147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe text classifier task."""
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import dataclasses
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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# TODO: Import MPImage directly one we have an alias
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from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
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from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2
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from mediapipe.tasks.python.components.containers import classifications
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from mediapipe.tasks.python.components.processors import classifier_options
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.text.core import base_text_task_api
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_BaseOptions = base_options_module.BaseOptions
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_TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions
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_ClassifierOptions = classifier_options.ClassifierOptions
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_TaskInfo = task_info_module.TaskInfo
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_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
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_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
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_TEXT_IN_STREAM_NAME = 'text_in'
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_TEXT_TAG = 'TEXT'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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class TextClassifierOptions:
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"""Options for the text classifier task.
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Attributes:
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base_options: Base options for the text classifier task.
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classifier_options: Options for the text classification task.
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"""
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base_options: _BaseOptions
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classifier_options: _ClassifierOptions = _ClassifierOptions()
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _TextClassifierGraphOptionsProto:
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"""Generates an TextClassifierOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
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classifier_options_proto = self.classifier_options.to_pb2()
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return _TextClassifierGraphOptionsProto(
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base_options=base_options_proto,
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classifier_options=classifier_options_proto)
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class TextClassifier(base_text_task_api.BaseTextTaskApi):
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"""Class that performs classification on text."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'TextClassifier':
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"""Creates an `TextClassifier` object from a TensorFlow Lite model and the default `TextClassifierOptions`.
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Args:
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model_path: Path to the model.
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Returns:
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`TextClassifier` object that's created from the model file and the
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default `TextClassifierOptions`.
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Raises:
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ValueError: If failed to create `TextClassifier` object from the provided
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file such as invalid file path.
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RuntimeError: If other types of error occurred.
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"""
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base_options = _BaseOptions(model_asset_path=model_path)
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options = TextClassifierOptions(base_options=base_options)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(cls,
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options: TextClassifierOptions) -> 'TextClassifier':
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"""Creates the `TextClassifier` object from text classifier options.
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Args:
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options: Options for the text classifier task.
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Returns:
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`TextClassifier` object that's created from `options`.
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Raises:
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ValueError: If failed to create `TextClassifier` object from
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`TextClassifierOptions` such as missing the model.
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RuntimeError: If other types of error occurred.
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"""
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_TEXT_TAG, _TEXT_IN_STREAM_NAME])
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],
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output_streams=[
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':'.join([
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_CLASSIFICATION_RESULT_TAG,
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_CLASSIFICATION_RESULT_OUT_STREAM_NAME
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])
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],
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task_options=options)
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return cls(task_info.generate_graph_config())
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def classify(
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self,
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text: str,
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) -> classifications.ClassificationResult:
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"""Performs classification on the input `text`.
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Args:
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text: The input text.
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Returns:
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A classification result object that contains a list of classifications.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If text classification failed to run.
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"""
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output_packets = self._runner.process({
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_TEXT_IN_STREAM_NAME: packet_creator.create_string(text)
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})
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classification_result_proto = classifications_pb2.ClassificationResult()
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classification_result_proto.CopyFrom(
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packet_getter.get_proto(
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output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
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return classifications.ClassificationResult([
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classifications.Classifications.create_from_pb2(classification)
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for classification in classification_result_proto.classifications
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])
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