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Merge branch 'google:master' into hand-landmarker-python

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Kinar R 2022-11-09 12:37:06 +05:30 committed by GitHub
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44 changed files with 1339 additions and 54 deletions
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7
mediapipe/framework/api2/builder.h
View File

@ -106,6 +106,13 @@ class MultiPort : public Single {
return Single{&GetWithAutoGrow(&vec_, index)};
}
template <typename U>
auto Cast() {
using SingleCastT =
std::invoke_result_t<decltype(&Single::template Cast<U>), Single*>;
return MultiPort<SingleCastT>(&vec_);
}
private:
std::vector<std::unique_ptr<Base>>& vec_;
};

51
mediapipe/framework/api2/builder_test.cc
View File

@ -445,6 +445,57 @@ TEST(BuilderTest, AnyTypeCanBeCast) {
EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
}
TEST(BuilderTest, MultiPortIsCastToMultiPort) {
builder::Graph graph;
builder::MultiSource<AnyType> any_input = graph.In("ANY_INPUT");
builder::MultiSource<int> int_input = any_input.Cast<int>();
builder::MultiDestination<AnyType> any_output = graph.Out("ANY_OUTPUT");
builder::MultiDestination<int> int_output = any_output.Cast<int>();
int_input >> int_output;
CalculatorGraphConfig expected =
mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
input_stream: "ANY_INPUT:__stream_0"
output_stream: "ANY_OUTPUT:__stream_0"
)pb");
EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
}
TEST(BuilderTest, MultiPortCanBeSlicedToSinglePort) {
builder::Graph graph;
builder::MultiSource<AnyType> any_multi_input = graph.In("ANY_INPUT");
builder::Source<AnyType> any_input = any_multi_input;
builder::MultiDestination<AnyType> any_multi_output = graph.Out("ANY_OUTPUT");
builder::Destination<AnyType> any_output = any_multi_output;
any_input >> any_output;
CalculatorGraphConfig expected =
mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
input_stream: "ANY_INPUT:__stream_0"
output_stream: "ANY_OUTPUT:__stream_0"
)pb");
EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
}
TEST(BuilderTest, SinglePortAccessWorksThroughSlicing) {
builder::Graph graph;
builder::Source<int> int_input = graph.In("INT_INPUT").Cast<int>();
builder::Source<AnyType> any_input = graph.In("ANY_OUTPUT");
builder::Destination<int> int_output = graph.Out("INT_OUTPUT").Cast<int>();
builder::Destination<AnyType> any_output = graph.Out("ANY_OUTPUT");
int_input >> int_output;
any_input >> any_output;
CalculatorGraphConfig expected =
mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(R"pb(
input_stream: "ANY_OUTPUT:__stream_0"
input_stream: "INT_INPUT:__stream_1"
output_stream: "ANY_OUTPUT:__stream_0"
output_stream: "INT_OUTPUT:__stream_1"
)pb");
EXPECT_THAT(graph.GetConfig(), EqualsProto(expected));
}
} // namespace test
} // namespace api2
} // namespace mediapipe

3
mediapipe/model_maker/python/core/tasks/BUILD
View File

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

67
mediapipe/model_maker/python/core/tasks/classifier.py
View File

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

13
mediapipe/model_maker/python/core/utils/BUILD
View File

@ -35,6 +35,7 @@ py_library(
name = "model_util",
srcs = ["model_util.py"],
deps = [
":file_util",
":quantization",
"//mediapipe/model_maker/python/core/data:dataset",
],
@ -50,6 +51,18 @@ py_test(
],
)
py_library(
name = "file_util",
srcs = ["file_util.py"],
)
py_test(
name = "file_util_test",
srcs = ["file_util_test.py"],
data = ["//mediapipe/model_maker/python/core/utils/testdata"],
deps = [":file_util"],
)
py_library(
name = "loss_functions",
srcs = ["loss_functions.py"],

36
mediapipe/model_maker/python/core/utils/file_util.py Normal file
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@ -0,0 +1,36 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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.
"""Utilities for files."""
import os
# resources dependency
def get_absolute_path(file_path: str) -> str:
"""Gets the absolute path of a file.
Args:
file_path: The path to a file relative to the `mediapipe` dir
Returns:
The full path of the file
"""
# Extract the file path before mediapipe/ as the `base_dir`. By joining it
# with the `path` which defines the relative path under mediapipe/, it
# yields to the absolute path of the model files directory.
cwd = os.path.dirname(__file__)
base_dir = cwd[:cwd.rfind('mediapipe')]
absolute_path = os.path.join(base_dir, file_path)
return absolute_path

29
mediapipe/model_maker/python/core/utils/file_util_test.py Normal file
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@ -0,0 +1,29 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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 os
from absl.testing import absltest
from mediapipe.model_maker.python.core.utils import file_util
class FileUtilTest(absltest.TestCase):
def test_get_absolute_path(self):
test_file = 'mediapipe/model_maker/python/core/utils/testdata/test.txt'
absolute_path = file_util.get_absolute_path(test_file)
self.assertTrue(os.path.exists(absolute_path))
if __name__ == '__main__':
absltest.main()

29
mediapipe/model_maker/python/core/utils/model_util.py
View File

@ -11,7 +11,7 @@
# 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.
"""Utilities for keras models."""
"""Utilities for models."""
from __future__ import absolute_import
from __future__ import division
@ -26,8 +26,8 @@ from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
# resources dependency
from mediapipe.model_maker.python.core.data import dataset
from mediapipe.model_maker.python.core.utils import file_util
from mediapipe.model_maker.python.core.utils import quantization
DEFAULT_SCALE, DEFAULT_ZERO_POINT = 0, 0
@ -39,11 +39,10 @@ def get_default_callbacks(
"""Gets default callbacks."""
summary_dir = os.path.join(export_dir, 'summaries')
summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
# Save checkpoint every 20 epochs.
checkpoint_path = os.path.join(export_dir, 'checkpoint')
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
checkpoint_path, save_weights_only=True, period=20)
checkpoint_path, save_weights_only=True)
return [summary_callback, checkpoint_callback]
@ -62,16 +61,26 @@ def load_keras_model(model_path: str,
Returns:
A tensorflow Keras model.
"""
# Extract the file path before mediapipe/ as the `base_dir`. By joining it
# with the `model_path` which defines the relative path under mediapipe/, it
# yields to the aboslution path of the model files directory.
cwd = os.path.dirname(__file__)
base_dir = cwd[:cwd.rfind('mediapipe')]
absolute_path = os.path.join(base_dir, model_path)
absolute_path = file_util.get_absolute_path(model_path)
return tf.keras.models.load_model(
absolute_path, custom_objects={'tf': tf}, compile=compile_on_load)
def load_tflite_model_buffer(model_path: str) -> bytearray:
"""Loads a TFLite model buffer from file.
Args:
model_path: Relative path to a TFLite file
Returns:
A TFLite model buffer
"""
absolute_path = file_util.get_absolute_path(model_path)
with tf.io.gfile.GFile(absolute_path, 'rb') as f:
tflite_model_buffer = f.read()
return tflite_model_buffer
def get_steps_per_epoch(steps_per_epoch: Optional[int] = None,
batch_size: Optional[int] = None,
train_data: Optional[dataset.Dataset] = None) -> int:

15
mediapipe/model_maker/python/core/utils/model_util_test.py
View File

@ -24,7 +24,7 @@ from mediapipe.model_maker.python.core.utils import test_util
class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
def test_load_model(self):
def test_load_keras_model(self):
input_dim = 4
model = test_util.build_model(input_shape=[input_dim], num_classes=2)
saved_model_path = os.path.join(self.get_temp_dir(), 'saved_model')
@ -36,6 +36,19 @@ class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
loaded_model_output = loaded_model.predict_on_batch(input_tensors)
self.assertTrue((model_output == loaded_model_output).all())
def test_load_tflite_model_buffer(self):
input_dim = 4
model = test_util.build_model(input_shape=[input_dim], num_classes=2)
tflite_model = model_util.convert_to_tflite(model)
tflite_file = os.path.join(self.get_temp_dir(), 'model.tflite')
model_util.save_tflite(tflite_model=tflite_model, tflite_file=tflite_file)
tflite_model_buffer = model_util.load_tflite_model_buffer(tflite_file)
test_util.test_tflite(
keras_model=model,
tflite_model=tflite_model_buffer,
size=[1, input_dim])
@parameterized.named_parameters(
dict(
testcase_name='input_only_steps_per_epoch',

23
mediapipe/model_maker/python/core/utils/testdata/BUILD vendored Normal file
View File

@ -0,0 +1,23 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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/model_maker/python/core/utils:__subpackages__"],
licenses = ["notice"], # Apache 2.0
)
filegroup(
name = "testdata",
srcs = ["test.txt"],
)

0
mediapipe/model_maker/python/core/utils/testdata/test.txt vendored Normal file
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7
mediapipe/model_maker/python/vision/image_classifier/image_classifier.py
View File

@ -68,7 +68,10 @@ class ImageClassifier(classifier.Classifier):
) -> 'ImageClassifier':
"""Creates and trains an image classifier.
Loads data and trains the model based on data for image classification.
Loads data and trains the model based on data for image classification. If a
checkpoint file exists in the {options.hparams.export_dir}/checkpoint/
directory, the training process will load the weight from the checkpoint
file for continual training.
Args:
train_data: Training data.
@ -190,7 +193,7 @@ class ImageClassifier(classifier.Classifier):
tflite_model,
self._model_spec.mean_rgb,
self._model_spec.stddev_rgb,
labels=metadata_writer.Labels().add(self._label_names))
labels=metadata_writer.Labels().add(list(self._label_names)))
tflite_model_with_metadata, metadata_json = writer.populate()
model_util.save_tflite(tflite_model_with_metadata, tflite_file)
with open(metadata_file, 'w') as f:

53
mediapipe/model_maker/python/vision/image_classifier/image_classifier_test.py
View File

@ -13,10 +13,13 @@
# limitations under the License.
import filecmp
import io
import os
import tempfile
from unittest import mock
from unittest import mock as unittest_mock
from absl.testing import parameterized
import mock
import numpy as np
import tensorflow as tf
@ -63,14 +66,20 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
options=image_classifier.ImageClassifierOptions(
supported_model=image_classifier.SupportedModels.MOBILENET_V2,
hparams=image_classifier.HParams(
epochs=1, batch_size=1, shuffle=True))),
epochs=1,
batch_size=1,
shuffle=True,
export_dir=tempfile.mkdtemp()))),
dict(
testcase_name='efficientnet_lite0',
options=image_classifier.ImageClassifierOptions(
supported_model=(
image_classifier.SupportedModels.EFFICIENTNET_LITE0),
hparams=image_classifier.HParams(
epochs=1, batch_size=1, shuffle=True))),
epochs=1,
batch_size=1,
shuffle=True,
export_dir=tempfile.mkdtemp()))),
dict(
testcase_name='efficientnet_lite0_change_dropout_rate',
options=image_classifier.ImageClassifierOptions(
@ -78,21 +87,30 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
image_classifier.SupportedModels.EFFICIENTNET_LITE0),
model_options=image_classifier.ModelOptions(dropout_rate=0.1),
hparams=image_classifier.HParams(
epochs=1, batch_size=1, shuffle=True))),
epochs=1,
batch_size=1,
shuffle=True,
export_dir=tempfile.mkdtemp()))),
dict(
testcase_name='efficientnet_lite2',
options=image_classifier.ImageClassifierOptions(
supported_model=(
image_classifier.SupportedModels.EFFICIENTNET_LITE2),
hparams=image_classifier.HParams(
epochs=1, batch_size=1, shuffle=True))),
epochs=1,
batch_size=1,
shuffle=True,
export_dir=tempfile.mkdtemp()))),
dict(
testcase_name='efficientnet_lite4',
options=image_classifier.ImageClassifierOptions(
supported_model=(
image_classifier.SupportedModels.EFFICIENTNET_LITE4),
hparams=image_classifier.HParams(
epochs=1, batch_size=1, shuffle=True))),
epochs=1,
batch_size=1,
shuffle=True,
export_dir=tempfile.mkdtemp()))),
)
def test_create_and_train_model(
self, options: image_classifier.ImageClassifierOptions):
@ -117,16 +135,35 @@ class ImageClassifierTest(tf.test.TestCase, parameterized.TestCase):
self.assertGreater(os.path.getsize(output_metadata_file), 0)
self.assertTrue(filecmp.cmp(output_metadata_file, expected_metadata_file))
def test_continual_training_by_loading_checkpoint(self):
mock_stdout = io.StringIO()
with mock.patch('sys.stdout', mock_stdout):
options = image_classifier.ImageClassifierOptions(
supported_model=image_classifier.SupportedModels.EFFICIENTNET_LITE0,
hparams=image_classifier.HParams(
epochs=5, batch_size=1, shuffle=True))
model = image_classifier.ImageClassifier.create(
train_data=self._train_data,
validation_data=self._test_data,
options=options)
model = image_classifier.ImageClassifier.create(
train_data=self._train_data,
validation_data=self._test_data,
options=options)
self._test_accuracy(model)
self.assertRegex(mock_stdout.getvalue(), 'Resuming from')
def _test_accuracy(self, model, threshold=0.0):
_, accuracy = model.evaluate(self._test_data)
self.assertGreaterEqual(accuracy, threshold)
@mock.patch.object(
@unittest_mock.patch.object(
image_classifier.hyperparameters,
'HParams',
autospec=True,
return_value=image_classifier.HParams(epochs=1))
@mock.patch.object(
@unittest_mock.patch.object(
image_classifier.model_options,
'ImageClassifierModelOptions',
autospec=True,

18
mediapipe/model_maker/python/vision/image_classifier/train_image_classifier_lib.py
View File

@ -13,6 +13,7 @@
# limitations under the License.
"""Library to train model."""
import os
import tensorflow as tf
from mediapipe.model_maker.python.core.utils import model_util
@ -78,11 +79,24 @@ def train_model(model: tf.keras.Model, hparams: hp.HParams,
loss = tf.keras.losses.CategoricalCrossentropy(
label_smoothing=hparams.label_smoothing)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
callbacks = model_util.get_default_callbacks(export_dir=hparams.export_dir)
summary_dir = os.path.join(hparams.export_dir, 'summaries')
summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
# Save checkpoint every 5 epochs.
checkpoint_path = os.path.join(hparams.export_dir, 'checkpoint')
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
os.path.join(checkpoint_path, 'model-{epoch:04d}'),
save_weights_only=True,
period=5)
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
if latest_checkpoint:
print(f'Resuming from {latest_checkpoint}')
model.load_weights(latest_checkpoint)
# Train the model.
return model.fit(
x=train_ds,
epochs=hparams.epochs,
validation_data=validation_ds,
callbacks=callbacks)
callbacks=[summary_callback, checkpoint_callback])

