Internal Model Maker change.
PiperOrigin-RevId: 500758488
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@ -48,11 +48,12 @@ class Classifier(custom_model.CustomModel):
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self._hparams: hp.BaseHParams = None
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self._history: tf.keras.callbacks.History = None
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# TODO: Integrate this into all Model Maker tasks.
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# TODO: Integrate this into GestureRecognizer.
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def _train_model(self,
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train_data: classification_ds.ClassificationDataset,
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validation_data: classification_ds.ClassificationDataset,
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preprocessor: Optional[Callable[..., bool]] = None):
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preprocessor: Optional[Callable[..., bool]] = None,
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checkpoint_path: Optional[str] = None):
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"""Trains the classifier model.
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Compiles and fits the tf.keras `_model` and records the `_history`.
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@ -62,6 +63,9 @@ class Classifier(custom_model.CustomModel):
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validation_data: Validation data.
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preprocessor: An optional data preprocessor that can be used when
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generating a tf.data.Dataset.
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checkpoint_path: An optional directory for the checkpoint file to support
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continual training. If provided, loads model weights from the latest
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checkpoint in the directory.
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"""
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tf.compat.v1.logging.info('Training the models...')
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if len(train_data) < self._hparams.batch_size:
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@ -88,6 +92,14 @@ class Classifier(custom_model.CustomModel):
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optimizer=self._optimizer,
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loss=self._loss_function,
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metrics=[self._metric_function])
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latest_checkpoint = (
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tf.train.latest_checkpoint(checkpoint_path)
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if checkpoint_path else None)
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if latest_checkpoint:
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print(f'Resuming from {latest_checkpoint}')
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self._model.load_weights(latest_checkpoint)
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self._history = self._model.fit(
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x=train_dataset,
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epochs=self._hparams.epochs,
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@ -42,7 +42,9 @@ def get_default_callbacks(
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checkpoint_path = os.path.join(export_dir, 'checkpoint')
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checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
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checkpoint_path, save_weights_only=True)
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os.path.join(checkpoint_path, 'model-{epoch:04d}'),
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save_weights_only=True,
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period=5)
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return [summary_callback, checkpoint_callback]
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@ -87,15 +87,6 @@ py_library(
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],
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)
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py_library(
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name = "train_image_classifier_lib",
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srcs = ["train_image_classifier_lib.py"],
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deps = [
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":hyperparameters",
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"//mediapipe/model_maker/python/core/utils:model_util",
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],
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)
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py_library(
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name = "image_classifier",
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srcs = ["image_classifier.py"],
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@ -104,7 +95,6 @@ py_library(
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":image_classifier_options",
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":model_options",
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":model_spec",
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":train_image_classifier_lib",
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"//mediapipe/model_maker/python/core/data:classification_dataset",
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"//mediapipe/model_maker/python/core/tasks:classifier",
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"//mediapipe/model_maker/python/core/utils:model_util",
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@ -35,4 +35,3 @@ del image_classifier
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del image_classifier_options
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del model_options
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del model_spec
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del train_image_classifier_lib # pylint: disable=undefined-variable
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@ -28,7 +28,6 @@ from mediapipe.model_maker.python.vision.image_classifier import hyperparameters
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from mediapipe.model_maker.python.vision.image_classifier import image_classifier_options
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from mediapipe.model_maker.python.vision.image_classifier import model_options as model_opt
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from mediapipe.model_maker.python.vision.image_classifier import model_spec as ms
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from mediapipe.model_maker.python.vision.image_classifier import train_image_classifier_lib
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from mediapipe.tasks.python.metadata.metadata_writers import image_classifier as image_classifier_writer
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from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
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@ -57,6 +56,10 @@ class ImageClassifier(classifier.Classifier):
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mean_rgb=self._model_spec.mean_rgb,
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stddev_rgb=self._model_spec.stddev_rgb,
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use_augmentation=hparams.do_data_augmentation)
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self._callbacks = model_util.get_default_callbacks(self._hparams.export_dir)
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self._loss_function = tf.keras.losses.CategoricalCrossentropy(
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label_smoothing=self._hparams.label_smoothing)
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self._metric_function = 'accuracy'
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self._history = None # Training history returned from `keras_model.fit`.
