# Copyright 2022 The MediaPipe Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # 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 text classifier task.""" import dataclasses from typing import Optional, List 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.components.processors.proto import classifier_options_pb2 from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2 from mediapipe.tasks.python.components.containers import classification_result as classification_result_module 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 = classification_result_module.ClassificationResult _BaseOptions = base_options_module.BaseOptions _TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions _ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions _TaskInfo = task_info_module.TaskInfo _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' @dataclasses.dataclass class TextClassifierOptions: """Options for the text classifier task. Attributes: base_options: Base options for the text classifier task. display_names_locale: The locale to use for display names specified through the TFLite Model Metadata. max_results: The maximum number of top-scored classification results to return. score_threshold: Overrides the ones provided in the model metadata. Results below this value are rejected. category_allowlist: Allowlist of category names. If non-empty, classification results whose category name is not in this set will be filtered out. Duplicate or unknown category names are ignored. Mutually exclusive with `category_denylist`. category_denylist: Denylist of category names. If non-empty, classification results whose category name is in this set will be filtered out. Duplicate or unknown category names are ignored. Mutually exclusive with `category_allowlist`. """ base_options: _BaseOptions display_names_locale: Optional[str] = None max_results: Optional[int] = None score_threshold: Optional[float] = None category_allowlist: Optional[List[str]] = None category_denylist: Optional[List[str]] = None @doc_controls.do_not_generate_docs def to_pb2(self) -> _TextClassifierGraphOptionsProto: """Generates an TextClassifierOptions protobuf object.""" base_options_proto = self.base_options.to_pb2() classifier_options_proto = _ClassifierOptionsProto( score_threshold=self.score_threshold, category_allowlist=self.category_allowlist, category_denylist=self.category_denylist, display_names_locale=self.display_names_locale, max_results=self.max_results) return _TextClassifierGraphOptionsProto( base_options=base_options_proto, classifier_options=classifier_options_proto) class TextClassifier(base_text_task_api.BaseTextTaskApi): """Class that performs classification on text. This API expects a TFLite model with (optional) TFLite Model Metadata that contains the mandatory (described below) input tensors, output tensor, and the optional (but recommended) category labels as AssociatedFiles with type TENSOR_AXIS_LABELS per output classification tensor. Metadata is required for models with int32 input tensors because it contains the input process unit for the model's Tokenizer. No metadata is required for models with string input tensors. Input tensors: (kTfLiteInt32) - 3 input tensors of size `[batch_size x bert_max_seq_len]` representing the input ids, segment ids, and mask ids - or 1 input tensor of size `[batch_size x max_seq_len]` representing the input ids or (kTfLiteString) - 1 input tensor that is shapeless or has shape [1] containing the input string At least one output tensor with: (kTfLiteFloat32/kBool) - `[1 x N]` array with `N` represents the number of categories. - optional (but recommended) category labels 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 `TextClassifierOptions` 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. """ @classmethod def create_from_model_path(cls, model_path: str) -> 'TextClassifier': """Creates an `TextClassifier` object from a TensorFlow Lite model and the default `TextClassifierOptions`. Args: model_path: Path to the model. Returns: `TextClassifier` object that's created from the model file and the default `TextClassifierOptions`. Raises: ValueError: If failed to create `TextClassifier` object from the provided file such as invalid file path. RuntimeError: If other types of error occurred. """ base_options = _BaseOptions(model_asset_path=model_path) options = TextClassifierOptions(base_options=base_options) return cls.create_from_options(options) @classmethod def create_from_options(cls, options: TextClassifierOptions) -> 'TextClassifier': """Creates the `TextClassifier` object from text classifier options. Args: options: Options for the text classifier task. Returns: `TextClassifier` object that's created from `options`. Raises: ValueError: If failed to create `TextClassifier` object from `TextClassifierOptions` such as missing the model. RuntimeError: If other types of error occurred. """ task_info = _TaskInfo( task_graph=_TASK_GRAPH_NAME, input_streams=[':'.join([_TEXT_TAG, _TEXT_IN_STREAM_NAME])], output_streams=[ ':'.join([_CLASSIFICATIONS_TAG, _CLASSIFICATIONS_STREAM_NAME]) ], task_options=options) return cls(task_info.generate_graph_config()) def classify(self, text: str) -> TextClassifierResult: """Performs classification on the input `text`. Args: text: The input text. Returns: A `TextClassifierResult` object that contains a list of text classifications. Raises: ValueError: If any of the input arguments is invalid. RuntimeError: If text classification failed to run. """ output_packets = self._runner.process( {_TEXT_IN_STREAM_NAME: packet_creator.create_string(text)}) classification_result_proto = classifications_pb2.ClassificationResult() classification_result_proto.CopyFrom( packet_getter.get_proto(output_packets[_CLASSIFICATIONS_STREAM_NAME])) return TextClassifierResult.create_from_pb2(classification_result_proto)