Adds a BertClassifier.

PiperOrigin-RevId: 487086744

mediapipe/model_maker/python/core/tasks/BUILD

@@ -45,7 +45,10 @@ py_library(
     srcs = ["classifier.py"],
     deps = [
         ":custom_model",
+        "//mediapipe/model_maker/python/core:hyperparameters",
+        "//mediapipe/model_maker/python/core/data:classification_dataset",
         "//mediapipe/model_maker/python/core/data:dataset",
+        "//mediapipe/model_maker/python/core/utils:model_util",
     ],
 )
 

mediapipe/model_maker/python/core/tasks/classifier.py

@@ -13,24 +13,24 @@
 # limitations under the License.
 """Custom classifier."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import os
-from typing import Any, List
+from typing import Any, Callable, Optional, Sequence, Union
 
 import tensorflow as tf
 
+from mediapipe.model_maker.python.core import hyperparameters as hp
+from mediapipe.model_maker.python.core.data import classification_dataset as classification_ds
 from mediapipe.model_maker.python.core.data import dataset
 from mediapipe.model_maker.python.core.tasks import custom_model
+from mediapipe.model_maker.python.core.utils import model_util
 
 
 class Classifier(custom_model.CustomModel):
   """An abstract base class that represents a TensorFlow classifier."""
 
-  def __init__(self, model_spec: Any, label_names: List[str], shuffle: bool):
+  def __init__(self, model_spec: Any, label_names: Sequence[str],
-    """Initilizes a classifier with its specifications.
+               shuffle: bool):
+    """Initializes a classifier with its specifications.
 
     Args:
         model_spec: Specification for the model.
@@ -40,6 +40,59 @@ class Classifier(custom_model.CustomModel):
     super(Classifier, self).__init__(model_spec, shuffle)
     self._label_names = label_names
     self._num_classes = len(label_names)
+    self._model: tf.keras.Model = None
+    self._optimizer: Union[str, tf.keras.optimizers.Optimizer] = None
+    self._loss_function: Union[str, tf.keras.losses.Loss] = None
+    self._metric_function: Union[str, tf.keras.metrics.Metric] = None
+    self._callbacks: Sequence[tf.keras.callbacks.Callback] = None
+    self._hparams: hp.BaseHParams = None
+    self._history: tf.keras.callbacks.History = None
+
+  # TODO: Integrate this into all Model Maker tasks.
+  def _train_model(self,
+                   train_data: classification_ds.ClassificationDataset,
+                   validation_data: classification_ds.ClassificationDataset,
+                   preprocessor: Optional[Callable[..., bool]] = None):
+    """Trains the classifier model.
+
+    Compiles and fits the tf.keras `_model` and records the `_history`.
+
+    Args:
+      train_data: Training data.
+      validation_data: Validation data.
+      preprocessor: An optional data preprocessor that can be used when
+        generating a tf.data.Dataset.
+    """
+    tf.compat.v1.logging.info('Training the models...')
+    if len(train_data) < self._hparams.batch_size:
+      raise ValueError(
+          f'The size of the train_data {len(train_data)} can\'t be smaller than'
+          f' batch_size {self._hparams.batch_size}. To solve this problem, set'
+          ' the batch_size smaller or increase the size of the train_data.')
+
+    train_dataset = train_data.gen_tf_dataset(
+        batch_size=self._hparams.batch_size,
+        is_training=True,
+        shuffle=self._shuffle,
+        preprocess=preprocessor)
+    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)
+    train_dataset = train_dataset.take(count=self._hparams.steps_per_epoch)
+    validation_dataset = validation_data.gen_tf_dataset(
+        batch_size=self._hparams.batch_size,
+        is_training=False,
+        preprocess=preprocessor)
+    self._model.compile(
+        optimizer=self._optimizer,
+        loss=self._loss_function,
+        metrics=[self._metric_function])
+    self._history = self._model.fit(
+        x=train_dataset,
+        epochs=self._hparams.epochs,
+        validation_data=validation_dataset,
+        callbacks=self._callbacks)
 
   def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
     """Evaluates the classifier with the provided evaluation dataset.

mediapipe/model_maker/python/vision/image_classifier/image_classifier.py

@@ -193,7 +193,7 @@ class ImageClassifier(classifier.Classifier):
         tflite_model,
         self._model_spec.mean_rgb,
         self._model_spec.stddev_rgb,
-        labels=metadata_writer.Labels().add(self._label_names))
+        labels=metadata_writer.Labels().add(list(self._label_names)))
     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: