Ensure that -std=c++14/17 is the first argument passed to Glog

PiperOrigin-RevId: 552509553
Updat WASM binaries for 0.10.3 release
2023-07-31 09:47:32 -07:00 · 2023-07-28 16:17:30 -07:00 · 2023-07-28 16:07:12 -07:00 · 2023-07-28 13:56:56 -07:00 · 2023-07-28 12:58:42 -07:00 · 2023-07-28 12:05:38 -07:00
57 changed files with 1499 additions and 248 deletions
@@ -157,19 +157,19 @@ http_archive(
 # 2020-08-21
 http_archive(
    name = "com_github_glog_glog",
-    strip_prefix = "glog-0.6.0",
-    sha256 = "8a83bf982f37bb70825df71a9709fa90ea9f4447fb3c099e1d720a439d88bad6",
+    strip_prefix = "glog-3a0d4d22c5ae0b9a2216988411cfa6bf860cc372",
+    sha256 = "170d08f80210b82d95563f4723a15095eff1aad1863000e8eeb569c96a98fefb",
    urls = [
-        "https://github.com/google/glog/archive/v0.6.0.tar.gz",
+        "https://github.com/google/glog/archive/3a0d4d22c5ae0b9a2216988411cfa6bf860cc372.zip",
    ],
 )
 http_archive(
    name = "com_github_glog_glog_no_gflags",
-    strip_prefix = "glog-0.6.0",
-    sha256 = "8a83bf982f37bb70825df71a9709fa90ea9f4447fb3c099e1d720a439d88bad6",
+    strip_prefix = "glog-3a0d4d22c5ae0b9a2216988411cfa6bf860cc372",
+    sha256 = "170d08f80210b82d95563f4723a15095eff1aad1863000e8eeb569c96a98fefb",
    build_file = "@//third_party:glog_no_gflags.BUILD",
    urls = [
-        "https://github.com/google/glog/archive/v0.6.0.tar.gz",
+        "https://github.com/google/glog/archive/3a0d4d22c5ae0b9a2216988411cfa6bf860cc372.zip",
    ],
    patches = [
        "@//third_party:com_github_glog_glog.diff",
@@ -406,8 +406,13 @@ cc_library(
    alwayslink = 1,
 )

-# This dependency removed tensorflow_jellyfish_deps and xprofilez_with_server because they failed
-# Boq conformance test. Weigh your use case to see if this will work for you.
+# This dependency removed the following 3 targets because they failed Boq conformance test:
+#
+# tensorflow_jellyfish_deps
+# jfprof_lib
+# xprofilez_with_server
+#
+# If you need them plz consider tensorflow_inference_calculator_no_envelope_loader.
 cc_library(
    name = "tensorflow_inference_calculator_for_boq",
    srcs = ["tensorflow_inference_calculator.cc"],
@@ -24,6 +24,7 @@ package_group(
 package_group(
    name = "1p_client",
    packages = [
+        "//cloud/ml/applications/vision/model_garden/model_oss/mediapipe/...",
        "//research/privacy/learning/fl_eval/pcvr/...",
    ],
 )
@@ -45,6 +45,8 @@ class TFRecordCacheFiles:
  num_shards: int = 1

  def __post_init__(self):
+    if not tf.io.gfile.exists(self.cache_dir):
+      tf.io.gfile.makedirs(self.cache_dir)
    if not self.cache_prefix_filename:
      raise ValueError('cache_prefix_filename cannot be empty.')
    if self.num_shards <= 0:
@@ -79,8 +81,6 @@ class TFRecordCacheFiles:
    Returns:
      Array of TFRecordWriter objects
    """
-    if not tf.io.gfile.exists(self.cache_dir):
-      tf.io.gfile.makedirs(self.cache_dir)
    return [tf.io.TFRecordWriter(path) for path in self.tfrecord_files]

  def save_metadata(self, metadata):
@@ -15,7 +15,7 @@

 import dataclasses
 import tempfile
-from typing import Optional
+from typing import Mapping, Optional

 import tensorflow as tf

@@ -36,6 +36,8 @@ class BaseHParams:
    steps_per_epoch: An optional integer indicate the number of training steps
      per epoch. If not set, the training pipeline calculates the default steps
      per epoch as the training dataset size divided by batch size.
+    class_weights: An optional mapping of indices to weights for weighting the
+      loss function during training.
    shuffle: True if the dataset is shuffled before training.
    export_dir: The location of the model checkpoint files.
    distribution_strategy: A string specifying which Distribution Strategy to
@@ -57,6 +59,7 @@ class BaseHParams:
  batch_size: int
  epochs: int
  steps_per_epoch: Optional[int] = None
+  class_weights: Optional[Mapping[int, float]] = None

  # Dataset-related parameters
  shuffle: bool = False
@@ -110,7 +110,9 @@ class Classifier(custom_model.CustomModel):
        # dataset is exhausted even if there are epochs remaining.
        steps_per_epoch=None,
        validation_data=validation_dataset,
-        callbacks=self._callbacks)
+        callbacks=self._callbacks,
+        class_weight=self._hparams.class_weights,
+    )

  def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
    """Evaluates the classifier with the provided evaluation dataset.
@@ -59,7 +59,7 @@ class FocalLoss(tf.keras.losses.Loss):
  """

  def __init__(self, gamma, class_weight: Optional[Sequence[float]] = None):
-    """Constructor.
+    """Initializes FocalLoss.

    Args:
      gamma: Focal loss gamma, as described in class docs.
@@ -115,6 +115,51 @@ class FocalLoss(tf.keras.losses.Loss):
    return tf.reduce_sum(losses) / batch_size


+class SparseFocalLoss(FocalLoss):
+  """Sparse implementation of Focal Loss.
+
+  This is the same as FocalLoss, except the labels are expected to be class ids
+  instead of 1-hot encoded vectors. See FocalLoss class documentation defined
+  in this same file for more details.
+
+  Example usage:
+  >>> y_true = [1, 2]
+  >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
+  >>> gamma = 2
+  >>> focal_loss = SparseFocalLoss(gamma, 3)
+  >>> focal_loss(y_true, y_pred).numpy()
+  0.9326
+
+  >>> # Calling with 'sample_weight'.
+  >>> focal_loss(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
+  0.6528
+  """
+
+  def __init__(
+      self, gamma, num_classes, class_weight: Optional[Sequence[float]] = None
+  ):
+    """Initializes SparseFocalLoss.
+
+    Args:
+      gamma: Focal loss gamma, as described in class docs.
+      num_classes: Number of classes.
+      class_weight: A weight to apply to the loss, one for each class. The
+        weight is applied for each input where the ground truth label matches.
+    """
+    super().__init__(gamma, class_weight=class_weight)
+    self._num_classes = num_classes
+
+  def __call__(
+      self,
+      y_true: tf.Tensor,
+      y_pred: tf.Tensor,
+      sample_weight: Optional[tf.Tensor] = None,
+  ) -> tf.Tensor:
+    y_true = tf.cast(tf.reshape(y_true, [-1]), tf.int32)
+    y_true_one_hot = tf.one_hot(y_true, self._num_classes)
+    return super().__call__(y_true_one_hot, y_pred, sample_weight=sample_weight)
+
+
@dataclasses.dataclass
 class PerceptualLossWeight:
  """The weight for each perceptual loss.
@@ -101,6 +101,23 @@ class FocalLossTest(tf.test.TestCase, parameterized.TestCase):
    self.assertNear(loss, expected_loss, 1e-4)


+class SparseFocalLossTest(tf.test.TestCase):
+
+  def test_sparse_focal_loss_matches_focal_loss(self):
+    num_classes = 2
+    y_pred = tf.constant([[0.8, 0.2], [0.3, 0.7]])
+    y_true = tf.constant([1, 0])
+    y_true_one_hot = tf.one_hot(y_true, num_classes)
+    for gamma in [0.0, 0.5, 1.0]:
+      expected_loss_fn = loss_functions.FocalLoss(gamma=gamma)
+      loss_fn = loss_functions.SparseFocalLoss(
+          gamma=gamma, num_classes=num_classes
+      )
+      expected_loss = expected_loss_fn(y_true_one_hot, y_pred)
+      loss = loss_fn(y_true, y_pred)
+      self.assertNear(loss, expected_loss, 1e-4)
+
+
 class MockPerceptualLoss(loss_functions.PerceptualLoss):
  """A mock class with implementation of abstract methods for testing."""

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# Placeholder for internal Python strict library and test compatibility macro.
+# Placeholder for internal Python strict binary and library compatibility macro.
 # Placeholder for internal Python strict test compatibility macro.

 package(default_visibility = ["//mediapipe:__subpackages__"])
@@ -76,7 +76,10 @@ py_test(
 py_library(
    name = "dataset",
    srcs = ["dataset.py"],
-    deps = ["//mediapipe/model_maker/python/core/data:classification_dataset"],
+    deps = [
+        "//mediapipe/model_maker/python/core/data:cache_files",
+        "//mediapipe/model_maker/python/core/data:classification_dataset",
+    ],
 )

 py_test(
@@ -88,7 +91,10 @@ py_test(
 py_library(
    name = "preprocessor",
    srcs = ["preprocessor.py"],
-    deps = [":dataset"],
+    deps = [
+        ":dataset",
+        "//mediapipe/model_maker/python/core/data:cache_files",
+    ],
 )

 py_test(
@@ -99,6 +105,7 @@ py_test(
        ":dataset",
        ":model_spec",
        ":preprocessor",
+        "//mediapipe/model_maker/python/core/data:cache_files",
    ],
 )

@@ -124,6 +131,7 @@ py_library(
        ":text_classifier_options",
        "//mediapipe/model_maker/python/core/data:dataset",
        "//mediapipe/model_maker/python/core/tasks:classifier",
+        "//mediapipe/model_maker/python/core/utils:loss_functions",
        "//mediapipe/model_maker/python/core/utils:metrics",
        "//mediapipe/model_maker/python/core/utils:model_util",
        "//mediapipe/model_maker/python/core/utils:quantization",
@@ -147,6 +155,7 @@ py_test(
    ],
    deps = [
        ":text_classifier_import",
+        "//mediapipe/model_maker/python/core/utils:loss_functions",
        "//mediapipe/tasks/python/test:test_utils",
    ],
 )
@@ -15,11 +15,15 @@

 import csv
 import dataclasses
+import hashlib
+import os
 import random
+import tempfile
+from typing import List, Optional, Sequence

-from typing import Optional, Sequence
 import tensorflow as tf

+from mediapipe.model_maker.python.core.data import cache_files as cache_files_lib
 from mediapipe.model_maker.python.core.data import classification_dataset


@@ -46,21 +50,49 @@ class CSVParameters:
 class Dataset(classification_dataset.ClassificationDataset):
  """Dataset library for text classifier."""

+  def __init__(
+      self,
+      dataset: tf.data.Dataset,
+      label_names: List[str],
+      tfrecord_cache_files: Optional[cache_files_lib.TFRecordCacheFiles] = None,
+      size: Optional[int] = None,
+  ):
+    super().__init__(dataset, label_names, size)
+    if not tfrecord_cache_files:
+      tfrecord_cache_files = cache_files_lib.TFRecordCacheFiles(
+          cache_prefix_filename="tfrecord", num_shards=1
+      )
+    self.tfrecord_cache_files = tfrecord_cache_files
+
  @classmethod
-  def from_csv(cls,
-               filename: str,
-               csv_params: CSVParameters,
-               shuffle: bool = True) -> "Dataset":
+  def from_csv(
+      cls,
+      filename: str,
+      csv_params: CSVParameters,
+      shuffle: bool = True,
+      cache_dir: Optional[str] = None,
+      num_shards: int = 1,
+  ) -> "Dataset":
    """Loads text with labels from a CSV file.

    Args:
      filename: Name of the CSV file.
      csv_params: Parameters used for reading the CSV file.
      shuffle: If True, randomly shuffle the data.
+      cache_dir: Optional parameter to specify where to store the preprocessed
+        dataset. Only used for BERT models.
+      num_shards: Optional parameter for num shards of the preprocessed dataset.
+        Note that using more than 1 shard will reorder the dataset. Only used
+        for BERT models.

    Returns:
      Dataset containing (text, label) pairs and other related info.
    """
+    if cache_dir is None:
+      cache_dir = tempfile.mkdtemp()
+    # calculate hash for cache based off of files
+    hasher = hashlib.md5()
+    hasher.update(os.path.basename(filename).encode("utf-8"))
    with tf.io.gfile.GFile(filename, "r") as f:
      reader = csv.DictReader(
          f,
@@ -69,6 +101,9 @@ class Dataset(classification_dataset.ClassificationDataset):
          quotechar=csv_params.quotechar)

    lines = list(reader)
+    for line in lines:
+      hasher.update(str(line).encode("utf-8"))
+
    if shuffle:
      random.shuffle(lines)

@@ -81,9 +116,18 @@ class Dataset(classification_dataset.ClassificationDataset):
        index_by_label[line[csv_params.label_column]] for line in lines
    ]
    label_index_ds = tf.data.Dataset.from_tensor_slices(
-        tf.cast(label_indices, tf.int64))
+        tf.cast(label_indices, tf.int64)
+    )
    text_label_ds = tf.data.Dataset.zip((text_ds, label_index_ds))

+    hasher.update(str(num_shards).encode("utf-8"))
+    cache_prefix_filename = hasher.hexdigest()
+    tfrecord_cache_files = cache_files_lib.TFRecordCacheFiles(
+        cache_prefix_filename, cache_dir, num_shards
+    )
    return Dataset(
-        dataset=text_label_ds, label_names=label_names, size=len(texts)
+        dataset=text_label_ds,
+        label_names=label_names,
+        tfrecord_cache_files=tfrecord_cache_files,
+        size=len(texts),
    )
@@ -53,7 +53,7 @@ class DatasetTest(tf.test.TestCase):

  def test_split(self):
    ds = tf.data.Dataset.from_tensor_slices(['good', 'bad', 'neutral', 'odd'])
-    data = dataset.Dataset(ds, ['pos', 'neg'], 4)
+    data = dataset.Dataset(ds, ['pos', 'neg'], size=4)
    train_data, test_data = data.split(0.5)
    expected_train_data = [b'good', b'bad']
    expected_test_data = [b'neutral', b'odd']
@@ -15,7 +15,7 @@

 import dataclasses
 import enum
-from typing import Union
+from typing import Sequence, Union

 from mediapipe.model_maker.python.core import hyperparameters as hp

@@ -39,16 +39,34 @@ class BertHParams(hp.BaseHParams):