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@classmethod
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@ -66,7 +69,7 @@ class ImageClassifier(classifier.Classifier):
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validation_data: classification_ds.ClassificationDataset,
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options: image_classifier_options.ImageClassifierOptions,
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) -> 'ImageClassifier':
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"""Creates and trains an image classifier.
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"""Creates and trains an ImageClassifier.
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Loads data and trains the model based on data for image classification. If a
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checkpoint file exists in the {options.hparams.export_dir}/checkpoint/
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@ -93,58 +96,29 @@ class ImageClassifier(classifier.Classifier):
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label_names=train_data.label_names,
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hparams=options.hparams,
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model_options=options.model_options)
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image_classifier._create_model()
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tf.compat.v1.logging.info('Training the models...')
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image_classifier._train(
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train_data=train_data, validation_data=validation_data)
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image_classifier._create_and_train_model(train_data, validation_data)
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return image_classifier
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# TODO: Migrate to the shared training library of Model Maker.
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def _train(self, train_data: classification_ds.ClassificationDataset,
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validation_data: classification_ds.ClassificationDataset):
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"""Trains the model with input train_data.
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The training results are recorded by a self._history object returned by
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tf.keras.Model.fit().
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def _create_and_train_model(
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self, train_data: classification_ds.ClassificationDataset,
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validation_data: classification_ds.ClassificationDataset):
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"""Creates and trains the model and optimizer.
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Args:
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train_data: Training data.
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validation_data: Validation data.
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"""
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tf.compat.v1.logging.info('Training the models...')
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hparams = self._hparams
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if len(train_data) < hparams.batch_size:
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raise ValueError('The size of the train_data (%d) couldn\'t be smaller '
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'than batch_size (%d). To solve this problem, set '
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'the batch_size smaller or increase the size of the '
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'train_data.' % (len(train_data), hparams.batch_size))
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train_dataset = train_data.gen_tf_dataset(
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batch_size=hparams.batch_size,
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is_training=True,
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shuffle=self._shuffle,
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preprocess=self._preprocess)
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hparams.steps_per_epoch = model_util.get_steps_per_epoch(
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steps_per_epoch=hparams.steps_per_epoch,
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batch_size=hparams.batch_size,
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self._create_model()
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self._hparams.steps_per_epoch = model_util.get_steps_per_epoch(
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steps_per_epoch=self._hparams.steps_per_epoch,
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batch_size=self._hparams.batch_size,
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train_data=train_data)
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train_dataset = train_dataset.take(count=hparams.steps_per_epoch)
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validation_dataset = validation_data.gen_tf_dataset(
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batch_size=hparams.batch_size,
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is_training=False,
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preprocess=self._preprocess)
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# Train the model.
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self._history = train_image_classifier_lib.train_model(
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model=self._model,
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hparams=hparams,
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train_ds=train_dataset,
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validation_ds=validation_dataset)
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self._optimizer = self._create_optimizer()
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self._train_model(
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train_data=train_data,
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validation_data=validation_data,
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preprocessor=self._preprocess,
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checkpoint_path=os.path.join(self._hparams.export_dir, 'checkpoint'))
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def _create_model(self):
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"""Creates the classifier model from TFHub pretrained models."""
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@ -198,3 +172,33 @@ class ImageClassifier(classifier.Classifier):
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model_util.save_tflite(tflite_model_with_metadata, tflite_file)
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with open(metadata_file, 'w') as f:
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f.write(metadata_json)
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def _create_optimizer(self) -> tf.keras.optimizers.Optimizer:
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"""Creates an optimizer with learning rate schedule.
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Uses Keras CosineDecay schedule for the learning rate by default.
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Returns:
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A tf.keras.optimizers.Optimizer for model training.
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"""
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# Learning rate is linear to batch size.
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init_lr = self._hparams.learning_rate * self._hparams.batch_size / 256
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# Get decay steps.
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total_training_steps = self._hparams.steps_per_epoch * self._hparams.epochs
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default_decay_steps = (
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self._hparams.decay_samples // self._hparams.batch_size)
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decay_steps = max(total_training_steps, default_decay_steps)
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learning_rate_fn = tf.keras.experimental.CosineDecay(
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initial_learning_rate=init_lr, decay_steps=decay_steps, alpha=0.0)
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warmup_steps = self._hparams.warmup_epochs * self._hparams.steps_per_epoch
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if warmup_steps:
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learning_rate_fn = model_util.WarmUp(
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initial_learning_rate=init_lr,
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decay_schedule_fn=learning_rate_fn,
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warmup_steps=warmup_steps)
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optimizer = tf.keras.optimizers.RMSprop(
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learning_rate=learning_rate_fn, rho=0.9, momentum=0.9, epsilon=0.001)
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return optimizer
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@ -1,102 +0,0 @@
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# 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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"""Library to train model."""
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import os
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import tensorflow as tf
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from mediapipe.model_maker.python.core.utils import model_util
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from mediapipe.model_maker.python.vision.image_classifier import hyperparameters as hp
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def _create_optimizer(init_lr: float, decay_steps: int,
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warmup_steps: int) -> tf.keras.optimizers.Optimizer:
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"""Creates an optimizer with learning rate schedule.
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Uses Keras CosineDecay schedule for the learning rate by default.
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Args:
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init_lr: Initial learning rate.
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decay_steps: Number of steps to decay over.
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warmup_steps: Number of steps to do warmup for.
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Returns:
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A tf.keras.optimizers.Optimizer for model training.
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"""
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learning_rate_fn = tf.keras.experimental.CosineDecay(
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initial_learning_rate=init_lr, decay_steps=decay_steps, alpha=0.0)
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if warmup_steps:
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learning_rate_fn = model_util.WarmUp(
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initial_learning_rate=init_lr,
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decay_schedule_fn=learning_rate_fn,
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warmup_steps=warmup_steps)
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optimizer = tf.keras.optimizers.RMSprop(
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learning_rate=learning_rate_fn, rho=0.9, momentum=0.9, epsilon=0.001)
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return optimizer
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def train_model(model: tf.keras.Model, hparams: hp.HParams,
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train_ds: tf.data.Dataset,
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validation_ds: tf.data.Dataset) -> tf.keras.callbacks.History:
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"""Trains model with the input data and hyperparameters.
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Args:
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model: Input tf.keras.Model.
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hparams: Hyperparameters for training image classifier.
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train_ds: tf.data.Dataset, training data to be fed in tf.keras.Model.fit().
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validation_ds: tf.data.Dataset, validation data to be fed in
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tf.keras.Model.fit().
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Returns:
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The tf.keras.callbacks.History object returned by tf.keras.Model.fit().
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"""
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# Learning rate is linear to batch size.
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learning_rate = hparams.learning_rate * hparams.batch_size / 256
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# Get decay steps.
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# NOMUTANTS--(b/256493858):Plan to test it in the unified training library.
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total_training_steps = hparams.steps_per_epoch * hparams.epochs
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default_decay_steps = hparams.decay_samples // hparams.batch_size
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decay_steps = max(total_training_steps, default_decay_steps)
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warmup_steps = hparams.warmup_epochs * hparams.steps_per_epoch
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optimizer = _create_optimizer(
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init_lr=learning_rate, decay_steps=decay_steps, warmup_steps=warmup_steps)
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loss = tf.keras.losses.CategoricalCrossentropy(
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label_smoothing=hparams.label_smoothing)
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model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
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summary_dir = os.path.join(hparams.export_dir, 'summaries')
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summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
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# Save checkpoint every 5 epochs.
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checkpoint_path = os.path.join(hparams.export_dir, 'checkpoint')
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checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
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os.path.join(checkpoint_path, 'model-{epoch:04d}'),
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save_weights_only=True,
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period=5)
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latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
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if latest_checkpoint:
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print(f'Resuming from {latest_checkpoint}')
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model.load_weights(latest_checkpoint)
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# Train the model.
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return model.fit(
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x=train_ds,
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epochs=hparams.epochs,
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validation_data=validation_ds,
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callbacks=[summary_callback, checkpoint_callback])
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