ebe8a..ef76bf226 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerResult.java @@ -29,20 +29,20 @@ import java.util.List; /** Represents the gesture recognition results generated by {@link GestureRecognizer}. */ @AutoValue -public abstract class GestureRecognitionResult implements TaskResult { +public abstract class GestureRecognizerResult implements TaskResult { private static final int kGestureDefaultIndex = -1; /** - * Creates a {@link GestureRecognitionResult} instance from the lists of landmarks, handedness, - * and gestures protobuf messages. + * Creates a {@link GestureRecognizerResult} instance from the lists of landmarks, handedness, and + * gestures protobuf messages. * * @param landmarksProto a List of {@link NormalizedLandmarkList} * @param worldLandmarksProto a List of {@link LandmarkList} * @param handednessesProto a List of {@link ClassificationList} * @param gesturesProto a List of {@link ClassificationList} */ - static GestureRecognitionResult create( + static GestureRecognizerResult create( List landmarksProto, List worldLandmarksProto, List handednessesProto, @@ -106,7 +106,7 @@ public abstract class GestureRecognitionResult implements TaskResult { classification.getDisplayName())); } } - return new AutoValue_GestureRecognitionResult( + return new AutoValue_GestureRecognizerResult( timestampMs, Collections.unmodifiableList(multiHandLandmarks), Collections.unmodifiableList(multiHandWorldLandmarks), diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java index 089e1c6e7..9be489bbe 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java @@ -242,8 +242,7 @@ public final class HandLandmarker extends BaseVisionTaskApi { * region-of-interest. * @throws MediaPipeException if there is an internal error. */ - public HandLandmarkerResult detect( - MPImage image, ImageProcessingOptions imageProcessingOptions) { + public HandLandmarkerResult detect(MPImage image, ImageProcessingOptions imageProcessingOptions) { validateImageProcessingOptions(imageProcessingOptions); return (HandLandmarkerResult) processImageData(image, imageProcessingOptions); } @@ -296,8 +295,7 @@ public final class HandLandmarker extends BaseVisionTaskApi { public HandLandmarkerResult detectForVideo( MPImage image, ImageProcessingOptions imageProcessingOptions, long timestampMs) { validateImageProcessingOptions(imageProcessingOptions); - return (HandLandmarkerResult) - processVideoData(image, imageProcessingOptions, timestampMs); + return (HandLandmarkerResult) processVideoData(image, imageProcessingOptions, timestampMs); } /** diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java index f76c4eaab..c0be4cffe 100644 --- a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java @@ -80,10 +80,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -98,7 +98,7 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(NO_HANDS_IMAGE)); assertThat(actualResult.landmarks()).isEmpty(); assertThat(actualResult.worldLandmarks()).isEmpty(); @@ -119,10 +119,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); // Only contains one top scoring gesture. assertThat(actualResult.gestures().get(0)).hasSize(1); assertActualGestureEqualExpectedGesture( @@ -141,7 +141,7 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(TWO_HANDS_IMAGE)); assertThat(actualResult.handednesses()).hasSize(2); } @@ -160,7 +160,7 @@ public class GestureRecognizerTest { GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); ImageProcessingOptions imageProcessingOptions = ImageProcessingOptions.builder().setRotationDegrees(-90).build(); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize( getImageFromAsset(POINTING_UP_ROTATED_IMAGE), imageProcessingOptions); assertThat(actualResult.gestures()).hasSize(1); @@ -179,10 +179,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(FIST_LANDMARKS, FIST_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(FIST_LANDMARKS, FIST_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -199,10 +199,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(FIST_LANDMARKS, ROCK_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(FIST_LANDMARKS, ROCK_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -223,10 +223,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(FIST_LANDMARKS, FIST_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(FIST_LANDMARKS, FIST_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -247,7 +247,7 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); assertThat(actualResult.landmarks()).isEmpty(); assertThat(actualResult.worldLandmarks()).isEmpty(); @@ -280,7 +280,7 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); assertThat(actualResult.landmarks()).isEmpty(); assertThat(actualResult.worldLandmarks()).isEmpty(); @@ -306,10 +306,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(FIST_LANDMARKS, FIST_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(FIST_LANDMARKS, FIST_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -478,10 +478,10 @@ public class GestureRecognizerTest { GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE)); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); } @@ -497,10 +497,10 @@ public class GestureRecognizerTest { .build(); GestureRecognizer gestureRecognizer = GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); for (int i = 0; i < 3; i++) { - GestureRecognitionResult actualResult = + GestureRecognizerResult actualResult = gestureRecognizer.recognizeForVideo( getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ i); assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); @@ -510,8 +510,8 @@ public class GestureRecognizerTest { @Test public void recognize_failsWithOutOfOrderInputTimestamps() throws Exception { MPImage image = getImageFromAsset(THUMB_UP_IMAGE); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); GestureRecognizerOptions options = GestureRecognizerOptions.builder() .setBaseOptions( @@ -542,8 +542,8 @@ public class GestureRecognizerTest { @Test public void recognize_successWithLiveSteamMode() throws Exception { MPImage image = getImageFromAsset(THUMB_UP_IMAGE); - GestureRecognitionResult expectedResult = - getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); + GestureRecognizerResult expectedResult = + getExpectedGestureRecognizerResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL); GestureRecognizerOptions options = GestureRecognizerOptions.builder() .setBaseOptions( @@ -572,7 +572,7 @@ public class GestureRecognizerTest { return new BitmapImageBuilder(BitmapFactory.decodeStream(istr)).build(); } - private static GestureRecognitionResult getExpectedGestureRecognitionResult( + private static GestureRecognizerResult getExpectedGestureRecognizerResult( String filePath, String gestureLabel) throws Exception { AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets(); InputStream istr = assetManager.open(filePath); @@ -583,7 +583,7 @@ public class GestureRecognizerTest { .addClassification( ClassificationProto.Classification.newBuilder().setLabel(gestureLabel)) .build(); - return GestureRecognitionResult.create( + return GestureRecognizerResult.create( Arrays.asList(landmarksDetectionResultProto.getLandmarks()), Arrays.asList(landmarksDetectionResultProto.getWorldLandmarks()), Arrays.asList(landmarksDetectionResultProto.getClassifications()), @@ -592,7 +592,7 @@ public class GestureRecognizerTest { } private static void assertActualResultApproximatelyEqualsToExpectedResult( - GestureRecognitionResult actualResult, GestureRecognitionResult expectedResult) { + GestureRecognizerResult actualResult, GestureRecognizerResult expectedResult) { // Expects to have the same number of hands detected. assertThat(actualResult.landmarks()).hasSize(expectedResult.landmarks().size()); assertThat(actualResult.worldLandmarks()).hasSize(expectedResult.worldLandmarks().size()); diff --git a/mediapipe/tasks/python/vision/gesture_recognizer.py b/mediapipe/tasks/python/vision/gesture_recognizer.py index d118a315a..9b6fd8cab 100644 --- a/mediapipe/tasks/python/vision/gesture_recognizer.py +++ b/mediapipe/tasks/python/vision/gesture_recognizer.py @@ -80,7 +80,7 @@ class GestureRecognizerResult: def _build_recognition_result( output_packets: Mapping[str, packet_module.Packet]) -> GestureRecognizerResult: - """Consturcts a `GestureRecognizerResult` from output packets.""" + """Constructs a `GestureRecognizerResult` from output packets.""" gestures_proto_list = packet_getter.get_proto_list( output_packets[_HAND_GESTURE_STREAM_NAME]) handedness_proto_list = packet_getter.get_proto_list( @@ -270,9 +270,9 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) return - gesture_recognition_result = _build_recognition_result(output_packets) + gesture_recognizer_result = _build_recognition_result(output_packets) timestamp = output_packets[_HAND_GESTURE_STREAM_NAME].timestamp - options.result_callback(gesture_recognition_result, image, + options.result_callback(gesture_recognizer_result, image, timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) task_info = _TaskInfo( diff --git a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer.ts b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer.ts index 1275ae875..c24d1a7b3 100644 --- a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer.ts +++ b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer.ts @@ -35,7 +35,7 @@ import {createMediaPipeLib, FileLocator, ImageSource, WasmModule} from '../../.. // Placeholder for internal dependency on trusted resource url import {GestureRecognizerOptions} from './gesture_recognizer_options'; -import {GestureRecognitionResult} from './gesture_recognizer_result'; +import {GestureRecognizerResult} from './gesture_recognizer_result'; export {ImageSource}; @@ -237,7 +237,7 @@ export class GestureRecognizer extends TaskRunner { * @return The detected gestures. */ recognize(imageSource: ImageSource, timestamp: number = performance.now()): - GestureRecognitionResult { + GestureRecognizerResult { this.gestures = []; this.landmarks = []; this.worldLandmarks = []; diff --git a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts index 08c8c2931..7c295c9e9 100644 --- a/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts +++ b/mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts @@ -20,7 +20,7 @@ import {Landmark} from '../../../../tasks/web/components/containers/landmark'; /** * Represents the gesture recognition results generated by `GestureRecognizer`. */ -export declare interface GestureRecognitionResult { +export declare interface GestureRecognizerResult { /** Hand landmarks of detected hands. */ landmarks: Landmark[][];