  Attributes:
    learning_rate: Learning rate to use for gradient descent training.
-    batch_size: Batch size for training.
-    epochs: Number of training iterations over the dataset.
-    optimizer: Optimizer to use for training. Only supported values are "adamw"
-      and "lamb".
+    end_learning_rate: End learning rate for linear decay. Defaults to 0.
+    batch_size: Batch size for training. Defaults to 48.
+    epochs: Number of training iterations over the dataset. Defaults to 2.
+    optimizer: Optimizer to use for training. Supported values are defined in
+      BertOptimizer enum: ADAMW and LAMB.
+    weight_decay: Weight decay of the optimizer. Defaults to 0.01.
+    desired_precisions: If specified, adds a RecallAtPrecision metric per
+      desired_precisions[i] entry which tracks the recall given the constraint
+      on precision. Only supported for binary classification.
+    desired_recalls: If specified, adds a PrecisionAtRecall metric per
+      desired_recalls[i] entry which tracks the precision given the constraint
+      on recall. Only supported for binary classification.
+    gamma: Gamma parameter for focal loss. To use cross entropy loss, set this
+      value to 0. Defaults to 2.0.
  """

  learning_rate: float = 3e-5
+  end_learning_rate: float = 0.0
+
  batch_size: int = 48
  epochs: int = 2
  optimizer: BertOptimizer = BertOptimizer.ADAMW
+  weight_decay: float = 0.01
+
+  desired_precisions: Sequence[float] = dataclasses.field(default_factory=list)
+  desired_recalls: Sequence[float] = dataclasses.field(default_factory=list)
+
+  gamma: float = 2.0


 HParams = Union[BertHParams, AverageWordEmbeddingHParams]
@@ -79,11 +79,6 @@ mobilebert_classifier_spec = functools.partial(
        epochs=3, batch_size=48, learning_rate=3e-5, distribution_strategy='off'
    ),
    name='MobileBert',
-    tflite_input_name={
-        'ids': 'serving_default_input_1:0',
-        'segment_ids': 'serving_default_input_2:0',
-        'mask': 'serving_default_input_3:0',
-    },
 )

 exbert_classifier_spec = functools.partial(
@@ -93,11 +88,6 @@ exbert_classifier_spec = functools.partial(
        epochs=3, batch_size=48, learning_rate=3e-5, distribution_strategy='off'
    ),
    name='ExBert',
-    tflite_input_name={
-        'ids': 'serving_default_input_1:0',
-        'segment_ids': 'serving_default_input_2:0',
-        'mask': 'serving_default_input_3:0',
-    },
 )


@@ -46,11 +46,13 @@ class ModelSpecTest(tf.test.TestCase):
    self.assertTrue(os.path.exists(model_spec_obj.downloaded_files.get_path()))
    self.assertTrue(model_spec_obj.do_lower_case)
    self.assertEqual(
-        model_spec_obj.tflite_input_name, {
-            'ids': 'serving_default_input_1:0',
-            'mask': 'serving_default_input_3:0',
-            'segment_ids': 'serving_default_input_2:0'
-        })
+        model_spec_obj.tflite_input_name,
+        {
+            'ids': 'serving_default_input_word_ids:0',
+            'mask': 'serving_default_input_mask:0',
+            'segment_ids': 'serving_default_input_type_ids:0',
+        },
+    )
    self.assertEqual(
        model_spec_obj.model_options,
        classifier_model_options.BertModelOptions(
@@ -15,14 +15,15 @@
 """Preprocessors for text classification."""

 import collections
+import hashlib
 import os
 import re
-import tempfile
 from typing import Mapping, Sequence, Tuple, Union

 import tensorflow as tf
 import tensorflow_hub

+from mediapipe.model_maker.python.core.data import cache_files as cache_files_lib
 from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds
 from official.nlp.data import classifier_data_lib
 from official.nlp.tools import tokenization
@@ -75,19 +76,20 @@ def _decode_record(
  return bert_features, example["label_ids"]


-def _single_file_dataset(
-    input_file: str, name_to_features: Mapping[str, tf.io.FixedLenFeature]
+def _tfrecord_dataset(
+    tfrecord_files: Sequence[str],
+    name_to_features: Mapping[str, tf.io.FixedLenFeature],
 ) -> tf.data.TFRecordDataset:
  """Creates a single-file dataset to be passed for BERT custom training.

  Args:
-    input_file: Filepath for the dataset.
+    tfrecord_files: Filepaths for the dataset.
    name_to_features: Maps record keys to feature types.

  Returns:
    Dataset containing BERT model input features and labels.
  """
-  d = tf.data.TFRecordDataset(input_file)
+  d = tf.data.TFRecordDataset(tfrecord_files)
  d = d.map(
      lambda record: _decode_record(record, name_to_features),
      num_parallel_calls=tf.data.AUTOTUNE)
@@ -221,15 +223,23 @@ class BertClassifierPreprocessor:
    seq_len: Length of the input sequence to the model.
    vocab_file: File containing the BERT vocab.
    tokenizer: BERT tokenizer.
+    model_name: Name of the model provided by the model_spec. Used to associate
+      cached files with specific Bert model vocab.
  """

-  def __init__(self, seq_len: int, do_lower_case: bool, uri: str):
+  def __init__(
+      self, seq_len: int, do_lower_case: bool, uri: str, model_name: str
+  ):
    self._seq_len = seq_len
    # Vocab filepath is tied to the BERT module's URI.
    self._vocab_file = os.path.join(
-        tensorflow_hub.resolve(uri), "assets", "vocab.txt")
-    self._tokenizer = tokenization.FullTokenizer(self._vocab_file,
-                                                 do_lower_case)
+        tensorflow_hub.resolve(uri), "assets", "vocab.txt"
+    )
+    self._do_lower_case = do_lower_case
+    self._tokenizer = tokenization.FullTokenizer(
+        self._vocab_file, self._do_lower_case
+    )
+    self._model_name = model_name

  def _get_name_to_features(self):
    """Gets the dictionary mapping record keys to feature types."""
@@ -244,8 +254,45 @@ class BertClassifierPreprocessor:
    """Returns the vocab file of the BertClassifierPreprocessor."""
    return self._vocab_file

+  def _get_tfrecord_cache_files(
+      self, ds_cache_files
+  ) -> cache_files_lib.TFRecordCacheFiles:
+    """Helper to regenerate cache prefix filename using preprocessor info.
+
+    We need to update the dataset cache_prefix cache because the actual cached
+    dataset depends on the preprocessor parameters such as model_name, seq_len,
+    and do_lower_case in addition to the raw dataset parameters which is already
+    included in the ds_cache_files.cache_prefix_filename
+
+    Specifically, the new cache_prefix_filename used by the preprocessor will
+    be a hash generated from the following:
+      1. cache_prefix_filename of the initial raw dataset
+      2. model_name
+      3. seq_len
+      4. do_lower_case
+
+    Args:
+      ds_cache_files: TFRecordCacheFiles from the original raw dataset object
+
+    Returns:
+      A new TFRecordCacheFiles object which incorporates the preprocessor
+      parameters.
+    """
+    hasher = hashlib.md5()
+    hasher.update(ds_cache_files.cache_prefix_filename.encode("utf-8"))
+    hasher.update(self._model_name.encode("utf-8"))
+    hasher.update(str(self._seq_len).encode("utf-8"))
+    hasher.update(str(self._do_lower_case).encode("utf-8"))
+    cache_prefix_filename = hasher.hexdigest()
+    return cache_files_lib.TFRecordCacheFiles(
+        cache_prefix_filename,
+        ds_cache_files.cache_dir,
+        ds_cache_files.num_shards,
+    )
+
  def preprocess(
-      self, dataset: text_classifier_ds.Dataset) -> text_classifier_ds.Dataset:
+      self, dataset: text_classifier_ds.Dataset
+  ) -> text_classifier_ds.Dataset:
    """Preprocesses data into input for a BERT-based classifier.

    Args:
@@ -254,32 +301,65 @@ class BertClassifierPreprocessor:
    Returns:
      Dataset containing (bert_features, label) data.
    """
-    examples = []
-    for index, (text, label) in enumerate(dataset.gen_tf_dataset()):
-      _validate_text_and_label(text, label)
-      examples.append(
-          classifier_data_lib.InputExample(
-              guid=str(index),
-              text_a=text.numpy()[0].decode("utf-8"),
-              text_b=None,
-              # InputExample expects the label name rather than the int ID
-              label=dataset.label_names[label.numpy()[0]]))
+    ds_cache_files = dataset.tfrecord_cache_files
+    # Get new tfrecord_cache_files by including preprocessor information.
+    tfrecord_cache_files = self._get_tfrecord_cache_files(ds_cache_files)
+    if not tfrecord_cache_files.is_cached():
+      print(f"Writing new cache files to {tfrecord_cache_files.cache_prefix}")
+      writers = tfrecord_cache_files.get_writers()
+      size = 0
+      for index, (text, label) in enumerate(dataset.gen_tf_dataset()):
+        _validate_text_and_label(text, label)
+        example = classifier_data_lib.InputExample(
+            guid=str(index),
+            text_a=text.numpy()[0].decode("utf-8"),
+            text_b=None,
+            # InputExample expects the label name rather than the int ID
+            # label=dataset.label_names[label.numpy()[0]])
+            label=label.numpy()[0],
+        )
+        feature = classifier_data_lib.convert_single_example(
+            index, example, None, self._seq_len, self._tokenizer
+        )

-    tfrecord_file = os.path.join(tempfile.mkdtemp(), "bert_features.tfrecord")
-    classifier_data_lib.file_based_convert_examples_to_features(
-        examples=examples,
-        label_list=dataset.label_names,
-        max_seq_length=self._seq_len,
-        tokenizer=self._tokenizer,
-        output_file=tfrecord_file)
-    preprocessed_ds = _single_file_dataset(tfrecord_file,
-                                           self._get_name_to_features())
+        def create_int_feature(values):
+          f = tf.train.Feature(
+              int64_list=tf.train.Int64List(value=list(values))
+          )
+          return f
+
+        features = collections.OrderedDict()
+        features["input_ids"] = create_int_feature(feature.input_ids)
+        features["input_mask"] = create_int_feature(feature.input_mask)
+        features["segment_ids"] = create_int_feature(feature.segment_ids)
+        features["label_ids"] = create_int_feature([feature.label_id])
+        tf_example = tf.train.Example(
+            features=tf.train.Features(feature=features)
+        )
+        writers[index % len(writers)].write(tf_example.SerializeToString())
+        size = index + 1
+      for writer in writers:
+        writer.close()
+      metadata = {"size": size, "label_names": dataset.label_names}
+      tfrecord_cache_files.save_metadata(metadata)
+    else:
+      print(
+          f"Using existing cache files at {tfrecord_cache_files.cache_prefix}"
+      )
+      metadata = tfrecord_cache_files.load_metadata()
+    size = metadata["size"]
+    label_names = metadata["label_names"]
+    preprocessed_ds = _tfrecord_dataset(
+        tfrecord_cache_files.tfrecord_files, self._get_name_to_features()
+    )
    return text_classifier_ds.Dataset(
        dataset=preprocessed_ds,
-        size=dataset.size,
-        label_names=dataset.label_names)
+        size=size,
+        label_names=label_names,
+        tfrecord_cache_files=tfrecord_cache_files,
+    )


-TextClassifierPreprocessor = (
-    Union[BertClassifierPreprocessor,
-          AverageWordEmbeddingClassifierPreprocessor])
+TextClassifierPreprocessor = Union[
+    BertClassifierPreprocessor, AverageWordEmbeddingClassifierPreprocessor
+]
@@ -13,14 +13,17 @@
 # limitations under the License.

 import csv
+import io
 import os
 import tempfile
 from unittest import mock as unittest_mock

+import mock
 import numpy as np
 import numpy.testing as npt
 import tensorflow as tf

+from mediapipe.model_maker.python.core.data import cache_files
 from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds
 from mediapipe.model_maker.python.text.text_classifier import model_spec
 from mediapipe.model_maker.python.text.text_classifier import preprocessor
@@ -84,11 +87,12 @@ class PreprocessorTest(tf.test.TestCase):
    csv_file = self._get_csv_file()
    dataset = text_classifier_ds.Dataset.from_csv(
        filename=csv_file, csv_params=self.CSV_PARAMS_)
-    bert_spec = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value()
+    bert_spec = model_spec.SupportedModels.EXBERT_CLASSIFIER.value()
    bert_preprocessor = preprocessor.BertClassifierPreprocessor(
        seq_len=5,
        do_lower_case=bert_spec.do_lower_case,
        uri=bert_spec.downloaded_files.get_path(),
+        model_name=bert_spec.name,
    )
    preprocessed_dataset = bert_preprocessor.preprocess(dataset)
    labels = []
@@ -97,18 +101,91 @@ class PreprocessorTest(tf.test.TestCase):
      self.assertEqual(label.shape, [1])
      labels.append(label.numpy()[0])
      self.assertSameElements(
-          features.keys(), ['input_word_ids', 'input_mask', 'input_type_ids'])
+          features.keys(), ['input_word_ids', 'input_mask', 'input_type_ids']
+      )
      for feature in features.values():
        self.assertEqual(feature.shape, [1, 5])
      input_masks.append(features['input_mask'].numpy()[0])
-      npt.assert_array_equal(features['input_type_ids'].numpy()[0],
-                             [0, 0, 0, 0, 0])
+      npt.assert_array_equal(
+          features['input_type_ids'].numpy()[0], [0, 0, 0, 0, 0]
+      )
    npt.assert_array_equal(
-        np.stack(input_masks), np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]]))
+        np.stack(input_masks), np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 0]])
+    )
    self.assertEqual(labels, [1, 0])

