MediaPipe TeamandCopybara-Service
bd7888cc0c
2. Move desired_precision and desired_recall from evaluate to hyperparameters so recall@precision metrics will be reported for both training and evaluation. This also fixes a bug where recompiling the model with the previously initialized metric objects would not properly reset the metric states. 3. Remove redundant label_names from create_... class methods in text_classifier. This information is already provided by the datasets. 4. Change loss function to FocalLoss. 5. Re-enable text_classifier unit tests using ExBert 6. Add input names to avoid flaky auto-assigned input names. PiperOrigin-RevId: 550992146
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
# Copyright 2022 The MediaPipe Authors.
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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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"""Specifications for text classifier models."""
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import dataclasses
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import enum
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import functools
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from mediapipe.model_maker.python.core.utils import file_util
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from mediapipe.model_maker.python.text.core import bert_model_spec
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from mediapipe.model_maker.python.text.text_classifier import hyperparameters as hp
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from mediapipe.model_maker.python.text.text_classifier import model_options as mo
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MOBILEBERT_TINY_FILES = file_util.DownloadedFiles(
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'text_classifier/mobilebert_tiny',
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'https://storage.googleapis.com/mediapipe-assets/mobilebert_tiny.tar.gz',
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is_folder=True,
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)
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EXBERT_FILES = file_util.DownloadedFiles(
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'text_classifier/exbert',
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'https://storage.googleapis.com/mediapipe-assets/exbert.tar.gz',
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is_folder=True,
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)
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@dataclasses.dataclass
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class AverageWordEmbeddingClassifierSpec:
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"""Specification for an average word embedding classifier model.
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Attributes:
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hparams: Configurable hyperparameters for training.
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model_options: Configurable options for the average word embedding model.
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name: The name of the object.
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"""
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# `learning_rate` is unused for the average word embedding model
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hparams: hp.AverageWordEmbeddingHParams = dataclasses.field(
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default_factory=lambda: hp.AverageWordEmbeddingHParams(
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epochs=10, batch_size=32, learning_rate=0
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)
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)
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model_options: mo.AverageWordEmbeddingModelOptions = dataclasses.field(
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default_factory=mo.AverageWordEmbeddingModelOptions
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)
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name: str = 'AverageWordEmbedding'
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average_word_embedding_classifier_spec = functools.partial(
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AverageWordEmbeddingClassifierSpec)
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@dataclasses.dataclass
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class BertClassifierSpec(bert_model_spec.BertModelSpec):
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"""Specification for a Bert classifier model.
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Only overrides the hparams attribute since the rest of the attributes are
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inherited from the BertModelSpec.
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"""
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hparams: hp.BertHParams = dataclasses.field(default_factory=hp.BertHParams)
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mobilebert_classifier_spec = functools.partial(
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BertClassifierSpec,
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downloaded_files=MOBILEBERT_TINY_FILES,
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hparams=hp.BertHParams(
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epochs=3, batch_size=48, learning_rate=3e-5, distribution_strategy='off'
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),
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name='MobileBert',
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)
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exbert_classifier_spec = functools.partial(
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BertClassifierSpec,
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downloaded_files=EXBERT_FILES,
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hparams=hp.BertHParams(
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epochs=3, batch_size=48, learning_rate=3e-5, distribution_strategy='off'
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),
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name='ExBert',
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)
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@enum.unique
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class SupportedModels(enum.Enum):
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"""Predefined text classifier model specs supported by Model Maker."""
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AVERAGE_WORD_EMBEDDING_CLASSIFIER = average_word_embedding_classifier_spec
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MOBILEBERT_CLASSIFIER = mobilebert_classifier_spec
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EXBERT_CLASSIFIER = exbert_classifier_spec
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