+  def test_bert_preprocessor_cache(self):
+    csv_file = self._get_csv_file()
+    dataset = text_classifier_ds.Dataset.from_csv(
+        filename=csv_file,
+        csv_params=self.CSV_PARAMS_,
+        cache_dir=self.get_temp_dir(),
+    )
+    bert_spec = model_spec.SupportedModels.EXBERT_CLASSIFIER.value()
+    bert_preprocessor = preprocessor.BertClassifierPreprocessor(
+        seq_len=5,
+        do_lower_case=bert_spec.do_lower_case,
+        uri=bert_spec.downloaded_files.get_path(),
+        model_name=bert_spec.name,
+    )
+    ds_cache_files = dataset.tfrecord_cache_files
+    preprocessed_cache_files = bert_preprocessor._get_tfrecord_cache_files(
+        ds_cache_files
+    )
+    self.assertFalse(preprocessed_cache_files.is_cached())
+    preprocessed_dataset = bert_preprocessor.preprocess(dataset)
+    self.assertTrue(preprocessed_cache_files.is_cached())
+    self.assertEqual(
+        preprocessed_dataset.tfrecord_cache_files, preprocessed_cache_files
+    )
+
+    # The second time running preprocessor, it should load from cache directly
+    mock_stdout = io.StringIO()
+    with mock.patch('sys.stdout', mock_stdout):
+      _ = bert_preprocessor.preprocess(dataset)
+    self.assertEqual(
+        mock_stdout.getvalue(),
+        'Using existing cache files at'
+        f' {preprocessed_cache_files.cache_prefix}\n',
+    )
+
+  def _get_new_prefix(self, cf, bert_spec, seq_len, do_lower_case):
+    bert_preprocessor = preprocessor.BertClassifierPreprocessor(
+        seq_len=seq_len,
+        do_lower_case=do_lower_case,
+        uri=bert_spec.downloaded_files.get_path(),
+        model_name=bert_spec.name,
+    )
+    new_cf = bert_preprocessor._get_tfrecord_cache_files(cf)
+    return new_cf.cache_prefix_filename
+
+  def test_bert_get_tfrecord_cache_files(self):
+    # Test to ensure regenerated cache_files have different prefixes
+    all_cf_prefixes = set()
+    cf = cache_files.TFRecordCacheFiles(
+        cache_prefix_filename='cache_prefix',
+        cache_dir=self.get_temp_dir(),
+        num_shards=1,
+    )
+    exbert_spec = model_spec.SupportedModels.EXBERT_CLASSIFIER.value()
+    all_cf_prefixes.add(self._get_new_prefix(cf, exbert_spec, 5, True))
+    all_cf_prefixes.add(self._get_new_prefix(cf, exbert_spec, 10, True))
+    all_cf_prefixes.add(self._get_new_prefix(cf, exbert_spec, 5, False))
+    mobilebert_spec = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value()
+    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 5, True))
+    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 10, True))
+    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 5, False))
+    new_cf = cache_files.TFRecordCacheFiles(
+        cache_prefix_filename='new_cache_prefix',
+        cache_dir=self.get_temp_dir(),
+        num_shards=1,
+    )
+    all_cf_prefixes.add(self._get_new_prefix(new_cf, exbert_spec, 5, True))
+
+    # Each item of all_cf_prefixes should be unique, so 7 total.
+    self.assertLen(all_cf_prefixes, 7)
+

 if __name__ == '__main__':
  # Load compressed models from tensorflow_hub
-  os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
  tf.test.main()
@@ -16,8 +16,8 @@
          }
        },
        {
-          "name": "mask",
-          "description": "Mask with 1 for real tokens and 0 for padding tokens.",
+          "name": "segment_ids",
+          "description": "0 for the first sequence, 1 for the second sequence if exists.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
@@ -27,8 +27,8 @@
          }
        },
        {
-          "name": "segment_ids",
-          "description": "0 for the first sequence, 1 for the second sequence if exists.",
+          "name": "mask",
+          "description": "Mask with 1 for real tokens and 0 for padding tokens.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
@@ -24,6 +24,7 @@ import tensorflow_hub as hub

 from mediapipe.model_maker.python.core.data import dataset as ds
 from mediapipe.model_maker.python.core.tasks import classifier
+from mediapipe.model_maker.python.core.utils import loss_functions
 from mediapipe.model_maker.python.core.utils import metrics
 from mediapipe.model_maker.python.core.utils import model_util
 from mediapipe.model_maker.python.core.utils import quantization
@@ -116,17 +117,14 @@ class TextClassifier(classifier.Classifier):
        options.supported_model == ms.SupportedModels.MOBILEBERT_CLASSIFIER
        or options.supported_model == ms.SupportedModels.EXBERT_CLASSIFIER
    ):
-      text_classifier = (
-          _BertClassifier.create_bert_classifier(train_data, validation_data,
-                                                 options,
-                                                 train_data.label_names))
+      text_classifier = _BertClassifier.create_bert_classifier(
+          train_data, validation_data, options
+      )
    elif (options.supported_model ==
          ms.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER):
-      text_classifier = (
-          _AverageWordEmbeddingClassifier
-          .create_average_word_embedding_classifier(train_data, validation_data,
-                                                    options,
-                                                    train_data.label_names))
+      text_classifier = _AverageWordEmbeddingClassifier.create_average_word_embedding_classifier(
+          train_data, validation_data, options
+      )
    else:
      raise ValueError(f"Unknown model {options.supported_model}")

@@ -166,28 +164,8 @@ class TextClassifier(classifier.Classifier):
    processed_data = self._text_preprocessor.preprocess(data)
    dataset = processed_data.gen_tf_dataset(batch_size, is_training=False)

-    additional_metrics = []
-    if desired_precisions and len(data.label_names) == 2:
-      for precision in desired_precisions:
-        additional_metrics.append(
-            metrics.BinarySparseRecallAtPrecision(
-                precision, name=f"recall_at_precision_{precision}"
-            )
-        )
-    if desired_recalls and len(data.label_names) == 2:
-      for recall in desired_recalls:
-        additional_metrics.append(
-            metrics.BinarySparsePrecisionAtRecall(
-                recall, name=f"precision_at_recall_{recall}"
-            )
-        )
-    metric_functions = self._metric_functions + additional_metrics
-    self._model.compile(
-        optimizer=self._optimizer,
-        loss=self._loss_function,
-        metrics=metric_functions,
-    )
-    return self._model.evaluate(dataset)
+    with self._hparams.get_strategy().scope():
+      return self._model.evaluate(dataset)

  def export_model(
      self,
@@ -255,16 +233,17 @@ class _AverageWordEmbeddingClassifier(TextClassifier):

  @classmethod
  def create_average_word_embedding_classifier(
-      cls, train_data: text_ds.Dataset, validation_data: text_ds.Dataset,
+      cls,
+      train_data: text_ds.Dataset,
+      validation_data: text_ds.Dataset,
      options: text_classifier_options.TextClassifierOptions,
-      label_names: Sequence[str]) -> "_AverageWordEmbeddingClassifier":
+  ) -> "_AverageWordEmbeddingClassifier":
    """Creates, trains, and returns an Average Word Embedding classifier.

    Args:
      train_data: Training data.
      validation_data: Validation data.
      options: Options for creating and training the text classifier.
-      label_names: Label names used in the data.

    Returns:
      An Average Word Embedding classifier.
@@ -370,28 +349,25 @@ class _BertClassifier(TextClassifier):
    self._callbacks = model_util.get_default_callbacks(self._hparams.export_dir)
    self._model_options = model_options
    with self._hparams.get_strategy().scope():
-      self._loss_function = tf.keras.losses.SparseCategoricalCrossentropy()
-      self._metric_functions = [
-          tf.keras.metrics.SparseCategoricalAccuracy(
-              "test_accuracy", dtype=tf.float32
-          ),
-          metrics.SparsePrecision(name="precision", dtype=tf.float32),
-          metrics.SparseRecall(name="recall", dtype=tf.float32),
-      ]
-    self._text_preprocessor: preprocessor.BertClassifierPreprocessor = None
+      self._loss_function = loss_functions.SparseFocalLoss(
+          self._hparams.gamma, self._num_classes
+      )
+      self._metric_functions = self._create_metrics()
+      self._text_preprocessor: preprocessor.BertClassifierPreprocessor = None

  @classmethod
  def create_bert_classifier(
-      cls, train_data: text_ds.Dataset, validation_data: text_ds.Dataset,
+      cls,
+      train_data: text_ds.Dataset,
+      validation_data: text_ds.Dataset,
      options: text_classifier_options.TextClassifierOptions,
-      label_names: Sequence[str]) -> "_BertClassifier":
+  ) -> "_BertClassifier":
    """Creates, trains, and returns a BERT-based classifier.

    Args:
      train_data: Training data.
      validation_data: Validation data.
      options: Options for creating and training the text classifier.
-      label_names: Label names used in the data.

    Returns:
      A BERT-based classifier.
@@ -435,9 +411,59 @@ class _BertClassifier(TextClassifier):
        seq_len=self._model_options.seq_len,
        do_lower_case=self._model_spec.do_lower_case,
        uri=self._model_spec.downloaded_files.get_path(),
+        model_name=self._model_spec.name,
    )
-    return (self._text_preprocessor.preprocess(train_data),
-            self._text_preprocessor.preprocess(validation_data))
+    return (
+        self._text_preprocessor.preprocess(train_data),
+        self._text_preprocessor.preprocess(validation_data),
+    )
+
+  def _create_metrics(self):
+    """Creates metrics for training and evaluation.
+
+    The default metrics are accuracy, precision, and recall.
+
+    For binary classification tasks only (num_classes=2):
+      Users can configure PrecisionAtRecall and RecallAtPrecision metrics using
+      the desired_presisions and desired_recalls fields in BertHParams.
+
+    Returns:
+      A list of tf.keras.Metric subclasses which can be used with model.compile
+    """
+    metric_functions = [
+        tf.keras.metrics.SparseCategoricalAccuracy(
+            "accuracy", dtype=tf.float32
+        ),
+        metrics.SparsePrecision(name="precision", dtype=tf.float32),
+        metrics.SparseRecall(name="recall", dtype=tf.float32),
+    ]
+    if self._num_classes == 2:
+      if self._hparams.desired_precisions:
+        for desired_precision in self._hparams.desired_precisions:
+          metric_functions.append(
+              metrics.BinarySparseRecallAtPrecision(
+                  desired_precision,
+                  name=f"recall_at_precision_{desired_precision}",
+                  num_thresholds=1000,
+              )
+          )
+      if self._hparams.desired_recalls:
+        for desired_recall in self._hparams.desired_recalls:
+          metric_functions.append(
+              metrics.BinarySparseRecallAtPrecision(
+                  desired_recall,
+                  name=f"precision_at_recall_{desired_recall}",
+                  num_thresholds=1000,
+              )
+          )
+    else:
+      if self._hparams.desired_precisions or self._hparams.desired_recalls:
+        raise ValueError(
+            "desired_recalls and desired_precisions parameters are binary"
+            " metrics and not supported for num_classes > 2. Found"
+            f" num_classes: {self._num_classes}"
+        )
+    return metric_functions

  def _create_model(self):
    """Creates a BERT-based classifier model.
@@ -447,11 +473,20 @@ class _BertClassifier(TextClassifier):
    """
    encoder_inputs = dict(
        input_word_ids=tf.keras.layers.Input(
-            shape=(self._model_options.seq_len,), dtype=tf.int32),
+            shape=(self._model_options.seq_len,),
+            dtype=tf.int32,
+            name="input_word_ids",
+        ),
        input_mask=tf.keras.layers.Input(
-            shape=(self._model_options.seq_len,), dtype=tf.int32),
+            shape=(self._model_options.seq_len,),
+            dtype=tf.int32,
+            name="input_mask",
+        ),
        input_type_ids=tf.keras.layers.Input(
-            shape=(self._model_options.seq_len,), dtype=tf.int32),
+            shape=(self._model_options.seq_len,),
+            dtype=tf.int32,
+            name="input_type_ids",
+        ),
    )
    encoder = hub.KerasLayer(
        self._model_spec.downloaded_files.get_path(),
@@ -493,16 +528,21 @@ class _BertClassifier(TextClassifier):
    lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=initial_lr,
        decay_steps=total_steps,
-        end_learning_rate=0.0,
-        power=1.0)
+        end_learning_rate=self._hparams.end_learning_rate,
+        power=1.0,
+    )
    if warmup_steps:
      lr_schedule = model_util.WarmUp(
          initial_learning_rate=initial_lr,
          decay_schedule_fn=lr_schedule,
-          warmup_steps=warmup_steps)
+          warmup_steps=warmup_steps,
+      )
    if self._hparams.optimizer == hp.BertOptimizer.ADAMW:
      self._optimizer = tf.keras.optimizers.experimental.AdamW(
-          lr_schedule, weight_decay=0.01, epsilon=1e-6, global_clipnorm=1.0
+          lr_schedule,
+          weight_decay=self._hparams.weight_decay,
+          epsilon=1e-6,
+          global_clipnorm=1.0,
      )
      self._optimizer.exclude_from_weight_decay(
          var_names=["LayerNorm", "layer_norm", "bias"]
@@ -510,7 +550,7 @@ class _BertClassifier(TextClassifier):
    elif self._hparams.optimizer == hp.BertOptimizer.LAMB:
      self._optimizer = tfa_optimizers.LAMB(
          lr_schedule,
-          weight_decay_rate=0.01,
+          weight_decay_rate=self._hparams.weight_decay,
          epsilon=1e-6,
          exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
          global_clipnorm=1.0,
@@ -84,8 +84,8 @@ def run(data_dir,
                                                options)

  # Gets evaluation results.
-  _, acc = model.evaluate(validation_data)
-  print('Eval accuracy: %f' % acc)
+  metrics = model.evaluate(validation_data)
+  print('Eval accuracy: %f' % metrics[1])

  model.export_model(quantization_config=quantization_config)
  model.export_labels(export_dir=options.hparams.export_dir)
@@ -16,17 +16,17 @@ import csv
 import filecmp
 import os
 import tempfile
-import unittest
 from unittest import mock as unittest_mock

+from absl.testing import parameterized
 import tensorflow as tf

+from mediapipe.model_maker.python.core.utils import loss_functions
 from mediapipe.model_maker.python.text import text_classifier
 from mediapipe.tasks.python.test import test_utils


-@unittest.skip('b/275624089')
-class TextClassifierTest(tf.test.TestCase):
+class TextClassifierTest(tf.test.TestCase, parameterized.TestCase):

  _AVERAGE_WORD_EMBEDDING_JSON_FILE = (
      test_utils.get_test_data_path('average_word_embedding_metadata.json'))
@@ -78,8 +78,8 @@ class TextClassifierTest(tf.test.TestCase):
        text_classifier.TextClassifier.create(train_data, validation_data,
                                              options))

-    _, accuracy = average_word_embedding_classifier.evaluate(validation_data)
-    self.assertGreaterEqual(accuracy, 0.0)
+    metrics = average_word_embedding_classifier.evaluate(validation_data)
+    self.assertGreaterEqual(metrics[1], 0.0)  # metrics[1] is accuracy

    # Test export_model
    average_word_embedding_classifier.export_model()
@@ -98,12 +98,25 @@ class TextClassifierTest(tf.test.TestCase):
        filecmp.cmp(
            output_metadata_file,
            self._AVERAGE_WORD_EMBEDDING_JSON_FILE,
-            shallow=False))
+            shallow=False,
+        )
+    )

-  def test_create_and_train_bert(self):
+  @parameterized.named_parameters(
+      # Skipping mobilebert b/c OSS test timeout/flakiness: b/275624089
+      # dict(
+      #     testcase_name='mobilebert',
+      #     supported_model=text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER,
+      # ),
+      dict(
+          testcase_name='exbert',
+          supported_model=text_classifier.SupportedModels.EXBERT_CLASSIFIER,
+      ),
+  )
+  def test_create_and_train_bert(self, supported_model):
    train_data, validation_data = self._get_data()
    options = text_classifier.TextClassifierOptions(
-        supported_model=text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER,
+        supported_model=supported_model,
        model_options=text_classifier.BertModelOptions(
            do_fine_tuning=False, seq_len=2
        ),
@@ -117,8 +130,8 @@ class TextClassifierTest(tf.test.TestCase):
    bert_classifier = text_classifier.TextClassifier.create(
        train_data, validation_data, options)

-    _, accuracy = bert_classifier.evaluate(validation_data)
-    self.assertGreaterEqual(accuracy, 0.0)
+    metrics = bert_classifier.evaluate(validation_data)
+    self.assertGreaterEqual(metrics[1], 0.0)  # metrics[1] is accuracy

    # Test export_model
    bert_classifier.export_model()
@@ -142,45 +155,93 @@ class TextClassifierTest(tf.test.TestCase):
    )

  def test_label_mismatch(self):
-    options = (
-        text_classifier.TextClassifierOptions(
-            supported_model=(
-                text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER)))
+    options = text_classifier.TextClassifierOptions(
+        supported_model=(text_classifier.SupportedModels.EXBERT_CLASSIFIER)
+    )
    train_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]])
-    train_data = text_classifier.Dataset(train_tf_dataset, 1, ['foo'])
+    train_data = text_classifier.Dataset(train_tf_dataset, ['foo'], 1)
    validation_tf_dataset = tf.data.Dataset.from_tensor_slices([[0]])
-    validation_data = text_classifier.Dataset(validation_tf_dataset, 1, ['bar'])
+    validation_data = text_classifier.Dataset(validation_tf_dataset, ['bar'], 1)
    with self.assertRaisesRegex(
        ValueError,
-        'Training data label names .* not equal to validation data label names'
+        'Training data label names .* not equal to validation data label names',
    ):
-      text_classifier.TextClassifier.create(train_data, validation_data,
-                                            options)
+      text_classifier.TextClassifier.create(
+          train_data, validation_data, options
+      )

  def test_options_mismatch(self):
    train_data, validation_data = self._get_data()

-    avg_options = (
-        text_classifier.TextClassifierOptions(
-            supported_model=(
-                text_classifier.SupportedModels.MOBILEBERT_CLASSIFIER),
-            model_options=text_classifier.AverageWordEmbeddingModelOptions()))
-    with self.assertRaisesRegex(
-        ValueError, 'Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER, got'
-        ' SupportedModels.MOBILEBERT_CLASSIFIER'):
-      text_classifier.TextClassifier.create(train_data, validation_data,
-                                            avg_options)
+    avg_options = text_classifier.TextClassifierOptions(
+        supported_model=(text_classifier.SupportedModels.EXBERT_CLASSIFIER),
+        model_options=text_classifier.AverageWordEmbeddingModelOptions(),
+    )
+    with self.assertRaisesWithLiteralMatch(
+        ValueError,
+        'Expected AVERAGE_WORD_EMBEDDING_CLASSIFIER, got'
+        ' SupportedModels.EXBERT_CLASSIFIER',
+    ):
+      text_classifier.TextClassifier.create(
+          train_data, validation_data, avg_options
+      )

-    bert_options = (
-        text_classifier.TextClassifierOptions(
-            supported_model=(text_classifier.SupportedModels
-                             .AVERAGE_WORD_EMBEDDING_CLASSIFIER),
-            model_options=text_classifier.BertModelOptions()))
-    with self.assertRaisesRegex(
-        ValueError, 'Expected MOBILEBERT_CLASSIFIER, got'
-        ' SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER'):
-      text_classifier.TextClassifier.create(train_data, validation_data,
-                                            bert_options)
+    bert_options = text_classifier.TextClassifierOptions(
+        supported_model=(
+            text_classifier.SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER
+        ),
+        model_options=text_classifier.BertModelOptions(),
+    )
+    with self.assertRaisesWithLiteralMatch(
+        ValueError,
+        'Expected a Bert Classifier(MobileBERT or EXBERT), got'
+        ' SupportedModels.AVERAGE_WORD_EMBEDDING_CLASSIFIER',
+    ):
+      text_classifier.TextClassifier.create(
+          train_data, validation_data, bert_options
+      )
+
+  def test_bert_loss_and_metrics_creation(self):
+    train_data, validation_data = self._get_data()
+    supported_model = text_classifier.SupportedModels.EXBERT_CLASSIFIER
+    hparams = text_classifier.BertHParams(
+        desired_recalls=[0.2],
+        desired_precisions=[0.9],
+        epochs=1,
+        batch_size=1,
+        learning_rate=3e-5,
+        distribution_strategy='off',
+        gamma=3.5,
+    )
+    options = text_classifier.TextClassifierOptions(
+        supported_model=supported_model, hparams=hparams
+    )
+    bert_classifier = text_classifier.TextClassifier.create(
+        train_data, validation_data, options
+    )
+    loss_fn = bert_classifier._loss_function
+    self.assertIsInstance(loss_fn, loss_functions.SparseFocalLoss)
+    self.assertEqual(loss_fn._gamma, 3.5)
+    self.assertEqual(loss_fn._num_classes, 2)
+    metric_names = [m.name for m in bert_classifier._metric_functions]
+    expected_metric_names = [
+        'accuracy',
+        'recall',
+        'precision',
+        'precision_at_recall_0.2',
+        'recall_at_precision_0.9',
+    ]
+    self.assertCountEqual(metric_names, expected_metric_names)
+
+    # Non-binary data
+    tf_dataset = tf.data.Dataset.from_tensor_slices([[0]])
+    data = text_classifier.Dataset(tf_dataset, ['foo', 'bar', 'baz'], 1)
+    with self.assertRaisesWithLiteralMatch(
+        ValueError,
+        'desired_recalls and desired_precisions parameters are binary metrics'
+        ' and not supported for num_classes > 2. Found num_classes: 3',
+    ):
+      text_classifier.TextClassifier.create(data, data, options)


 if __name__ == '__main__':
@@ -13,7 +13,7 @@
 # limitations under the License.

 # Placeholder for internal Python strict test compatibility macro.
-# Placeholder for internal Python strict library and test compatibility macro.
+# Placeholder for internal Python strict binary and library compatibility macro.

 licenses(["notice"])

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# Placeholder for internal Python strict library and test compatibility macro.
+# Placeholder for internal Python strict binary and library compatibility macro.
 # Placeholder for internal Python library rule.

 licenses(["notice"])
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# Placeholder for internal Python strict library and test compatibility macro.
+# Placeholder for internal Python strict binary and library compatibility macro.
 # Placeholder for internal Python strict test compatibility macro.

 licenses(["notice"])
@@ -5,4 +5,4 @@ opencv-python
 tensorflow>=2.10
 tensorflow-datasets
 tensorflow-hub
-tf-models-official==2.11.6
+tf-models-official>=2.13.1
@@ -13,17 +13,17 @@
 # limitations under the License.
 """MediaPipe solution drawing utils."""

+import dataclasses
 import math
 from typing import List, Mapping, Optional, Tuple, Union

 import cv2
-import dataclasses
 import matplotlib.pyplot as plt
 import numpy as np

 from mediapipe.framework.formats import detection_pb2
-from mediapipe.framework.formats import location_data_pb2
 from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import location_data_pb2

 _PRESENCE_THRESHOLD = 0.5
 _VISIBILITY_THRESHOLD = 0.5
@@ -20,7 +20,6 @@ import cv2
 import numpy as np

 from google.protobuf import text_format
-
 from mediapipe.framework.formats import detection_pb2
 from mediapipe.framework.formats import landmark_pb2
 from mediapipe.python.solutions import drawing_utils
@@ -0,0 +1,29 @@
+# TODO: describe this package.
+
+# 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+cc_library(
+    name = "category",
+    hdrs = ["category.h"],
+)
+
+cc_library(
+    name = "classification_result",
+    hdrs = ["classification_result.h"],
+)
@@ -0,0 +1,42 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CATEGORY_H_
+#define MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CATEGORY_H_
+
+// Defines a single classification result.
+//
+// The label maps packed into the TFLite Model Metadata [1] are used to populate
+// the 'category_name' and 'display_name' fields.
+//
+// [1]: https://www.tensorflow.org/lite/convert/metadata
+struct Category {
+  // The index of the category in the classification model output.
+  int index;
+
+  // The score for this category, e.g. (but not necessarily) a probability in
+  // [0,1].
+  float score;
+
+  // The optional ID for the category, read from the label map packed in the
+  // TFLite Model Metadata if present. Not necessarily human-readable.
+  char* category_name;
+
+  // The optional human-readable name for the category, read from the label map
+  // packed in the TFLite Model Metadata if present.
+  char* display_name;
+};
+
+#endif  // MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CATEGORY_H_
@@ -0,0 +1,60 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CLASSIFICATION_RESULT_H_
+#define MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CLASSIFICATION_RESULT_H_
+
+#include <stdbool.h>
+#include <stdint.h>
+
+// Defines classification results for a given classifier head.
+struct Classifications {
+  // The array of predicted categories, usually sorted by descending scores,
+  // e.g. from high to low probability.
+  struct Category* categories;
+  // The number of elements in the categories array.
+  uint32_t categories_count;
+
+  // The index of the classifier head (i.e. output tensor) these categories
+  // refer to. This is useful for multi-head models.
+  int head_index;
+
+  // The optional name of the classifier head, as provided in the TFLite Model
+  // Metadata [1] if present. This is useful for multi-head models.
+  //
+  // [1]: https://www.tensorflow.org/lite/convert/metadata
+  char* head_name;
+};
+
+// Defines classification results of a model.
+struct ClassificationResult {
+  // The classification results for each head of the model.
+  struct Classifications* classifications;
+  // The number of classifications in the classifications array.
+  uint32_t classifications_count;
+
+  // The optional timestamp (in milliseconds) of the start of the chunk of data
+  // corresponding to these results.
+  //
+  // This is only used for classification on time series (e.g. audio
+  // classification). In these use cases, the amount of data to process might
+  // exceed the maximum size that the model can process: to solve this, the
+  // input data is split into multiple chunks starting at different timestamps.
+  int64_t timestamp_ms;
+  // Specifies whether the timestamp contains a valid value.
+  bool has_timestamp_ms;
+};
+
+#endif  // MEDIAPIPE_TASKS_C_COMPONENTS_CONTAINERS_CLASSIFICATION_RESULT_H_
@@ -0,0 +1,22 @@
+# Copyright 2023 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+cc_library(
+    name = "classifier_options",
+    hdrs = ["classifier_options.h"],
+)
@@ -0,0 +1,51 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_C_COMPONENTS_PROCESSORS_CLASSIFIER_OPTIONS_H_
+#define MEDIAPIPE_TASKS_C_COMPONENTS_PROCESSORS_CLASSIFIER_OPTIONS_H_
+
+#include <stdint.h>
+
+// Classifier options for MediaPipe C classification Tasks.
+struct ClassifierOptions {
+  // The locale to use for display names specified through the TFLite Model
+  // Metadata, if any. Defaults to English.
+  char* display_names_locale;
+
+  // The maximum number of top-scored classification results to return. If < 0,
+  // all available results will be returned. If 0, an invalid argument error is
+  // returned.
+  int max_results;
+
+  // Score threshold to override the one provided in the model metadata (if
+  // any). Results below this value are rejected.
+  float score_threshold;
+
+  // The allowlist of category names. If non-empty, detection 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.
+  char** category_allowlist;
+  // The number of elements in the category allowlist.
+  uint32_t category_allowlist_count;
+
+  // The denylist of category names. If non-empty, detection results whose
+  // category name is in this set will be filtered out. Duplicate or unknown
+  // category names are ignored. Mutually exclusive with category_allowlist.
+  char** category_denylist = {};
+  // The number of elements in the category denylist.
+  uint32_t category_denylist_count;
+};
+
+#endif  // MEDIAPIPE_TASKS_C_COMPONENTS_PROCESSORS_CLASSIFIER_OPTIONS_H_
@@ -0,0 +1,22 @@
+# Copyright 2023 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+cc_library(
+    name = "base_options",
+    hdrs = ["base_options.h"],
+)
@@ -0,0 +1,28 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_C_CORE_BASE_OPTIONS_H_
+#define MEDIAPIPE_TASKS_C_CORE_BASE_OPTIONS_H_
+
+// Base options for MediaPipe C Tasks.
+struct BaseOptions {
+  // The model asset file contents as a string.
+  char* model_asset_buffer;
+
+  // The path to the model asset to open and mmap in memory.
+  char* model_asset_path;
+};
+
+#endif  // MEDIAPIPE_TASKS_C_CORE_BASE_OPTIONS_H_
@@ -0,0 +1,28 @@
+# Copyright 2023 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+cc_library(
+    name = "text_classifier",
+    hdrs = ["text_classifier.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/tasks/c/components/containers:classification_result",
+        "//mediapipe/tasks/c/components/processors:classifier_options",
+        "//mediapipe/tasks/c/core:base_options",
+    ],
+)
@@ -0,0 +1,46 @@
+/* Copyright 2023 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.
+==============================================================================*/
+
+#ifndef MEDIAPIPE_TASKS_C_TEXT_TEXT_CLASSIFIER_TEXT_CLASSIFIER_H_
+#define MEDIAPIPE_TASKS_C_TEXT_TEXT_CLASSIFIER_TEXT_CLASSIFIER_H_
+
+#include "mediapipe/tasks/c/components/containers/classification_result.h"
+#include "mediapipe/tasks/c/components/processors/classifier_options.h"
+#include "mediapipe/tasks/c/core/base_options.h"
+
+typedef ClassificationResult TextClassifierResult;
+
+// The options for configuring a MediaPipe text classifier task.
+struct TextClassifierOptions {
+  // Base options for configuring MediaPipe Tasks, such as specifying the model
+  // file with metadata, accelerator options, op resolver, etc.
+  struct BaseOptions base_options;
+
+  // Options for configuring the classifier behavior, such as score threshold,
+  // number of results, etc.
+  struct ClassifierOptions classifier_options;
+};
+
+// Creates a TextClassifier from the provided `options`.
+void* text_classifier_create(struct TextClassifierOptions options);
+
+// Performs classification on the input `text`.
+TextClassifierResult text_classifier_classify(void* classifier,
+                                              char* utf8_text);
+
+// Shuts down the TextClassifier when all the work is done. Frees all memory.
+void text_classifier_close(void* classifier);
+
+#endif  // MEDIAPIPE_TASKS_C_TEXT_TEXT_CLASSIFIER_TEXT_CLASSIFIER_H_
@@ -16,6 +16,7 @@ limitations under the License.
 #include "mediapipe/tasks/cc/vision/image_segmenter/image_segmenter.h"

 #include <optional>
+#include <utility>

 #include "absl/strings/str_format.h"
 #include "mediapipe/framework/api2/builder.h"
@@ -41,6 +42,8 @@ constexpr char kConfidenceMasksTag[] = "CONFIDENCE_MASKS";
 constexpr char kConfidenceMasksStreamName[] = "confidence_masks";
 constexpr char kCategoryMaskTag[] = "CATEGORY_MASK";
 constexpr char kCategoryMaskStreamName[] = "category_mask";
+constexpr char kOutputSizeTag[] = "OUTPUT_SIZE";
+constexpr char kOutputSizeStreamName[] = "output_size";
 constexpr char kImageInStreamName[] = "image_in";
 constexpr char kImageOutStreamName[] = "image_out";
 constexpr char kImageTag[] = "IMAGE";
@@ -70,6 +73,7 @@ CalculatorGraphConfig CreateGraphConfig(
      options.get());
  graph.In(kImageTag).SetName(kImageInStreamName);
  graph.In(kNormRectTag).SetName(kNormRectStreamName);
+  graph.In(kOutputSizeTag).SetName(kOutputSizeStreamName);
  if (output_confidence_masks) {
    task_subgraph.Out(kConfidenceMasksTag)
            .SetName(kConfidenceMasksStreamName) >>
@@ -85,10 +89,12 @@ CalculatorGraphConfig CreateGraphConfig(
      graph.Out(kImageTag);
  if (enable_flow_limiting) {
    return tasks::core::AddFlowLimiterCalculator(
-        graph, task_subgraph, {kImageTag, kNormRectTag}, kConfidenceMasksTag);
+        graph, task_subgraph, {kImageTag, kNormRectTag, kOutputSizeTag},
+        kConfidenceMasksTag);
  }
  graph.In(kImageTag) >> task_subgraph.In(kImageTag);
  graph.In(kNormRectTag) >> task_subgraph.In(kNormRectTag);
+  graph.In(kOutputSizeTag) >> task_subgraph.In(kOutputSizeTag);
  return graph.GetConfig();
 }

@@ -211,6 +217,13 @@ absl::StatusOr<std::unique_ptr<ImageSegmenter>> ImageSegmenter::Create(
 absl::StatusOr<ImageSegmenterResult> ImageSegmenter::Segment(
    mediapipe::Image image,
    std::optional<core::ImageProcessingOptions> image_processing_options) {
+  return Segment(image, image.width(), image.height(),
+                 std::move(image_processing_options));
+}
+
+absl::StatusOr<ImageSegmenterResult> ImageSegmenter::Segment(
+    mediapipe::Image image, int output_width, int output_height,
+    std::optional<core::ImageProcessingOptions> image_processing_options) {
  if (image.UsesGpu()) {
    return CreateStatusWithPayload(
        absl::StatusCode::kInvalidArgument,
@@ -225,7 +238,10 @@ absl::StatusOr<ImageSegmenterResult> ImageSegmenter::Segment(
      ProcessImageData(
          {{kImageInStreamName, mediapipe::MakePacket<Image>(std::move(image))},
           {kNormRectStreamName,
-            MakePacket<NormalizedRect>(std::move(norm_rect))}}));
+            MakePacket<NormalizedRect>(std::move(norm_rect))},
+           {kOutputSizeStreamName,
+            MakePacket<std::pair<int, int>>(
+                std::make_pair(output_width, output_height))}}));
  std::optional<std::vector<Image>> confidence_masks;
  if (output_confidence_masks_) {
    confidence_masks =
@@ -243,6 +259,14 @@ absl::StatusOr<ImageSegmenterResult> ImageSegmenter::Segment(
 absl::StatusOr<ImageSegmenterResult> ImageSegmenter::SegmentForVideo(
    mediapipe::Image image, int64_t timestamp_ms,
    std::optional<core::ImageProcessingOptions> image_processing_options) {
+  return SegmentForVideo(image, image.width(), image.height(), timestamp_ms,
+                         image_processing_options);
+}
+
+absl::StatusOr<ImageSegmenterResult> ImageSegmenter::SegmentForVideo(
+    mediapipe::Image image, int output_width, int output_height,
+    int64_t timestamp_ms,
+    std::optional<core::ImageProcessingOptions> image_processing_options) {
  if (image.UsesGpu()) {
    return CreateStatusWithPayload(
        absl::StatusCode::kInvalidArgument,
@@ -260,6 +284,10 @@ absl::StatusOr<ImageSegmenterResult> ImageSegmenter::SegmentForVideo(
                .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))},
           {kNormRectStreamName,
            MakePacket<NormalizedRect>(std::move(norm_rect))
+                .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))},
+           {kOutputSizeStreamName,
+            MakePacket<std::pair<int, int>>(
+                std::make_pair(output_width, output_height))
                .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))}}));
  std::optional<std::vector<Image>> confidence_masks;
  if (output_confidence_masks_) {
@@ -278,6 +306,13 @@ absl::StatusOr<ImageSegmenterResult> ImageSegmenter::SegmentForVideo(
 absl::Status ImageSegmenter::SegmentAsync(
    Image image, int64_t timestamp_ms,
    std::optional<core::ImageProcessingOptions> image_processing_options) {
+  return SegmentAsync(image, image.width(), image.height(), timestamp_ms,
+                      image_processing_options);
+}
+
+absl::Status ImageSegmenter::SegmentAsync(
+    Image image, int output_width, int output_height, int64_t timestamp_ms,
+    std::optional<core::ImageProcessingOptions> image_processing_options) {
  if (image.UsesGpu()) {
    return CreateStatusWithPayload(
        absl::StatusCode::kInvalidArgument,
@@ -293,6 +328,10 @@ absl::Status ImageSegmenter::SegmentAsync(
            .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))},
       {kNormRectStreamName,
        MakePacket<NormalizedRect>(std::move(norm_rect))
+            .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))},
+       {kOutputSizeStreamName,
+        MakePacket<std::pair<int, int>>(
+            std::make_pair(output_width, output_height))
            .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))}});
 }

@@ -102,17 +102,36 @@ class ImageSegmenter : tasks::vision::core::BaseVisionTaskApi {
  //
  // The image can be of any size with format RGB or RGBA.
  //
+  // The output size is the same as the input image size.
+  //
  // The optional 'image_processing_options' parameter can be used to specify
  // the rotation to apply to the image before performing segmentation, by
  // setting its 'rotation_degrees' field. Note that specifying a
  // region-of-interest using the 'region_of_interest' field is NOT supported
  // and will result in an invalid argument error being returned.
-
  absl::StatusOr<ImageSegmenterResult> Segment(
      mediapipe::Image image,
      std::optional<core::ImageProcessingOptions> image_processing_options =
          std::nullopt);

+  // Performs image segmentation on the provided single image.
+  // Only use this method when the ImageSegmenter is created with the image
+  // running mode.
+  //
+  // The image can be of any size with format RGB or RGBA.
+  //
+  // The output width and height specify the size of the resulted mask.
+  //
+  // The optional 'image_processing_options' parameter can be used to specify
+  // the rotation to apply to the image before performing segmentation, by
+  // setting its 'rotation_degrees' field. Note that specifying a
+  // region-of-interest using the 'region_of_interest' field is NOT supported
+  // and will result in an invalid argument error being returned.
+  absl::StatusOr<ImageSegmenterResult> Segment(
+      mediapipe::Image image, int output_width, int output_height,
+      std::optional<core::ImageProcessingOptions> image_processing_options =
+          std::nullopt);
+
  // Performs image segmentation on the provided video frame.
  // Only use this method when the ImageSegmenter is created with the video
  // running mode.
@@ -121,16 +140,39 @@ class ImageSegmenter : tasks::vision::core::BaseVisionTaskApi {
  // provide the video frame's timestamp (in milliseconds). The input timestamps
  // must be monotonically increasing.
  //
-  // The optional 'image_processing_options' parameter can be used to specify
-  // the rotation to apply to the image before performing segmentation, by
-  // setting its 'rotation_degrees' field. Note that specifying a
-  // region-of-interest using the 'region_of_interest' field is NOT supported
+  // The output size is the same as the input image size.
+  //
+  // The optional 'image_processing_options' parameter can be used
+  // to specify the rotation to apply to the image before performing
+  // segmentation, by setting its 'rotation_degrees' field. Note that specifying
+  // a region-of-interest using the 'region_of_interest' field is NOT supported
  // and will result in an invalid argument error being returned.
  absl::StatusOr<ImageSegmenterResult> SegmentForVideo(
      mediapipe::Image image, int64_t timestamp_ms,
      std::optional<core::ImageProcessingOptions> image_processing_options =
          std::nullopt);

+  // Performs image segmentation on the provided video frame.
+  // Only use this method when the ImageSegmenter is created with the video
+  // running mode.
+  //
+  // The image can be of any size with format RGB or RGBA. It's required to
+  // provide the video frame's timestamp (in milliseconds). The input timestamps
+  // must be monotonically increasing.
+  //
+  // The output width and height specify the size of the resulted mask.
+  //
+  // The optional 'image_processing_options' parameter can be used
+  // to specify the rotation to apply to the image before performing
+  // segmentation, by setting its 'rotation_degrees' field. Note that specifying
+  // a region-of-interest using the 'region_of_interest' field is NOT supported
+  // and will result in an invalid argument error being returned.
+  absl::StatusOr<ImageSegmenterResult> SegmentForVideo(
+      mediapipe::Image image, int output_width, int output_height,
+      int64_t timestamp_ms,
+      std::optional<core::ImageProcessingOptions> image_processing_options =
+          std::nullopt);
+
  // Sends live image data to perform image segmentation, and the results will
  // be available via the "result_callback" provided in the
  // ImageSegmenterOptions. Only use this method when the ImageSegmenter is
@@ -141,6 +183,8 @@ class ImageSegmenter : tasks::vision::core::BaseVisionTaskApi {
  // sent to the image segmenter. The input timestamps must be monotonically
  // increasing.
  //
+  // The output size is the same as the input image size.
+  //
  // The optional 'image_processing_options' parameter can be used to specify
  // the rotation to apply to the image before performing segmentation, by
  // setting its 'rotation_degrees' field. Note that specifying a
@@ -158,6 +202,36 @@ class ImageSegmenter : tasks::vision::core::BaseVisionTaskApi {
                            std::optional<core::ImageProcessingOptions>
                                image_processing_options = std::nullopt);

+  // Sends live image data to perform image segmentation, and the results will
+  // be available via the "result_callback" provided in the
+  // ImageSegmenterOptions. Only use this method when the ImageSegmenter is
+  // created with the live stream running mode.
+  //
+  // The image can be of any size with format RGB or RGBA. It's required to
+  // provide a timestamp (in milliseconds) to indicate when the input image is
+  // sent to the image segmenter. The input timestamps must be monotonically
+  // increasing.
+  //
+  // The output width and height specify the size of the resulted mask.
+  //
+  // The optional 'image_processing_options' parameter can be used to specify
+  // the rotation to apply to the image before performing segmentation, by
+  // setting its 'rotation_degrees' field. Note that specifying a
+  // region-of-interest using the 'region_of_interest' field is NOT supported
+  // and will result in an invalid argument error being returned.
+  //
+  // The "result_callback" prvoides
+  //   - An ImageSegmenterResult.
+  //   - The const reference to the corresponding input image that the image
+  //     segmentation runs on. Note that the const reference to the image will
+  //     no longer be valid when the callback returns. To access the image data
+  //     outside of the callback, callers need to make a copy of the image.
+  //   - The input timestamp in milliseconds.
+  absl::Status SegmentAsync(mediapipe::Image image, int output_width,
+                            int output_height, int64_t timestamp_ms,
+                            std::optional<core::ImageProcessingOptions>
+                                image_processing_options = std::nullopt);
+
  // Shuts down the ImageSegmenter when all works are done.
  absl::Status Close() { return runner_->Close(); }

@@ -82,6 +82,7 @@ constexpr char kImageGpuTag[] = "IMAGE_GPU";
 constexpr char kNormRectTag[] = "NORM_RECT";
 constexpr char kTensorsTag[] = "TENSORS";
 constexpr char kOutputSizeTag[] = "OUTPUT_SIZE";
+constexpr char kSizeTag[] = "SIZE";
 constexpr char kQualityScoresTag[] = "QUALITY_SCORES";
 constexpr char kSegmentationMetadataName[] = "SEGMENTER_METADATA";

@@ -356,6 +357,9 @@ absl::StatusOr<ImageAndTensorsOnDevice> ConvertImageToTensors(
 //     Describes image rotation and region of image to perform detection
 //     on.
 //     @Optional: rect covering the whole image is used if not specified.
+//   OUTPUT_SIZE - std::pair<int, int> @Optional
+//     The output size of the mask, in width and height. If not specified, the
+//     output size of the input image is used.
 //
 // Outputs:
 //   CONFIDENCE_MASK - mediapipe::Image @Multiple
@@ -400,11 +404,16 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
    if (!options.segmenter_options().has_output_type()) {
      MP_RETURN_IF_ERROR(SanityCheck(sc));
    }
+    std::optional<Source<std::pair<int, int>>> output_size;
+    if (HasInput(sc->OriginalNode(), kOutputSizeTag)) {
+      output_size = graph.In(kOutputSizeTag).Cast<std::pair<int, int>>();
+    }
    ASSIGN_OR_RETURN(
        auto output_streams,
        BuildSegmentationTask(
            options, *model_resources, graph[Input<Image>(kImageTag)],
-            graph[Input<NormalizedRect>::Optional(kNormRectTag)], graph));
+            graph[Input<NormalizedRect>::Optional(kNormRectTag)], output_size,
+            graph));

    // TODO: remove deprecated output type support.
    if (options.segmenter_options().has_output_type()) {
@@ -469,7 +478,8 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
  absl::StatusOr<ImageSegmenterOutputs> BuildSegmentationTask(
      const ImageSegmenterGraphOptions& task_options,
      const core::ModelResources& model_resources, Source<Image> image_in,
-      Source<NormalizedRect> norm_rect_in, Graph& graph) {
+      Source<NormalizedRect> norm_rect_in,
+      std::optional<Source<std::pair<int, int>>> output_size, Graph& graph) {
    MP_RETURN_IF_ERROR(SanityCheckOptions(task_options));

    // Adds preprocessing calculators and connects them to the graph input image
@@ -514,10 +524,14 @@ class ImageSegmenterGraph : public core::ModelTaskGraph {
    image_and_tensors.tensors >> inference.In(kTensorsTag);
    inference.Out(kTensorsTag) >> tensor_to_images.In(kTensorsTag);

-    // Adds image property calculator for output size.
-    auto& image_properties = graph.AddNode("ImagePropertiesCalculator");
-    image_in >> image_properties.In("IMAGE");
-    image_properties.Out("SIZE") >> tensor_to_images.In(kOutputSizeTag);
+    if (output_size.has_value()) {
+      *output_size >> tensor_to_images.In(kOutputSizeTag);
+    } else {
+      // Adds image property calculator for output size.
+      auto& image_properties = graph.AddNode("ImagePropertiesCalculator");
+      image_in >> image_properties.In(kImageTag);
+      image_properties.Out(kSizeTag) >> tensor_to_images.In(kOutputSizeTag);
+    }

    // Exports multiple segmented masks.
    // TODO: remove deprecated output type support.
@@ -66,7 +66,9 @@ strip_api_include_path_prefix(
        "//mediapipe/tasks/ios/components/containers:sources/MPPClassificationResult.h",
        "//mediapipe/tasks/ios/components/containers:sources/MPPEmbedding.h",
        "//mediapipe/tasks/ios/components/containers:sources/MPPEmbeddingResult.h",
+        "//mediapipe/tasks/ios/components/containers:sources/MPPConnection.h",
        "//mediapipe/tasks/ios/components/containers:sources/MPPDetection.h",
+        "//mediapipe/tasks/ios/components/containers:sources/MPPLandmark.h",
        "//mediapipe/tasks/ios/core:sources/MPPBaseOptions.h",
        "//mediapipe/tasks/ios/core:sources/MPPTaskOptions.h",
        "//mediapipe/tasks/ios/core:sources/MPPTaskResult.h",
@@ -160,6 +162,8 @@ apple_static_xcframework(
        ":MPPCategory.h",
        ":MPPClassificationResult.h",
        ":MPPDetection.h",
+        ":MPPLandmark.h",
+        ":MPPConnection.h",
        ":MPPCommon.h",
        ":MPPTaskOptions.h",
        ":MPPTaskResult.h",
@@ -30,7 +30,7 @@ NS_ASSUME_NONNULL_BEGIN
 * The delegate of `MPPFaceLandmarker` must adopt `MPPFaceLandmarkerLiveStreamDelegate` protocol.
 * The methods in this protocol are optional.
 */
-NS_SWIFT_NAME(FaceDetectorLiveStreamDelegate)
+NS_SWIFT_NAME(FaceLandmarkerLiveStreamDelegate)
@protocol MPPFaceLandmarkerLiveStreamDelegate <NSObject>

 /**
@@ -35,3 +35,13 @@ objc_library(
        "//mediapipe/tasks/ios/vision/core:MPPRunningMode",
    ],
 )
+
+objc_library(
+    name = "MPPImageSegmenter",
+    hdrs = ["sources/MPPImageSegmenterOptions.h"],
+    deps = [
+        ":MPPImageSegmenterOptions",
+        ":MPPImageSegmenterResult",
+        "//mediapipe/tasks/ios/vision/core:MPPImage",
+    ],
+)
@@ -0,0 +1,217 @@
+// Copyright 2023 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.
+
+#import <Foundation/Foundation.h>
+
+#import "mediapipe/tasks/ios/vision/core/sources/MPPImage.h"
+#import "mediapipe/tasks/ios/vision/image_segmenter/sources/MPPImageSegmenterOptions.h"
+#import "mediapipe/tasks/ios/vision/image_segmenter/sources/MPPImageSegmenterResult.h"
+
+NS_ASSUME_NONNULL_BEGIN
+
+/**
+ * @brief Class that performs segmentation on images.
+ *
+ * The API expects a TFLite model with mandatory TFLite Model Metadata.
+ */
+NS_SWIFT_NAME(ImageSegmenter)
+@interface MPPImageSegmenter : NSObject
+
+/**
+ * Creates a new instance of `MPPImageSegmenter` from an absolute path to a TensorFlow Lite model
+ * file stored locally on the device and the default `MPPImageSegmenterOptions`.
+ *
+ * @param modelPath An absolute path to a TensorFlow Lite model file stored locally on the device.
+ * @param error An optional error parameter populated when there is an error in initializing the
+ * image segmenter.
+ *
+ * @return A new instance of `MPPImageSegmenter` with the given model path. `nil` if there is an
+ * error in initializing the image segmenter.
+ */
+- (nullable instancetype)initWithModelPath:(NSString *)modelPath error:(NSError **)error;
+
+/**
+ * Creates a new instance of `MPPImageSegmenter` from the given `MPPImageSegmenterOptions`.
+ *
+ * @param options The options of type `MPPImageSegmenterOptions` to use for configuring the
+ * `MPPImageSegmenter`.
+ * @param error An optional error parameter populated when there is an error in initializing the
+ * image segmenter.
+ *
+ * @return A new instance of `MPPImageSegmenter` with the given options. `nil` if there is an error
+ * in initializing the image segmenter.
+ */
+- (nullable instancetype)initWithOptions:(MPPImageSegmenterOptions *)options
+                                   error:(NSError **)error NS_DESIGNATED_INITIALIZER;
+
+/**
+ * Performs segmentation on the provided MPPImage using the whole image as region of interest.
+ * Rotation will be applied according to the `orientation` property of the provided `MPPImage`. Only
+ * use this method when the `MPPImageSegmenter` is created with `MPPRunningModeImage`.
+ *
+ * This method supports RGBA images. If your `MPPImage` has a source type of
+ * `MPPImageSourceTypePixelBuffer` or `MPPImageSourceTypeSampleBuffer`, the underlying pixel buffer
+ * must have one of the following pixel format types:
+ * 1. kCVPixelFormatType_32BGRA
+ * 2. kCVPixelFormatType_32RGBA
+ *
+ * If your `MPPImage` has a source type of `MPPImageSourceTypeImage` ensure that the color space is
+ * RGB with an Alpha channel.
+ *
+ * @param image The `MPPImage` on which segmentation is to be performed.
+ * @param error An optional error parameter populated when there is an error in performing
+ * segmentation on the input image.
+ *
+ * @return An `MPPImageSegmenterResult` that contains the segmented masks.
+ */
+- (nullable MPPImageSegmenterResult *)segmentImage:(MPPImage *)image
+                                             error:(NSError *)error NS_SWIFT_NAME(segment(image:));
+
+/**
+ * Performs segmentation on the provided MPPImage using the whole image as region of interest and
+ * invokes the given completion handler block with the response. The method returns synchronously
+ * once the completion handler returns.
+ *
+ * Rotation will be applied according to the `orientation` property of the provided
+ * `MPPImage`. Only use this method when the `MPPImageSegmenter` is created with
+ * `MPPRunningModeImage`.
+ *
+ * This method supports RGBA images. If your `MPPImage` has a source type of
+ * `MPPImageSourceTypePixelBuffer` or `MPPImageSourceTypeSampleBuffer`, the underlying pixel buffer
+ * must have one of the following pixel format types:
+ * 1. kCVPixelFormatType_32BGRA
+ * 2. kCVPixelFormatType_32RGBA
+ *
+ * If your `MPPImage` has a source type of `MPPImageSourceTypeImage` ensure that the color space is
+ * RGB with an Alpha channel.
+ *
+ * @param image The `MPPImage` on which segmentation is to be performed.
+ * @param completionHandler A block to be invoked with the results of performing segmentation on the
+ * image. The block takes two arguments, the optional `MPPImageSegmenterResult` that contains the
+ * segmented masks if the segmentation was successful and an optional error populated upon failure.
+ * The lifetime of the returned masks is only guaranteed for the duration of the block.
+ */
+- (void)segmentImage:(MPPImage *)image
+    withCompletionHandler:((void ^)(MPPImageSegmenterResult *_Nullable result,
+                                    NSError *_Nullable error))completionHandler
+    NS_SWIFT_NAME(segment(image:completion:));
+
+/**
+ * Performs segmentation on the provided video frame of type `MPPImage` using the whole image as
+ * region of interest.
+ *
+ * Rotation will be applied according to the `orientation` property of the provided `MPPImage`. Only
+ * use this method when the `MPPImageSegmenter` is created with `MPPRunningModeVideo`.
+ *
+ * This method supports RGBA images. If your `MPPImage` has a source type of
+ * `MPPImageSourceTypePixelBuffer` or `MPPImageSourceTypeSampleBuffer`, the underlying pixel buffer
+ * must have one of the following pixel format types:
+ * 1. kCVPixelFormatType_32BGRA
+ * 2. kCVPixelFormatType_32RGBA
+ *
+ * If your `MPPImage` has a source type of `MPPImageSourceTypeImage` ensure that the color space is
+ * RGB with an Alpha channel.
+ *
+ * @param image The `MPPImage` on which segmentation is to be performed.
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
+ * timestamps must be monotonically increasing.
+ * @param error An optional error parameter populated when there is an error in performing
+ * segmentation on the input image.
+ *
+ * @return An `MPPImageSegmenterResult` that contains a the segmented masks.
+ */
+- (nullable MPPImageSegmenterResult *)segmentVideoFrame:(MPPImage *)image
+                                timestampInMilliseconds:(NSInteger)timestampInMilliseconds
+                                                  error:(NSError **)error
+    NS_SWIFT_NAME(segment(videoFrame:timestampInMilliseconds:));
+
+/**
+ * Performs segmentation on the provided video frame of type `MPPImage` using the whole image as
+ * region of interest invokes the given completion handler block with the response. The method
+ * returns synchronously once the completion handler returns.
+ *
+ * Rotation will be applied according to the `orientation` property of the provided `MPPImage`. Only
+ * use this method when the `MPPImageSegmenter` is created with `MPPRunningModeVideo`.
+ *
+ * This method supports RGBA images. If your `MPPImage` has a source type of
+ * `MPPImageSourceTypePixelBuffer` or `MPPImageSourceTypeSampleBuffer`, the underlying pixel buffer
+ * must have one of the following pixel format types:
+ * 1. kCVPixelFormatType_32BGRA
+ * 2. kCVPixelFormatType_32RGBA
+ *
+ * If your `MPPImage` has a source type of `MPPImageSourceTypeImage` ensure that the color space is
+ * RGB with an Alpha channel.
+ *
+ * @param image The `MPPImage` on which segmentation is to be performed.
+ * @param timestampInMilliseconds The video frame's timestamp (in milliseconds). The input
+ * timestamps must be monotonically increasing.
+ * @param completionHandler A block to be invoked with the results of performing segmentation on the
+ * image. The block takes two arguments, the optional `MPPImageSegmenterResult` that contains the
+ * segmented masks if the segmentation was successful and an optional error only populated upon
+ * failure. The lifetime of the returned masks is only guaranteed for the duration of the block.
+ */
+- (void)segmentVideoFrame:(MPPImage *)image
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
+      withCompletionHandler:((void ^)(MPPImageSegmenterResult *_Nullable result,
+                                      NSError *_Nullable error))completionHandler
+    NS_SWIFT_NAME(segment(videoFrame:timestampInMilliseconds:completion:));
+
+/**
+ * Sends live stream image data of type `MPPImage` to perform segmentation using the whole image as
+ * region of interest.
+ *
+ * Rotation will be applied according to the `orientation` property of the provided `MPPImage`. Only
+ * use this method when the `MPPImageSegmenter` is created with`MPPRunningModeLiveStream`.
+ *
+ * The object which needs to be continuously notified of the available results of image segmentation
+ * must confirm to `MPPImageSegmenterLiveStreamDelegate` protocol and implement the
+ *`imageSegmenter:didFinishSegmentationWithResult:timestampInMilliseconds:error:` delegate method.
+ *
+ * It's required to provide a timestamp (in milliseconds) to indicate when the input image is sent
+ * to the segmenter. The input timestamps must be monotonically increasing.
+ *
+ * This method supports RGBA images. If your `MPPImage` has a source type of
+ *`MPPImageSourceTypePixelBuffer` or `MPPImageSourceTypeSampleBuffer`, the underlying pixel buffer
+ * must have one of the following pixel format types:
+ * 1. kCVPixelFormatType_32BGRA
+ * 2. kCVPixelFormatType_32RGBA
+ *
+ * If the input `MPPImage` has a source type of `MPPImageSourceTypeImage` ensure that the color
+ * space is RGB with an Alpha channel.
+ *
+ * If this method is used for classifying live camera frames using `AVFoundation`, ensure that you
+ * request `AVCaptureVideoDataOutput` to output frames in `kCMPixelFormat_32RGBA` using its
+ * `videoSettings` property.
+ *
+ * @param image A live stream image data of type `MPPImage` on which segmentation is to be
+ * performed.
+ * @param timestampInMilliseconds The timestamp (in milliseconds) which indicates when the input
+ * image is sent to the segmenter. The input timestamps must be monotonically increasing.
+ * @param error An optional error parameter populated when there is an error when sending the input
+ * image to the graph.
+ *
+ * @return `YES` if the image was sent to the task successfully, otherwise `NO`.
+ */
+- (BOOL)segmentAsyncInImage:(MPPImage *)image
+    timestampInMilliseconds:(NSInteger)timestampInMilliseconds
+                      error:(NSError **)error
+    NS_SWIFT_NAME(segmentAsync(image:timestampInMilliseconds:));
+
+- (instancetype)init NS_UNAVAILABLE;
+
+ (instancetype)new NS_UNAVAILABLE;
+
+@end
+
+NS_ASSUME_NONNULL_END
@@ -92,6 +92,9 @@ android_library(
 android_library(
    name = "landmark",
    srcs = ["Landmark.java"],
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
    deps = [
        "//third_party:autovalue",
        "@maven//:com_google_guava_guava",
@@ -101,6 +104,9 @@ android_library(
 android_library(
    name = "normalized_landmark",
    srcs = ["NormalizedLandmark.java"],
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
    deps = [
        "//third_party:autovalue",
        "@maven//:com_google_guava_guava",
@@ -16,6 +16,7 @@ package com.google.mediapipe.tasks.components.containers;

 import com.google.auto.value.AutoValue;
 import java.util.Objects;
+import java.util.Optional;

 /**
 * Landmark represents a point in 3D space with x, y, z coordinates. The landmark coordinates are in
@@ -27,7 +28,12 @@ public abstract class Landmark {
  private static final float TOLERANCE = 1e-6f;

  public static Landmark create(float x, float y, float z) {
-    return new AutoValue_Landmark(x, y, z);
+    return new AutoValue_Landmark(x, y, z, Optional.empty(), Optional.empty());
+  }
+
+  public static Landmark create(
+      float x, float y, float z, Optional<Float> visibility, Optional<Float> presence) {
+    return new AutoValue_Landmark(x, y, z, visibility, presence);
  }

  // The x coordinates of the landmark.
@@ -39,6 +45,12 @@ public abstract class Landmark {
  // The z coordinates of the landmark.
  public abstract float z();

+  // Visibility of the normalized landmark.
+  public abstract Optional<Float> visibility();
+
+  // Presence of the normalized landmark.
+  public abstract Optional<Float> presence();
+
  @Override
  public final boolean equals(Object o) {
    if (!(o instanceof Landmark)) {
@@ -57,6 +69,16 @@ public abstract class Landmark {

  @Override
  public final String toString() {
-    return "<Landmark (x=" + x() + " y=" + y() + " z=" + z() + ")>";
+    return "<Landmark (x="
+        + x()
+        + " y="
+        + y()
+        + " z="
+        + z()
+        + " visibility= "
+        + visibility()
+        + " presence="
+        + presence()
+        + ")>";
  }
 }
@@ -16,6 +16,7 @@ package com.google.mediapipe.tasks.components.containers;

 import com.google.auto.value.AutoValue;
 import java.util.Objects;
+import java.util.Optional;

 /**
 * Normalized Landmark represents a point in 3D space with x, y, z coordinates. x and y are
@@ -28,7 +29,12 @@ public abstract class NormalizedLandmark {
  private static final float TOLERANCE = 1e-6f;

  public static NormalizedLandmark create(float x, float y, float z) {
-    return new AutoValue_NormalizedLandmark(x, y, z);
+    return new AutoValue_NormalizedLandmark(x, y, z, Optional.empty(), Optional.empty());
+  }
+
+  public static NormalizedLandmark create(
+      float x, float y, float z, Optional<Float> visibility, Optional<Float> presence) {
+    return new AutoValue_NormalizedLandmark(x, y, z, visibility, presence);
  }

  // The x coordinates of the normalized landmark.
@@ -40,6 +46,12 @@ public abstract class NormalizedLandmark {
  // The z coordinates of the normalized landmark.
  public abstract float z();

+  // Visibility of the normalized landmark.
+  public abstract Optional<Float> visibility();
+
+  // Presence of the normalized landmark.
+  public abstract Optional<Float> presence();
+
  @Override
  public final boolean equals(Object o) {
    if (!(o instanceof NormalizedLandmark)) {
@@ -58,6 +70,16 @@ public abstract class NormalizedLandmark {

  @Override
  public final String toString() {
-    return "<Normalized Landmark (x=" + x() + " y=" + y() + " z=" + z() + ")>";
+    return "<Normalized Landmark (x="
+        + x()
+        + " y="
+        + y()
+        + " z="
+        + z()
+        + " visibility= "
+        + visibility()
+        + " presence="
+        + presence()
+        + ")>";
  }
 }
@@ -53,7 +53,15 @@ public abstract class FaceLandmarkerResult implements TaskResult {
          faceLandmarksProto.getLandmarkList()) {
        faceLandmarks.add(
            NormalizedLandmark.create(
-                faceLandmarkProto.getX(), faceLandmarkProto.getY(), faceLandmarkProto.getZ()));
+                faceLandmarkProto.getX(),
+                faceLandmarkProto.getY(),
+                faceLandmarkProto.getZ(),
+                faceLandmarkProto.hasVisibility()
+                    ? Optional.of(faceLandmarkProto.getVisibility())
+                    : Optional.empty(),
+                faceLandmarkProto.hasPresence()
+                    ? Optional.of(faceLandmarkProto.getPresence())
+                    : Optional.empty()));
      }
    }
    Optional<List<List<Category>>> multiFaceBlendshapes = Optional.empty();
@@ -25,6 +25,7 @@ import com.google.mediapipe.tasks.core.TaskResult;
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.List;
+import java.util.Optional;

 /** Represents the hand landmarks deection results generated by {@link HandLandmarker}. */
@AutoValue
@@ -53,7 +54,15 @@ public abstract class HandLandmarkerResult implements TaskResult {
          handLandmarksProto.getLandmarkList()) {
        handLandmarks.add(
            NormalizedLandmark.create(
-                handLandmarkProto.getX(), handLandmarkProto.getY(), handLandmarkProto.getZ()));
+                handLandmarkProto.getX(),
+                handLandmarkProto.getY(),
+                handLandmarkProto.getZ(),
+                handLandmarkProto.hasVisibility()
+                    ? Optional.of(handLandmarkProto.getVisibility())
+                    : Optional.empty(),
+                handLandmarkProto.hasPresence()
+                    ? Optional.of(handLandmarkProto.getPresence())
+                    : Optional.empty()));
      }
    }
    for (LandmarkProto.LandmarkList handWorldLandmarksProto : worldLandmarksProto) {
@@ -65,7 +74,13 @@ public abstract class HandLandmarkerResult implements TaskResult {
            com.google.mediapipe.tasks.components.containers.Landmark.create(
                handWorldLandmarkProto.getX(),
                handWorldLandmarkProto.getY(),
-                handWorldLandmarkProto.getZ()));
+                handWorldLandmarkProto.getZ(),
+                handWorldLandmarkProto.hasVisibility()
+                    ? Optional.of(handWorldLandmarkProto.getVisibility())
+                    : Optional.empty(),
+                handWorldLandmarkProto.hasPresence()
+                    ? Optional.of(handWorldLandmarkProto.getPresence())
+                    : Optional.empty()));
      }
    }
    for (ClassificationList handednessProto : handednessesProto) {
@@ -58,7 +58,15 @@ public abstract class PoseLandmarkerResult implements TaskResult {
          poseLandmarksProto.getLandmarkList()) {
        poseLandmarks.add(
            NormalizedLandmark.create(
-                poseLandmarkProto.getX(), poseLandmarkProto.getY(), poseLandmarkProto.getZ()));
+                poseLandmarkProto.getX(),
+                poseLandmarkProto.getY(),
+                poseLandmarkProto.getZ(),
+                poseLandmarkProto.hasVisibility()
+                    ? Optional.of(poseLandmarkProto.getVisibility())
+                    : Optional.empty(),
+                poseLandmarkProto.hasPresence()
+                    ? Optional.of(poseLandmarkProto.getPresence())
+                    : Optional.empty()));
      }
    }
    for (LandmarkProto.LandmarkList poseWorldLandmarksProto : worldLandmarksProto) {
@@ -70,7 +78,13 @@ public abstract class PoseLandmarkerResult implements TaskResult {
            Landmark.create(
                poseWorldLandmarkProto.getX(),
                poseWorldLandmarkProto.getY(),
-                poseWorldLandmarkProto.getZ()));
+                poseWorldLandmarkProto.getZ(),
+                poseWorldLandmarkProto.hasVisibility()
+                    ? Optional.of(poseWorldLandmarkProto.getVisibility())
+                    : Optional.empty(),
+                poseWorldLandmarkProto.hasPresence()
+                    ? Optional.of(poseWorldLandmarkProto.getPresence())
+                    : Optional.empty()));
      }
    }
    return new AutoValue_PoseLandmarkerResult(
@@ -140,7 +140,7 @@ public class TextEmbedderTest {
        TextEmbedder.cosineSimilarity(
            result0.embeddingResult().embeddings().get(0),
            result1.embeddingResult().embeddings().get(0));
-    assertThat(similarity).isWithin(DOUBLE_DIFF_TOLERANCE).of(0.3477488707202946);
+    assertThat(similarity).isWithin(DOUBLE_DIFF_TOLERANCE).of(0.3565317439544432);
  }

  @Test
@@ -15,6 +15,7 @@
 package com.google.mediapipe.tasks.vision.poselandmarker;

 import static com.google.common.truth.Truth.assertThat;
+import static com.google.common.truth.Truth.assertWithMessage;
 import static org.junit.Assert.assertThrows;

 import android.content.res.AssetManager;
@@ -26,6 +27,7 @@ import com.google.common.truth.Correspondence;
 import com.google.mediapipe.framework.MediaPipeException;
 import com.google.mediapipe.framework.image.BitmapImageBuilder;
 import com.google.mediapipe.framework.image.MPImage;
+import com.google.mediapipe.tasks.components.containers.Landmark;
 import com.google.mediapipe.tasks.components.containers.NormalizedLandmark;
 import com.google.mediapipe.tasks.components.containers.proto.LandmarksDetectionResultProto.LandmarksDetectionResult;
 import com.google.mediapipe.tasks.core.BaseOptions;
@@ -34,6 +36,7 @@ import com.google.mediapipe.tasks.vision.core.RunningMode;
 import com.google.mediapipe.tasks.vision.poselandmarker.PoseLandmarker.PoseLandmarkerOptions;
 import java.io.InputStream;
 import java.util.Arrays;
+import java.util.List;
 import java.util.Optional;
 import org.junit.Test;
 import org.junit.runner.RunWith;
@@ -50,6 +53,8 @@ public class PoseLandmarkerTest {
  private static final String NO_POSES_IMAGE = "burger.jpg";
  private static final String TAG = "Pose Landmarker Test";
  private static final float LANDMARKS_ERROR_TOLERANCE = 0.03f;
+  private static final float VISIBILITY_TOLERANCE = 0.9f;
+  private static final float PRESENCE_TOLERANCE = 0.9f;
  private static final int IMAGE_WIDTH = 1000;
  private static final int IMAGE_HEIGHT = 667;

@@ -70,6 +75,8 @@ public class PoseLandmarkerTest {
      PoseLandmarkerResult actualResult = poseLandmarker.detect(getImageFromAsset(POSE_IMAGE));
      PoseLandmarkerResult expectedResult = getExpectedPoseLandmarkerResult(POSE_LANDMARKS);
      assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+      assertAllLandmarksAreVisibleAndPresent(
+          actualResult, VISIBILITY_TOLERANCE, PRESENCE_TOLERANCE);
    }

    @Test
@@ -361,4 +368,40 @@ public class PoseLandmarkerTest {
    assertThat(inputImage.getWidth()).isEqualTo(IMAGE_WIDTH);
    assertThat(inputImage.getHeight()).isEqualTo(IMAGE_HEIGHT);
  }
+
+  private static void assertAllLandmarksAreVisibleAndPresent(
+      PoseLandmarkerResult result, float visbilityThreshold, float presenceThreshold) {
+    for (int i = 0; i < result.landmarks().size(); i++) {
+      List<NormalizedLandmark> landmarks = result.landmarks().get(i);
+      for (int j = 0; j < landmarks.size(); j++) {
+        NormalizedLandmark landmark = landmarks.get(j);
+        String landmarkMessage = "Landmark List " + i + " landmark " + j + ": " + landmark;
+        landmark
+            .visibility()
+            .ifPresent(
+                val ->
+                    assertWithMessage(landmarkMessage).that(val).isAtLeast((visbilityThreshold)));
+        landmark
+            .presence()
+            .ifPresent(
+                val -> assertWithMessage(landmarkMessage).that(val).isAtLeast((presenceThreshold)));
+      }
+    }
+    for (int i = 0; i < result.worldLandmarks().size(); i++) {
+      List<Landmark> landmarks = result.worldLandmarks().get(i);
+      for (int j = 0; j < landmarks.size(); j++) {
+        Landmark landmark = landmarks.get(j);
+        String landmarkMessage = "World Landmark List " + i + " landmark " + j + ": " + landmark;
+        landmark
+            .visibility()
+            .ifPresent(
+                val ->
+                    assertWithMessage(landmarkMessage).that(val).isAtLeast((visbilityThreshold)));
+        landmark
+            .presence()
+            .ifPresent(
+                val -> assertWithMessage(landmarkMessage).that(val).isAtLeast((presenceThreshold)));
+      }
+    }
+  }
 }
@@ -37,7 +37,7 @@ _TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
 # Tolerance for embedding vector coordinate values.
 _EPSILON = 1e-4
 # Tolerance for cosine similarity evaluation.
-_SIMILARITY_TOLERANCE = 1e-6
+_SIMILARITY_TOLERANCE = 1e-3


 class ModelFileType(enum.Enum):
@@ -287,7 +287,7 @@ class TextEmbedderTest(parameterized.TestCase):

  @parameterized.parameters(
      # TODO: The similarity should likely be lower
-      (_BERT_MODEL_FILE, 0.980880),
+      (_BERT_MODEL_FILE, 0.98077),
      (_USE_MODEL_FILE, 0.780334),
  )
  def test_embed_with_different_themes(self, model_file, expected_similarity):
@@ -57,6 +57,7 @@ mediapipe_files(srcs = [
    "hand_landmarker.task",
    "left_hands.jpg",
    "left_hands_rotated.jpg",
+    "leopard_bg_removal_result_512x512.png",
    "mobilenet_v1_0.25_192_quantized_1_default_1.tflite",
    "mobilenet_v1_0.25_224_1_default_1.tflite",
    "mobilenet_v1_0.25_224_1_metadata_1.tflite",
@@ -65,6 +66,7 @@ mediapipe_files(srcs = [
    "mobilenet_v1_0.25_224_quant_without_subgraph_metadata.tflite",
    "mobilenet_v2_1.0_224.tflite",
    "mobilenet_v3_small_100_224_embedder.tflite",
+    "mobilenetsweep_dptrigmqn384_unit_384_384_fp16quant_fp32input_opt.tflite",
    "mozart_square.jpg",
    "multi_objects.jpg",
    "multi_objects_rotated.jpg",
@@ -136,6 +138,7 @@ filegroup(
        "hand_landmark_lite.tflite",
        "left_hands.jpg",
        "left_hands_rotated.jpg",
+        "leopard_bg_removal_result_512x512.png",
        "mozart_square.jpg",
        "multi_objects.jpg",
        "multi_objects_rotated.jpg",
@@ -555,9 +555,9 @@ without timestamps, use the `context`.
 |`PREFIX/feature/dimensions`|context int list|`set_feature_dimensions` / `SetFeatureDimensions`|A list of integer dimensions for each feature.|
 |`PREFIX/feature/rate`|context float|`set_feature_rate` / `SetFeatureRate`|The rate that features are calculated as features per second.|
 |`PREFIX/feature/bytes/format`|context bytes|`set_feature_bytes_format` / `SetFeatureBytesFormat`|The encoding format if any for features stored as bytes.|
-|`PREFIX/context_feature/floats`|context float list|`add_context_feature_floats` / `AddContextFeatureFloats`|A list of floats for the entire example.|
-|`PREFIX/context_feature/bytes`|context bytes list|`add_context_feature_bytes` / `AddContextFeatureBytes`|A list of bytes for the entire example. Maybe be encoded.|
-|`PREFIX/context_feature/ints`|context int list|`add_context_feature_ints` / `AddContextFeatureInts`|A list of ints for the entire example.|
+|`PREFIX/context_feature/floats`|context float list|`set_context_feature_floats` / `AddContextFeatureFloats`|A list of floats for the entire example.|
+|`PREFIX/context_feature/bytes`|context bytes list|`set_context_feature_bytes` / `AddContextFeatureBytes`|A list of bytes for the entire example. Maybe be encoded.|
+|`PREFIX/context_feature/ints`|context int list|`set_context_feature_ints` / `AddContextFeatureInts`|A list of ints for the entire example.|

 ### Keys related to audio
 Audio is a special subtype of generic features with additional data about the
@@ -379,9 +379,9 @@ java_library(
    ],
 )

-java_proto_library(
+java_import(
    name = "any_java_proto",
-    deps = [
-        "@com_google_protobuf//:any_proto",
+    jars = [
+        "@com_google_protobuf//java/core:libcore.jar",
    ],
 )
@@ -39,3 +39,30 @@ index 4028ccc..483e639 100644
   if (append_newline) {
     // Fix the ostrstream back how it was before we screwed with it.
     // It's 99.44% certain that we don't need to worry about doing this.
+
+diff --git a/bazel/glog.bzl b/bazel/glog.bzl
+index dacd934..d7b3d78 100644
+--- a/bazel/glog.bzl
+++ b/bazel/glog.bzl
+@@ -53,7 +53,6 @@ def glog_library(namespace = "google", with_gflags = 1, **kwargs):
+     )
+ 
+     common_copts = [
+-        "-std=c++14",
+         "-DGLOG_BAZEL_BUILD",
+         # Inject a C++ namespace.
+         "-DGOOGLE_NAMESPACE='%s'" % namespace,
+@@ -145,7 +144,13 @@ def glog_library(namespace = "google", with_gflags = 1, **kwargs):
+         ],
+     })
+ 
+    c14_opts = ["-std=c++14"]
+    c17_opts = ["-std=c++17"]
+
+     final_lib_copts = select({
+        "@bazel_tools//src/conditions:windows": c17_opts,
+         "//conditions:default": c14_opts,
+    }) + select({
+         "@bazel_tools//src/conditions:windows": common_copts + windows_only_copts,
+         "@bazel_tools//src/conditions:darwin": common_copts + linux_or_darwin_copts + darwin_only_copts,
+         "@bazel_tools//src/conditions:freebsd": common_copts + linux_or_darwin_copts + freebsd_only_copts,
@@ -646,6 +646,12 @@ def external_files():
        urls = ["https://storage.googleapis.com/mediapipe-assets/left_hands_rotated.jpg?generation=1666037068103465"],
    )

+    http_file(
+        name = "com_google_mediapipe_leopard_bg_removal_result_512x512_png",
+        sha256 = "30be22e89fdd1d7b985294498ec67509b0caa1ca941fe291fa25f43a3873e4dd",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/leopard_bg_removal_result_512x512.png?generation=1690239134617707"],
+    )
+
    http_file(
        name = "com_google_mediapipe_leopard_bg_removal_result_png",
        sha256 = "afd33f2058fd58d189cda86ec931647741a6139970c9bcbc637cdd151ec657c5",
@@ -712,6 +718,12 @@ def external_files():
        urls = ["https://storage.googleapis.com/mediapipe-assets/mobile_ica_8bit-with-unsupported-metadata-version.tflite?generation=1661875819091013"],
    )

+    http_file(
+        name = "com_google_mediapipe_mobilenetsweep_dptrigmqn384_unit_384_384_fp16quant_fp32input_opt_tflite",
+        sha256 = "3c4c7e36b35fc903ecfb51b351b4849b23c57cc18d1416cf6cabaa1522d84760",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/mobilenetsweep_dptrigmqn384_unit_384_384_fp16quant_fp32input_opt.tflite?generation=1690302146106240"],
+    )
+
    http_file(
        name = "com_google_mediapipe_mobilenet_v1_0_25_192_quantized_1_default_1_tflite",
        sha256 = "f80999b6324c6f101300c3ee38fbe7e11e74a743b5e0be7350602087fe7430a3",
@@ -12,72 +12,72 @@ def wasm_files():

    http_file(
        name = "com_google_mediapipe_wasm_audio_wasm_internal_js",
-        sha256 = "0a6d057ead24a09f116dd388146b1614f5e12559a88eb3d141e93d3f8193a29d",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1688751355212943"],
+        sha256 = "9e5f88363212ac1ad505a0b9e59e3dd34413064f3b70219ff8b0216d6a53128f",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1690577772170421"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_audio_wasm_internal_wasm",
-        sha256 = "3c475f7420f4fe5382d7123c6f5fb21fe08e2bc47e2acbc5aefd82ab589f2850",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1688751357824803"],
+        sha256 = "8e4c7e9efcfe0d1107b40626f14070f17a817d2b830205ae642ea645fa882d28",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1690577774642876"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_js",
-        sha256 = "e92c7630cd873b2a3984c41287b65a338d56806baaddd2b6261bddbb4b5f2ea2",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1688751360158457"],
+        sha256 = "9b9d1fbbead06a26461bb664189d46f0c327a1077e67f0aeeb0628d04de13a81",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1690577777075565"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_wasm",
-        sha256 = "b1445e29bc187f53f6b36da1b9ce505351b4931f16fbc8aa8b34f082dde3becf",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1688751362506882"],
+        sha256 = "44734a8fdb979eb9359de0c0282565d74cdced5d3a6687be849875e0eb11503c",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1690577779811164"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_text_wasm_internal_js",
-        sha256 = "095161b74dca1991d15483b9525433853c4b141e5682ca0b32f42fba7ec92ed2",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1688751364517949"],
+        sha256 = "93275ebbae8dd2e9be0394391b722a0de5ac9ed51066093b1ac6ec24bebf5813",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1690577782193422"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_text_wasm_internal_wasm",
-        sha256 = "157b3e32546e5ff6a223d2f137a4f52e89ff28c95236a5ffd9baf185559bc3f9",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1688751366879784"],
+        sha256 = "35e734890cae0c51c1ad91e3589d5777b013bcbac64a5bcbb3a67ce4a5815dd6",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1690577784996034"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_js",
-        sha256 = "beae70d5a1a2975cada2d8acbf291ee17a298a75018b1918405e8d6029458231",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1688751369120108"],
+        sha256 = "4e6cea3ae95ffac595bfc08f0dab4ff452c91434eb71f92c0dd34250a46825a1",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1690577787398460"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_wasm",
-        sha256 = "1223d5069ba1fa70a585a193d3d5f9bf990d043c0a1de03544ad2869daa8f03c",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1688751371734691"],
+        sha256 = "43cfab25c1d47822015e434d726a80d84e0bfdb5e685a511ab45d8b5cbe944d3",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1690577790301890"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_vision_wasm_internal_js",
-        sha256 = "8f97c81a2e15065828ca3877aaff90f870e15b628e902e453f28c8c59c373c8b",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1688751373720358"],
+        sha256 = "6a73602a14484297690e69d716e683341b62a5fde8f5debde78de2651cb69bbe",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1690577792657082"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_vision_wasm_internal_wasm",
-        sha256 = "a007d064939cf4f447416e1e5a777fcabe1413346e1c65982329d05b7472bbc8",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1688751376340177"],
+        sha256 = "3431f70071f3980bf13e638551e9bb333335223e35542ee768db06501f7a26f2",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1690577795814175"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_js",
-        sha256 = "42e2ed5d23a36a607f81bc8f6a6801806887b4d284b520b04777230000682592",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1688751378413876"],
+        sha256 = "ece9ac1f41b93340b08682514ca291431ff7084c858caf6455e65b0c6c3eb717",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1690577798226032"],
    )

    http_file(
        name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_wasm",
-        sha256 = "2c246638f29add7cc06bc65be3c5f9eddf66296a83a90a9b697c3f6281184b9c",
-        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1688751380722112"],
+        sha256 = "4d54739714db6b3d0fbdd0608c2824c4ccceaaf279aa4ba160f2eab2663b30f2",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1690577801077668"],
    )
Author	SHA1	Message	Date
Sebastian SchmidtandCopybara-Service	d392f8ad98	Ensure that -std=c++14/17 is the first argument passed to Glog PiperOrigin-RevId: 552509553	2023-07-31 09:47:32 -07:00
Sebastian SchmidtandCopybara-Service	81cf7fa173	Updat WASM binaries for 0.10.3 release PiperOrigin-RevId: 551975834	2023-07-28 16:17:30 -07:00
MediaPipe TeamandCopybara-Service	8e313b4b0c	Fix typo in model maker requirements.txt PiperOrigin-RevId: 551973577	2023-07-28 16:07:12 -07:00
Sebastian SchmidtandCopybara-Service	b4bcfab4f5	Remove extra letter from text classifier API PiperOrigin-RevId: 551942087	2023-07-28 13:56:56 -07:00
Sebastian SchmidtandCopybara-Service	8ab9185c1d	Use C+++ 17 for Glog only on Windows PiperOrigin-RevId: 551928369	2023-07-28 12:58:42 -07:00
MediaPipe TeamandCopybara-Service	3f7752561b	No public description PiperOrigin-RevId: 551914786	2023-07-28 12:05:38 -07:00
MediaPipe TeamandCopybara-Service	9edb059d9f	No public description PiperOrigin-RevId: 551868738	2023-07-28 09:09:39 -07:00
MediaPipe TeamandCopybara-Service	7db0c1944b	Internal change PiperOrigin-RevId: 551789915	2023-07-28 02:29:52 -07:00
MediaPipe TeamandCopybara-Service	db9a72a5df	Internal Changes PiperOrigin-RevId: 551674542	2023-07-27 16:35:35 -07:00
MediaPipe TeamandCopybara-Service	5c007558f8	internal change. PiperOrigin-RevId: 551645248	2023-07-27 14:45:18 -07:00
Sebastian SchmidtandCopybara-Service	4d5c6bd33a	Internal PiperOrigin-RevId: 551625147	2023-07-27 13:33:14 -07:00
Sebastian SchmidtandCopybara-Service	fdea10d230	Add C Headers for Text Classifier PiperOrigin-RevId: 551618765	2023-07-27 13:09:49 -07:00
MediaPipe TeamandCopybara-Service	5b31f1e3e9	Update glog to latest commit PiperOrigin-RevId: 551601991	2023-07-27 12:07:20 -07:00
Sebastian SchmidtandCopybara-Service	7d9cb4ee67	No public description PiperOrigin-RevId: 551586945	2023-07-27 11:17:54 -07:00
MediaPipe TeamandCopybara-Service	f3f9e71ccb	No public description PiperOrigin-RevId: 551549511	2023-07-27 09:15:46 -07:00
MediaPipe TeamandCopybara-Service	6de275834d	internal change. PiperOrigin-RevId: 551366789	2023-07-26 17:59:06 -07:00
Sebastian SchmidtandCopybara-Service	c9d79a0076	Rollback of "Fix duplicate condition error in :resource_util" PiperOrigin-RevId: 551332734	2023-07-26 15:31:51 -07:00
Sebastian SchmidtandCopybara-Service	dad46e1e90	Update glog to 0.6 PiperOrigin-RevId: 551330044	2023-07-26 15:20:34 -07:00
Sebastian SchmidtandCopybara-Service	f156397e8f	Fix Android build with any Protos PiperOrigin-RevId: 551325541	2023-07-26 15:04:38 -07:00
MediaPipe TeamandCopybara-Service	fa5c1b03d2	No public description PiperOrigin-RevId: 551277242	2023-07-26 12:08:33 -07:00
Sebastian SchmidtandCopybara-Service	87b925795d	Update glog to 0.6 PiperOrigin-RevId: 551269455	2023-07-26 11:41:24 -07:00
Sebastian SchmidtandCopybara-Service	750f498b14	Internal PiperOrigin-RevId: 551247471	2023-07-26 10:33:19 -07:00
MediaPipe TeamandCopybara-Service	1f6851c577	C++ Image segmenter add output size parameters. PiperOrigin-RevId: 550995124	2023-07-25 14:22:11 -07:00
MediaPipe TeamandCopybara-Service	bd7888cc0c	1. Move evaluation onto GPU/TPU hardware if available. 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	2023-07-25 14:12:26 -07:00
MediaPipe TeamandCopybara-Service	85c3fed70a	Add class weights to core hyperparameters and classifier library. PiperOrigin-RevId: 550962843	2023-07-25 12:29:46 -07:00
MediaPipe TeamandCopybara-Service	62538a9496	No public description PiperOrigin-RevId: 550954023	2023-07-25 11:57:08 -07:00
MediaPipe TeamandCopybara-Service	113c9b30c2	No public description PiperOrigin-RevId: 550616150	2023-07-24 11:09:31 -07:00
Copybara-Service	66cceddb5e	Merge pull request #4639 from priankakariatyml:ios-image-segmenter-container-utils PiperOrigin-RevId: 550608973	2023-07-24 10:46:56 -07:00
Prianka Liz Kariat	72c62f7d5d	Added iOS Image Segmenter Header	2023-07-24 20:38:16 +05:30
MediaPipe TeamandCopybara-Service	25b01784de	Fix documentation PiperOrigin-RevId: 549968822	2023-07-21 09:33:48 -07:00
MediaPipe TeamandCopybara-Service	9af637b125	Java API add visibility and presence for landmarks. PiperOrigin-RevId: 549709256	2023-07-20 12:42:01 -07:00
Copybara-Service	236a36e39a	Merge pull request #4629 from priankakariatyml:ios-vision-library-fixes PiperOrigin-RevId: 549659874	2023-07-20 09:52:11 -07:00
Prianka Liz Kariat	540f4f7fe6	Fixed swift name of iOS face landmarker delegate	2023-07-20 15:57:37 +05:30
Prianka Liz Kariat	3198ccf6a5	Added missing headers in ios vision framework build	2023-07-20 15:57:16 +05:30