87
mediapipe/tasks/cc/text/text_embedder/BUILD Normal file
View File

@ -0,0 +1,87 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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_embedder",
srcs = ["text_embedder.cc"],
hdrs = ["text_embedder.h"],
deps = [
":text_embedder_graph",
"//mediapipe/calculators/tensor:inference_calculator_cc_proto",
"//mediapipe/framework:calculator_cc_proto",
"//mediapipe/framework/api2:builder",
"//mediapipe/tasks/cc/components/containers:embedding_result",
"//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
"//mediapipe/tasks/cc/components/processors:embedder_options",
"//mediapipe/tasks/cc/components/processors/proto:embedder_options_cc_proto",
"//mediapipe/tasks/cc/components/utils:cosine_similarity",
"//mediapipe/tasks/cc/core:base_options",
"//mediapipe/tasks/cc/core:base_task_api",
"//mediapipe/tasks/cc/core:task_api_factory",
"//mediapipe/tasks/cc/core/proto:base_options_cc_proto",
"//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_cc_proto",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@com_google_absl//absl/strings",
],
)
cc_library(
name = "text_embedder_graph",
srcs = ["text_embedder_graph.cc"],
deps = [
"//mediapipe/calculators/tensor:inference_calculator",
"//mediapipe/calculators/tensor:inference_calculator_cc_proto",
"//mediapipe/framework:calculator_cc_proto",
"//mediapipe/framework:calculator_framework",
"//mediapipe/framework/api2:builder",
"//mediapipe/framework/api2:port",
"//mediapipe/tasks/cc/components:text_preprocessing_graph",
"//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto",
"//mediapipe/tasks/cc/components/processors:embedding_postprocessing_graph",
"//mediapipe/tasks/cc/components/processors/proto:embedding_postprocessing_graph_options_cc_proto",
"//mediapipe/tasks/cc/components/proto:text_preprocessing_graph_options_cc_proto",
"//mediapipe/tasks/cc/core:model_resources",
"//mediapipe/tasks/cc/core:model_task_graph",
"//mediapipe/tasks/cc/core/proto:model_resources_calculator_cc_proto",
"//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_cc_proto",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
],
alwayslink = 1,
)
cc_test(
name = "text_embedder_test",
srcs = ["text_embedder_test.cc"],
data = [
"//mediapipe/tasks/testdata/text:mobilebert_embedding_model",
"//mediapipe/tasks/testdata/text:regex_embedding_with_metadata",
],
deps = [
":text_embedder",
"//mediapipe/framework/deps:file_path",
"//mediapipe/framework/port:gtest_main",
"//mediapipe/tasks/cc:common",
"//mediapipe/tasks/cc/components/containers:embedding_result",
"@com_google_absl//absl/flags:flag",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util",
],
)

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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# 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.
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
package(default_visibility = ["//mediapipe/tasks:internal"])
licenses(["notice"])
mediapipe_proto_library(
name = "text_embedder_graph_options_proto",
srcs = ["text_embedder_graph_options.proto"],
deps = [
"//mediapipe/framework:calculator_options_proto",
"//mediapipe/framework:calculator_proto",
"//mediapipe/tasks/cc/components/processors/proto:embedder_options_proto",
"//mediapipe/tasks/cc/core/proto:base_options_proto",
],
)

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/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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.
==============================================================================*/
syntax = "proto2";
package mediapipe.tasks.text.text_embedder.proto;
import "mediapipe/framework/calculator.proto";
import "mediapipe/tasks/cc/components/processors/proto/embedder_options.proto";
import "mediapipe/tasks/cc/core/proto/base_options.proto";
message TextEmbedderGraphOptions {
extend mediapipe.CalculatorOptions {
optional TextEmbedderGraphOptions ext = 477589892;
}
// Base options for configuring MediaPipe Tasks, such as specifying the TfLite
// model file with metadata, accelerator options, etc.
optional core.proto.BaseOptions base_options = 1;
// Options for configuring the embedder behavior, such as normalization or
// quantization.
optional components.processors.proto.EmbedderOptions embedder_options = 2;
}

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/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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.
==============================================================================*/
#include "mediapipe/tasks/cc/text/text_embedder/text_embedder.h"
#include <memory>
#include "absl/status/statusor.h"
#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/framework/calculator.pb.h"
#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
#include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h"
#include "mediapipe/tasks/cc/components/utils/cosine_similarity.h"
#include "mediapipe/tasks/cc/core/base_options.h"
#include "mediapipe/tasks/cc/core/proto/base_options.pb.h"
#include "mediapipe/tasks/cc/core/task_api_factory.h"
#include "mediapipe/tasks/cc/text/text_embedder/proto/text_embedder_graph_options.pb.h"
namespace mediapipe::tasks::text::text_embedder {
namespace {
constexpr char kTextTag[] = "TEXT";
constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
constexpr char kTextInStreamName[] = "text_in";
constexpr char kEmbeddingsStreamName[] = "embeddings_out";
constexpr char kGraphTypeName[] =
"mediapipe.tasks.text.text_embedder.TextEmbedderGraph";
using ::mediapipe::tasks::components::containers::ConvertToEmbeddingResult;
using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
// Creates a MediaPipe graph config that contains a single node of type
// "mediapipe.tasks.text.text_embedder.TextEmbedderGraph".
CalculatorGraphConfig CreateGraphConfig(
std::unique_ptr<proto::TextEmbedderGraphOptions> options_proto) {
api2::builder::Graph graph;
auto& task_graph = graph.AddNode(kGraphTypeName);
task_graph.GetOptions<proto::TextEmbedderGraphOptions>().Swap(
options_proto.get());
graph.In(kTextTag).SetName(kTextInStreamName) >> task_graph.In(kTextTag);
task_graph.Out(kEmbeddingsTag).SetName(kEmbeddingsStreamName) >>
graph.Out(kEmbeddingsTag);
return graph.GetConfig();
}
// Converts the user-facing TextEmbedderOptions struct to the internal
// TextEmbedderGraphOptions proto.
std::unique_ptr<proto::TextEmbedderGraphOptions>
ConvertTextEmbedderOptionsToProto(TextEmbedderOptions* options) {
auto options_proto = std::make_unique<proto::TextEmbedderGraphOptions>();
auto base_options_proto = std::make_unique<tasks::core::proto::BaseOptions>(
tasks::core::ConvertBaseOptionsToProto(&(options->base_options)));
options_proto->mutable_base_options()->Swap(base_options_proto.get());
auto embedder_options_proto =
std::make_unique<components::processors::proto::EmbedderOptions>(
components::processors::ConvertEmbedderOptionsToProto(
&(options->embedder_options)));
options_proto->mutable_embedder_options()->Swap(embedder_options_proto.get());
return options_proto;
}
} // namespace
absl::StatusOr<std::unique_ptr<TextEmbedder>> TextEmbedder::Create(
std::unique_ptr<TextEmbedderOptions> options) {
std::unique_ptr<proto::TextEmbedderGraphOptions> options_proto =
ConvertTextEmbedderOptionsToProto(options.get());
return core::TaskApiFactory::Create<TextEmbedder,
proto::TextEmbedderGraphOptions>(
CreateGraphConfig(std::move(options_proto)),
std::move(options->base_options.op_resolver));
}
absl::StatusOr<TextEmbedderResult> TextEmbedder::Embed(absl::string_view text) {
ASSIGN_OR_RETURN(
auto output_packets,
runner_->Process(
{{kTextInStreamName, MakePacket<std::string>(std::string(text))}}));
return ConvertToEmbeddingResult(
output_packets[kEmbeddingsStreamName].Get<EmbeddingResult>());
}
absl::StatusOr<double> TextEmbedder::CosineSimilarity(
const components::containers::Embedding& u,
const components::containers::Embedding& v) {
return components::utils::CosineSimilarity(u, v);
}
} // namespace mediapipe::tasks::text::text_embedder

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/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_
#define MEDIAPIPE_TASKS_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_
#include <memory>
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/string_view.h"
#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
#include "mediapipe/tasks/cc/components/processors/embedder_options.h"
#include "mediapipe/tasks/cc/core/base_options.h"
#include "mediapipe/tasks/cc/core/base_task_api.h"
namespace mediapipe::tasks::text::text_embedder {
// Alias the shared EmbeddingResult struct as result typo.
using TextEmbedderResult =
::mediapipe::tasks::components::containers::EmbeddingResult;
// Options for configuring a MediaPipe text embedder task.
struct TextEmbedderOptions {
// Base options for configuring MediaPipe Tasks, such as specifying the model
// file with metadata, accelerator options, op resolver, etc.
tasks::core::BaseOptions base_options;
// Options for configuring the embedder behavior, such as L2-normalization or
// scalar-quantization.
components::processors::EmbedderOptions embedder_options;
};
// Performs embedding extraction on text.
//
// This API expects a TFLite model with TFLite Model Metadata that contains the
// mandatory (described below) input tensors and output tensors. Metadata should
// contain the input process unit for the model's Tokenizer as well as input /
// output tensor metadata.
//
// TODO: Support Universal Sentence Encoder.
// Input tensors:
// (kTfLiteInt32)
// - 3 input tensors of size `[batch_size x bert_max_seq_len]` with names
// "ids", "mask", and "segment_ids" representing the input ids, mask ids, and
// segment ids respectively
// - or 1 input tensor of size `[batch_size x max_seq_len]` representing the
// input ids
//
// At least one output tensor with:
// (kTfLiteFloat32)
// - `N` components corresponding to the `N` dimensions of the returned
// feature vector for this output layer.
// - Either 2 or 4 dimensions, i.e. `[1 x N]` or `[1 x 1 x 1 x N]`.
class TextEmbedder : core::BaseTaskApi {
public:
using BaseTaskApi::BaseTaskApi;
// Creates a TextEmbedder from the provided `options`. A non-default
// OpResolver can be specified in the BaseOptions in order to support custom
// Ops or specify a subset of built-in Ops.
static absl::StatusOr<std::unique_ptr<TextEmbedder>> Create(
std::unique_ptr<TextEmbedderOptions> options);
// Performs embedding extraction on the input `text`.
absl::StatusOr<TextEmbedderResult> Embed(absl::string_view text);
// Shuts down the TextEmbedder when all the work is done.
absl::Status Close() { return runner_->Close(); }
// Utility function to compute cosine similarity [1] between two embeddings.
// May return an InvalidArgumentError if e.g. the embeddings are of different
// types (quantized vs. float), have different sizes, or have a an L2-norm of
// 0.
//
// [1]: https://en.wikipedia.org/wiki/Cosine_similarity
static absl::StatusOr<double> CosineSimilarity(
const components::containers::Embedding& u,
const components::containers::Embedding& v);
};
} // namespace mediapipe::tasks::text::text_embedder
#endif // MEDIAPIPE_TASKS_CC_TEXT_TEXT_EMBEDDER_TEXT_EMBEDDER_H_

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/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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.
==============================================================================*/
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
#include "mediapipe/framework/api2/builder.h"
#include "mediapipe/framework/api2/port.h"
#include "mediapipe/framework/calculator.pb.h"
#include "mediapipe/framework/calculator_framework.h"
#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h"
#include "mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h"
#include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h"
#include "mediapipe/tasks/cc/components/proto/text_preprocessing_graph_options.pb.h"
#include "mediapipe/tasks/cc/components/text_preprocessing_graph.h"
#include "mediapipe/tasks/cc/core/model_resources.h"
#include "mediapipe/tasks/cc/core/model_task_graph.h"
#include "mediapipe/tasks/cc/core/proto/model_resources_calculator.pb.h"
#include "mediapipe/tasks/cc/text/text_embedder/proto/text_embedder_graph_options.pb.h"
namespace mediapipe::tasks::text::text_embedder {
namespace {
using ::mediapipe::api2::Input;
using ::mediapipe::api2::Output;
using ::mediapipe::api2::builder::Graph;
using ::mediapipe::api2::builder::Source;
using ::mediapipe::tasks::components::containers::proto::EmbeddingResult;
using ::mediapipe::tasks::core::ModelResources;
constexpr char kEmbeddingsTag[] = "EMBEDDINGS";
constexpr char kTextTag[] = "TEXT";
constexpr char kMetadataExtractorTag[] = "METADATA_EXTRACTOR";
constexpr char kTensorsTag[] = "TENSORS";
} // namespace
// A "mediapipe.tasks.text.TextEmbedderGraph" performs text embedding
// extraction.
// - Accepts input text and outputs embeddings on CPU.
//
// Inputs:
// TEXT - std::string
// Input text to perform embedding extraction on.
//
// Outputs:
// EMBEDDINGS - EmbeddingResult
// The embedding result.
//
// Example:
// node {
// calculator: "mediapipe.tasks.text.TextEmbedderGraph"
// input_stream: "TEXT:text_in"
// output_stream: "EMBEDDINGS:embedding_result_out"
// options {
// [mediapipe.tasks.text.text_embedder.proto.TextEmbedderGraphOptions.ext] {
// base_options {
// model_asset {
// file_name: "/path/to/model.tflite"
// }
// }
// }
// }
// }
class TextEmbedderGraph : public core::ModelTaskGraph {
public:
absl::StatusOr<CalculatorGraphConfig> GetConfig(
SubgraphContext* sc) override {
CHECK(sc != nullptr);
ASSIGN_OR_RETURN(const ModelResources* model_resources,
CreateModelResources<proto::TextEmbedderGraphOptions>(sc));
Graph graph;
ASSIGN_OR_RETURN(
Source<EmbeddingResult> embedding_result_out,
BuildTextEmbedderTask(sc->Options<proto::TextEmbedderGraphOptions>(),
*model_resources,
graph[Input<std::string>(kTextTag)], graph));
embedding_result_out >> graph[Output<EmbeddingResult>(kEmbeddingsTag)];
return graph.GetConfig();
}
private:
// Adds a mediapipe TextEmbedder task graph into the provided
// builder::Graph instance. The TextEmbedder task takes an input
// text (std::string) and returns an embedding result.
//
// task_options: the mediapipe tasks TextEmbedderGraphOptions proto.
// model_resources: the ModelResources object initialized from a
// TextEmbedder model file with model metadata.
// text_in: (std::string) stream to run embedding extraction on.
// graph: the mediapipe builder::Graph instance to be updated.
absl::StatusOr<Source<EmbeddingResult>> BuildTextEmbedderTask(
const proto::TextEmbedderGraphOptions& task_options,
const ModelResources& model_resources, Source<std::string> text_in,
Graph& graph) {
// Adds preprocessing calculators and connects them to the text input
// stream.
auto& preprocessing =
graph.AddNode("mediapipe.tasks.components.TextPreprocessingSubgraph");
MP_RETURN_IF_ERROR(components::ConfigureTextPreprocessingSubgraph(
model_resources,
preprocessing.GetOptions<
tasks::components::proto::TextPreprocessingGraphOptions>()));
text_in >> preprocessing.In(kTextTag);
// Adds both InferenceCalculator and ModelResourcesCalculator.
auto& inference = AddInference(
model_resources, task_options.base_options().acceleration(), graph);
// The metadata extractor side-output comes from the
// ModelResourcesCalculator.
inference.SideOut(kMetadataExtractorTag) >>
preprocessing.SideIn(kMetadataExtractorTag);
preprocessing.Out(kTensorsTag) >> inference.In(kTensorsTag);
// Adds postprocessing calculators and connects its input stream to the
// inference results.
auto& postprocessing = graph.AddNode(
"mediapipe.tasks.components.processors.EmbeddingPostprocessingGraph");
MP_RETURN_IF_ERROR(components::processors::ConfigureEmbeddingPostprocessing(
model_resources, task_options.embedder_options(),
&postprocessing.GetOptions<components::processors::proto::
EmbeddingPostprocessingGraphOptions>()));
inference.Out(kTensorsTag) >> postprocessing.In(kTensorsTag);
// Outputs the embedding result.
return postprocessing[Output<EmbeddingResult>(kEmbeddingsTag)];
}
};
REGISTER_MEDIAPIPE_GRAPH(
::mediapipe::tasks::text::text_embedder::TextEmbedderGraph);
} // namespace mediapipe::tasks::text::text_embedder

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@ -0,0 +1,143 @@
/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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.
==============================================================================*/
#include "mediapipe/tasks/cc/text/text_embedder/text_embedder.h"
#include <memory>
#include "absl/flags/flag.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/framework/deps/file_path.h"
#include "mediapipe/framework/port/gmock.h"
#include "mediapipe/framework/port/gtest.h"
#include "mediapipe/framework/port/status_matchers.h"
#include "mediapipe/tasks/cc/common.h"
#include "mediapipe/tasks/cc/components/containers/embedding_result.h"
#include "tensorflow/lite/core/shims/cc/shims_test_util.h"
namespace mediapipe::tasks::text::text_embedder {
namespace {
constexpr char kTestDataDirectory[] = "/mediapipe/tasks/testdata/text/";
// Note that these models use dynamic-sized tensors.
// Embedding model with BERT preprocessing.
constexpr char kMobileBert[] = "mobilebert_embedding_with_metadata.tflite";
// Embedding model with regex preprocessing.
constexpr char kRegexOneEmbeddingModel[] =
"regex_one_embedding_with_metadata.tflite";
// Tolerance for embedding vector coordinate values.
constexpr float kEpsilon = 1e-4;
// Tolerancy for cosine similarity evaluation.
constexpr double kSimilarityTolerancy = 1e-6;
using ::mediapipe::file::JoinPath;
using ::testing::HasSubstr;
using ::testing::Optional;
class EmbedderTest : public tflite_shims::testing::Test {};
TEST_F(EmbedderTest, FailsWithMissingModel) {
auto text_embedder =
TextEmbedder::Create(std::make_unique<TextEmbedderOptions>());
ASSERT_EQ(text_embedder.status().code(), absl::StatusCode::kInvalidArgument);
ASSERT_THAT(
text_embedder.status().message(),
HasSubstr("ExternalFile must specify at least one of 'file_content', "
"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."));
ASSERT_THAT(text_embedder.status().GetPayload(kMediaPipeTasksPayload),
Optional(absl::Cord(absl::StrCat(
MediaPipeTasksStatus::kRunnerInitializationError))));
}
TEST_F(EmbedderTest, SucceedsWithMobileBert) {
auto options = std::make_unique<TextEmbedderOptions>();
options->base_options.model_asset_path =
JoinPath("./", kTestDataDirectory, kMobileBert);
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
TextEmbedder::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(
TextEmbedderResult result0,
text_embedder->Embed("it's a charming and often affecting journey"));
ASSERT_EQ(result0.embeddings.size(), 1);
ASSERT_EQ(result0.embeddings[0].float_embedding.size(), 512);
ASSERT_NEAR(result0.embeddings[0].float_embedding[0], 19.9016f, kEpsilon);
MP_ASSERT_OK_AND_ASSIGN(
auto result1, text_embedder->Embed("what a great and fantastic trip"));
ASSERT_EQ(result1.embeddings.size(), 1);
ASSERT_EQ(result1.embeddings[0].float_embedding.size(), 512);
ASSERT_NEAR(result1.embeddings[0].float_embedding[0], 22.626251f, kEpsilon);
// Check cosine similarity.
MP_ASSERT_OK_AND_ASSIGN(
double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
result1.embeddings[0]));
EXPECT_NEAR(similarity, 0.969514, kSimilarityTolerancy);
MP_ASSERT_OK(text_embedder->Close());
}
TEST(EmbedTest, SucceedsWithRegexOneEmbeddingModel) {
auto options = std::make_unique<TextEmbedderOptions>();
options->base_options.model_asset_path =
JoinPath("./", kTestDataDirectory, kRegexOneEmbeddingModel);
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
TextEmbedder::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(
auto result0,
text_embedder->Embed("it's a charming and often affecting journey"));
EXPECT_EQ(result0.embeddings.size(), 1);
EXPECT_EQ(result0.embeddings[0].float_embedding.size(), 16);
EXPECT_NEAR(result0.embeddings[0].float_embedding[0], 0.0309356f, kEpsilon);
MP_ASSERT_OK_AND_ASSIGN(
auto result1, text_embedder->Embed("what a great and fantastic trip"));
EXPECT_EQ(result1.embeddings.size(), 1);
EXPECT_EQ(result1.embeddings[0].float_embedding.size(), 16);
EXPECT_NEAR(result1.embeddings[0].float_embedding[0], 0.0312863f, kEpsilon);
// Check cosine similarity.
MP_ASSERT_OK_AND_ASSIGN(
double similarity, TextEmbedder::CosineSimilarity(result0.embeddings[0],
result1.embeddings[0]));
EXPECT_NEAR(similarity, 0.999937, kSimilarityTolerancy);
MP_ASSERT_OK(text_embedder->Close());
}
TEST_F(EmbedderTest, SucceedsWithQuantization) {
auto options = std::make_unique<TextEmbedderOptions>();
options->base_options.model_asset_path =
JoinPath("./", kTestDataDirectory, kMobileBert);
options->embedder_options.quantize = true;
MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextEmbedder> text_embedder,
TextEmbedder::Create(std::move(options)));
MP_ASSERT_OK_AND_ASSIGN(
TextEmbedderResult result,
text_embedder->Embed("it's a charming and often affecting journey"));
ASSERT_EQ(result.embeddings.size(), 1);
ASSERT_EQ(result.embeddings[0].quantized_embedding.size(), 512);
MP_ASSERT_OK(text_embedder->Close());
}
} // namespace
} // namespace mediapipe::tasks::text::text_embedder

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mediapipe/tasks/java/com/google/mediapipe/tasks/core/BaseOptions.java
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@ -26,22 +26,23 @@ public abstract class BaseOptions {
@AutoValue.Builder
public abstract static class Builder {
/**
* Sets the model path to a tflite model with metadata in the assets.
* Sets the model path to a model asset file (a tflite model or a model asset bundle file) in
* the Android app assets folder.
*
* <p>Note: when model path is set, both model file descriptor and model buffer should be empty.
*/
public abstract Builder setModelAssetPath(String value);
/**
* Sets the native fd int of a tflite model with metadata.
* Sets the native fd int of a model asset file (a tflite model or a model asset bundle file).
*
* <p>Note: when model file descriptor is set, both model path and model buffer should be empty.
*/
public abstract Builder setModelAssetFileDescriptor(Integer value);
/**
* Sets either the direct {@link ByteBuffer} or the {@link MappedByteBuffer} of a tflite model
* with metadata.
* Sets either the direct {@link ByteBuffer} or the {@link MappedByteBuffer} of a model asset
* file (a tflite model or a model asset bundle file).
*
* <p>Note: when model buffer is set, both model file and model file descriptor should be empty.
*/

5
mediapipe/tasks/testdata/vision/BUILD vendored
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@ -144,8 +144,13 @@ filegroup(
)
# Gestures related models. Visible to model_maker.
# TODO: Upload canned gesture model and gesture embedding model to GCS after Model Card approval
filegroup(
name = "test_gesture_models",
srcs = [
"hand_landmark_full.tflite",
"palm_detection_full.tflite",
],
visibility = [
"//mediapipe/model_maker:__subpackages__",
"//mediapipe/tasks:internal",

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# This contains the MediaPipe Tasks NPM package definitions.
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm")
load("@npm//@bazel/rollup:index.bzl", "rollup_bundle")
package(default_visibility = ["//mediapipe/tasks:internal"])
# Audio
mediapipe_ts_library(
name = "audio_lib",
srcs = ["audio.ts"],
deps = ["//mediapipe/tasks/web/audio:audio_lib"],
)
rollup_bundle(
name = "audio_bundle",
config_file = "rollup.config.mjs",
entry_point = "audio.ts",
output_dir = False,
deps = [
":audio_lib",
"@npm//@rollup/plugin-commonjs",
"@npm//@rollup/plugin-node-resolve",
],
)
pkg_npm(
name = "audio_pkg",
package_name = "__PACKAGE_NAME__",
srcs = ["package.json"],
substitutions = {
"__PACKAGE_NAME__": "@mediapipe/tasks-audio",
"__DESCRIPTION__": "MediaPipe Audio Tasks",
"__BUNDLE__": "audio_bundle.js",
},
tgz = "audio.tgz",
deps = [":audio_bundle"],
)
# Text
mediapipe_ts_library(
name = "text_lib",
srcs = ["text.ts"],
deps = ["//mediapipe/tasks/web/text:text_lib"],
)
rollup_bundle(
name = "text_bundle",
config_file = "rollup.config.mjs",
entry_point = "text.ts",
output_dir = False,
deps = [
":text_lib",
"@npm//@rollup/plugin-commonjs",
"@npm//@rollup/plugin-node-resolve",
],
)
pkg_npm(
name = "text_pkg",
package_name = "__PACKAGE_NAME__",
srcs = ["package.json"],
substitutions = {
"__PACKAGE_NAME__": "@mediapipe/tasks-text",
"__DESCRIPTION__": "MediaPipe Text Tasks",
"__BUNDLE__": "text_bundle.js",
},
tgz = "text.tgz",
deps = [":text_bundle"],
)
# Vision
mediapipe_ts_library(
name = "vision_lib",
srcs = ["vision.ts"],
deps = ["//mediapipe/tasks/web/vision:vision_lib"],
)
rollup_bundle(
name = "vision_bundle",
config_file = "rollup.config.mjs",
entry_point = "vision.ts",
output_dir = False,
deps = [
":vision_lib",
"@npm//@rollup/plugin-commonjs",
"@npm//@rollup/plugin-node-resolve",
],
)
pkg_npm(
name = "vision_pkg",
package_name = "__PACKAGE_NAME__",
srcs = ["package.json"],
substitutions = {
"__PACKAGE_NAME__": "@mediapipe/tasks-vision",
"__DESCRIPTION__": "MediaPipe Vision Tasks",
"__BUNDLE__": "vision_bundle.js",
},
tgz = "vision.tgz",
deps = [":vision_bundle"],
)

17
mediapipe/tasks/web/audio.ts Normal file
View File

@ -0,0 +1,17 @@
/**
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
*
* 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.
*/
export * from '../../tasks/web/audio/index';

2
mediapipe/tasks/web/audio/BUILD
View File

@ -2,6 +2,8 @@
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
package(default_visibility = ["//mediapipe/tasks:internal"])
mediapipe_ts_library(
name = "audio_lib",
srcs = ["index.ts"],

2
mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts
View File

@ -198,7 +198,7 @@ export class AudioClassifier extends TaskRunner {
classifierNode.addInputStream('AUDIO:' + AUDIO_STREAM);
classifierNode.addInputStream('SAMPLE_RATE:' + SAMPLE_RATE_STREAM);
classifierNode.addOutputStream(
'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM);
'CLASSIFICATIONS:' + CLASSIFICATION_RESULT_STREAM);
classifierNode.setOptions(calculatorOptions);
graphConfig.addNode(classifierNode);

15
mediapipe/tasks/web/package.json Normal file
View File

@ -0,0 +1,15 @@
{
"name": "__PACKAGE_NAME__",
"version": "__VERSION__",
"description": "__DESCRIPTION__",
"main": "__BUNDLE__",
"module": "__BUNDLE__",
"author": "mediapipe@google.com",
"license": "Apache-2.0",
"type": "module",
"dependencies": {
"google-protobuf": "^3.21.2"
},
"homepage": "http://mediapipe.dev",
"keywords": [ "AR", "ML", "Augmented", "MediaPipe", "MediaPipe Tasks" ]
}

9
mediapipe/tasks/web/rollup.config.mjs Normal file
View File

@ -0,0 +1,9 @@
import resolve from '@rollup/plugin-node-resolve';
import commonjs from '@rollup/plugin-commonjs';
export default {
plugins: [
resolve(),
commonjs()
]
}

17
mediapipe/tasks/web/text.ts Normal file
View File

@ -0,0 +1,17 @@
/**
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
*
* 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.
*/
export * from '../../tasks/web/text/index';

2
mediapipe/tasks/web/text/BUILD
View File

@ -2,6 +2,8 @@
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
package(default_visibility = ["//mediapipe/tasks:internal"])
mediapipe_ts_library(
name = "text_lib",
srcs = ["index.ts"],

4
mediapipe/tasks/web/text/text_classifier/BUILD
View File

@ -13,8 +13,8 @@ mediapipe_ts_library(
name = "text_classifier",
srcs = [
"text_classifier.ts",
"text_classifier_options.d.ts",
"text_classifier_result.d.ts",
"text_classifier_options.ts",
"text_classifier_result.ts",
],
deps = [
"//mediapipe/framework:calculator_jspb_proto",

0
mediapipe/tasks/web/text/text_classifier/text_classifier_options.d.ts → mediapipe/tasks/web/text/text_classifier/text_classifier_options.ts
View File

0
mediapipe/tasks/web/text/text_classifier/text_classifier_result.d.ts → mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts
View File

17
mediapipe/tasks/web/vision.ts Normal file
View File

@ -0,0 +1,17 @@
/**
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
*
* 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.
*/
export * from '../../tasks/web/vision/index';

2
mediapipe/tasks/web/vision/BUILD
View File

@ -2,6 +2,8 @@
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
package(default_visibility = ["//mediapipe/tasks:internal"])
mediapipe_ts_library(
name = "vision_lib",
srcs = ["index.ts"],

4
mediapipe/tasks/web/vision/gesture_recognizer/BUILD
View File

@ -13,8 +13,8 @@ mediapipe_ts_library(
name = "gesture_recognizer",
srcs = [
"gesture_recognizer.ts",
"gesture_recognizer_options.d.ts",
"gesture_recognizer_result.d.ts",
"gesture_recognizer_options.ts",
"gesture_recognizer_result.ts",
],
deps = [
"//mediapipe/framework:calculator_jspb_proto",

0
mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.d.ts → mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_options.ts
View File

0
mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.d.ts → mediapipe/tasks/web/vision/gesture_recognizer/gesture_recognizer_result.ts
View File

4
mediapipe/tasks/web/vision/object_detector/BUILD
View File

@ -13,8 +13,8 @@ mediapipe_ts_library(
name = "object_detector",
srcs = [
"object_detector.ts",
"object_detector_options.d.ts",
"object_detector_result.d.ts",
"object_detector_options.ts",
"object_detector_result.ts",
],
deps = [
"//mediapipe/framework:calculator_jspb_proto",

0
mediapipe/tasks/web/vision/object_detector/object_detector_options.d.ts → mediapipe/tasks/web/vision/object_detector/object_detector_options.ts
View File

0
mediapipe/tasks/web/vision/object_detector/object_detector_result.d.ts → mediapipe/tasks/web/vision/object_detector/object_detector_result.ts
View File

4
package.json
View File

@ -3,12 +3,16 @@
"version": "0.0.0-alphga",
"description": "MediaPipe GitHub repo",
"devDependencies": {
"@bazel/rollup": "^5.7.1",
"@bazel/typescript": "^5.7.1",
"@rollup/plugin-commonjs": "^23.0.2",
"@rollup/plugin-node-resolve": "^15.0.1",
"@types/google-protobuf": "^3.15.6",
"@types/offscreencanvas": "^2019.7.0",
"google-protobuf": "^3.21.2",
"protobufjs": "^7.1.2",
"protobufjs-cli": "^1.0.2",
"rollup": "^2.3.0",
"ts-protoc-gen": "^0.15.0",
"typescript": "^4.8.4"
}

182
yarn.lock
View File

@ -3,9 +3,16 @@
"@babel/parser@^7.9.4":
version "7.20.1"
resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.20.1.tgz#3e045a92f7b4623cafc2425eddcb8cf2e54f9cc5"
integrity sha512-hp0AYxaZJhxULfM1zyp7Wgr+pSUKBcP3M+PHnSzWGdXOzg/kHWIgiUWARvubhUKGOEw3xqY4x+lyZ9ytBVcELw==
version "7.20.3"
resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.20.3.tgz#5358cf62e380cf69efcb87a7bb922ff88bfac6e2"
integrity sha512-OP/s5a94frIPXwjzEcv5S/tpQfc6XhxYUnmWpgdqMWGgYCuErA3SzozaRAMQgSZWKeTJxht9aWAkUY+0UzvOFg==
"@bazel/rollup@^5.7.1":
version "5.7.1"
resolved "https://registry.yarnpkg.com/@bazel/rollup/-/rollup-5.7.1.tgz#6f644c2d493a5bd9cd3724a6f239e609585c6e37"
integrity sha512-LLNogoK2Qx9GIJVywQ+V/czjud8236mnaRX//g7qbOyXoWZDQvAEgsxRHq+lS/XX9USbh+zJJlfb+Dfp/PXx4A==
dependencies:
"@bazel/worker" "5.7.1"
"@bazel/typescript@^5.7.1":
version "5.7.1"
@ -77,6 +84,44 @@
resolved "https://registry.yarnpkg.com/@protobufjs/utf8/-/utf8-1.1.0.tgz#a777360b5b39a1a2e5106f8e858f2fd2d060c570"
integrity sha512-Vvn3zZrhQZkkBE8LSuW3em98c0FwgO4nxzv6OdSxPKJIEKY2bGbHn+mhGIPerzI4twdxaP8/0+06HBpwf345Lw==
"@rollup/plugin-commonjs@^23.0.2":
version "23.0.2"
resolved "https://registry.yarnpkg.com/@rollup/plugin-commonjs/-/plugin-commonjs-23.0.2.tgz#3a3a5b7b1b1cb29037eb4992edcaae997d7ebd92"
integrity sha512-e9ThuiRf93YlVxc4qNIurvv+Hp9dnD+4PjOqQs5vAYfcZ3+AXSrcdzXnVjWxcGQOa6KGJFcRZyUI3ktWLavFjg==
dependencies:
"@rollup/pluginutils" "^5.0.1"
commondir "^1.0.1"
estree-walker "^2.0.2"
glob "^8.0.3"
is-reference "1.2.1"
magic-string "^0.26.4"
"@rollup/plugin-node-resolve@^15.0.1":
version "15.0.1"
resolved "https://registry.yarnpkg.com/@rollup/plugin-node-resolve/-/plugin-node-resolve-15.0.1.tgz#72be449b8e06f6367168d5b3cd5e2802e0248971"
integrity sha512-ReY88T7JhJjeRVbfCyNj+NXAG3IIsVMsX9b5/9jC98dRP8/yxlZdz7mHZbHk5zHr24wZZICS5AcXsFZAXYUQEg==
dependencies:
"@rollup/pluginutils" "^5.0.1"
"@types/resolve" "1.20.2"
deepmerge "^4.2.2"
is-builtin-module "^3.2.0"
is-module "^1.0.0"
resolve "^1.22.1"
"@rollup/pluginutils@^5.0.1":
version "5.0.2"
resolved "https://registry.yarnpkg.com/@rollup/pluginutils/-/pluginutils-5.0.2.tgz#012b8f53c71e4f6f9cb317e311df1404f56e7a33"
integrity sha512-pTd9rIsP92h+B6wWwFbW8RkZv4hiR/xKsqre4SIuAOaOEQRxi0lqLke9k2/7WegC85GgUs9pjmOjCUi3In4vwA==
dependencies:
"@types/estree" "^1.0.0"
estree-walker "^2.0.2"
picomatch "^2.3.1"
"@types/estree@*", "@types/estree@^1.0.0":
version "1.0.0"
resolved "https://registry.yarnpkg.com/@types/estree/-/estree-1.0.0.tgz#5fb2e536c1ae9bf35366eed879e827fa59ca41c2"
integrity sha512-WulqXMDUTYAXCjZnk6JtIHPigp55cVtDgDrO2gHRwhyJto21+1zbVCtOYB2L1F9w4qCQ0rOGWBnBe0FNTiEJIQ==
"@types/google-protobuf@^3.15.6":
version "3.15.6"
resolved "https://registry.yarnpkg.com/@types/google-protobuf/-/google-protobuf-3.15.6.tgz#674a69493ef2c849b95eafe69167ea59079eb504"
@ -110,6 +155,11 @@
resolved "https://registry.yarnpkg.com/@types/offscreencanvas/-/offscreencanvas-2019.7.0.tgz#e4a932069db47bb3eabeb0b305502d01586fa90d"
integrity sha512-PGcyveRIpL1XIqK8eBsmRBt76eFgtzuPiSTyKHZxnGemp2yzGzWpjYKAfK3wIMiU7eH+851yEpiuP8JZerTmWg==
"@types/resolve@1.20.2":
version "1.20.2"
resolved "https://registry.yarnpkg.com/@types/resolve/-/resolve-1.20.2.tgz#97d26e00cd4a0423b4af620abecf3e6f442b7975"
integrity sha512-60BCwRFOZCQhDncwQdxxeOEEkbc5dIMccYLwbxsS4TUNeVECQ/pBJ0j09mrHOl/JJvpRPGwO9SvE4nR2Nb/a4Q==
acorn-jsx@^5.3.2:
version "5.3.2"
resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.2.tgz#7ed5bb55908b3b2f1bc55c6af1653bada7f07937"
@ -162,6 +212,11 @@ buffer-from@^1.0.0:
resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
builtin-modules@^3.3.0:
version "3.3.0"
resolved "https://registry.yarnpkg.com/builtin-modules/-/builtin-modules-3.3.0.tgz#cae62812b89801e9656336e46223e030386be7b6"
integrity sha512-zhaCDicdLuWN5UbN5IMnFqNMhNfo919sH85y2/ea+5Yg9TsTkeZxpL+JLbp6cgYFS4sRLp3YV4S6yDuqVWHYOw==
catharsis@^0.9.0:
version "0.9.0"
resolved "https://registry.yarnpkg.com/catharsis/-/catharsis-0.9.0.tgz#40382a168be0e6da308c277d3a2b3eb40c7d2121"
@ -189,6 +244,11 @@ color-name@~1.1.4:
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
commondir@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/commondir/-/commondir-1.0.1.tgz#ddd800da0c66127393cca5950ea968a3aaf1253b"
integrity sha512-W9pAhw0ja1Edb5GVdIF1mjZw/ASI0AlShXM83UUGe2DVr5TdAPEA1OA8m/g8zWp9x6On7gqufY+FatDbC3MDQg==
concat-map@0.0.1:
version "0.0.1"
resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
@ -199,6 +259,11 @@ deep-is@~0.1.3:
resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831"
integrity sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==
deepmerge@^4.2.2:
version "4.2.2"
resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-4.2.2.tgz#44d2ea3679b8f4d4ffba33f03d865fc1e7bf4955"
integrity sha512-FJ3UgI4gIl+PHZm53knsuSFpE+nESMr7M4v9QcgB7S63Kj/6WqMiFQJpBBYz1Pt+66bZpP3Q7Lye0Oo9MPKEdg==
entities@~2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/entities/-/entities-2.1.0.tgz#992d3129cf7df6870b96c57858c249a120f8b8b5"
@ -227,9 +292,9 @@ eslint-visitor-keys@^3.3.0:
integrity sha512-mQ+suqKJVyeuwGYHAdjMFqjCyfl8+Ldnxuyp3ldiMBFKkvytrXUZWaiPCEav8qDHKty44bD+qV1IP4T+w+xXRA==
espree@^9.0.0:
version "9.4.0"
resolved "https://registry.yarnpkg.com/espree/-/espree-9.4.0.tgz#cd4bc3d6e9336c433265fc0aa016fc1aaf182f8a"
integrity sha512-DQmnRpLj7f6TgN/NYb0MTzJXL+vJF9h3pHy4JhCIs3zwcgez8xmGg3sXHcEO97BrmO2OSvCwMdfdlyl+E9KjOw==
version "9.4.1"
resolved "https://registry.yarnpkg.com/espree/-/espree-9.4.1.tgz#51d6092615567a2c2cff7833445e37c28c0065bd"
integrity sha512-XwctdmTO6SIvCzd9810yyNzIrOrqNYV9Koizx4C/mRhf9uq0o4yHoCEU/670pOxOL/MSraektvSAji79kX90Vg==
dependencies:
acorn "^8.8.0"
acorn-jsx "^5.3.2"
@ -250,6 +315,11 @@ estraverse@^5.1.0:
resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.3.0.tgz#2eea5290702f26ab8fe5370370ff86c965d21123"
integrity sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==
estree-walker@^2.0.2:
version "2.0.2"
resolved "https://registry.yarnpkg.com/estree-walker/-/estree-walker-2.0.2.tgz#52f010178c2a4c117a7757cfe942adb7d2da4cac"
integrity sha512-Rfkk/Mp/DL7JVje3u18FxFujQlTNR2q6QfMSMB7AvCBx91NGj/ba3kCfza0f6dVDbw7YlRf/nDrn7pQrCCyQ/w==
esutils@^2.0.2:
version "2.0.3"
resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64"
@ -265,6 +335,16 @@ fs.realpath@^1.0.0:
resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
integrity sha512-OO0pH2lK6a0hZnAdau5ItzHPI6pUlvI7jMVnxUQRtw4owF2wk8lOSabtGDCTP4Ggrg2MbGnWO9X8K1t4+fGMDw==
fsevents@~2.3.2:
version "2.3.2"
resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.2.tgz#8a526f78b8fdf4623b709e0b975c52c24c02fd1a"
integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==
function-bind@^1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
glob@^7.1.3:
version "7.2.3"
resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.3.tgz#b8df0fb802bbfa8e89bd1d938b4e16578ed44f2b"
@ -277,7 +357,7 @@ glob@^7.1.3:
once "^1.3.0"
path-is-absolute "^1.0.0"
glob@^8.0.0:
glob@^8.0.0, glob@^8.0.3:
version "8.0.3"
resolved "https://registry.yarnpkg.com/glob/-/glob-8.0.3.tgz#415c6eb2deed9e502c68fa44a272e6da6eeca42e"
integrity sha512-ull455NHSHI/Y1FqGaaYFaLGkNMMJbavMrEGFXG/PGrg6y7sutWHUHrz6gy6WEBH6akM1M414dWKCNs+IhKdiQ==
@ -303,6 +383,13 @@ has-flag@^4.0.0:
resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
has@^1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796"
integrity sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==
dependencies:
function-bind "^1.1.1"
inflight@^1.0.4:
version "1.0.6"
resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
@ -316,6 +403,32 @@ inherits@2:
resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
is-builtin-module@^3.2.0:
version "3.2.0"
resolved "https://registry.yarnpkg.com/is-builtin-module/-/is-builtin-module-3.2.0.tgz#bb0310dfe881f144ca83f30100ceb10cf58835e0"
integrity sha512-phDA4oSGt7vl1n5tJvTWooWWAsXLY+2xCnxNqvKhGEzujg+A43wPlPOyDg3C8XQHN+6k/JTQWJ/j0dQh/qr+Hw==
dependencies:
builtin-modules "^3.3.0"
is-core-module@^2.9.0:
version "2.11.0"
resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.11.0.tgz#ad4cb3e3863e814523c96f3f58d26cc570ff0144"
integrity sha512-RRjxlvLDkD1YJwDbroBHMb+cukurkDWNyHx7D3oNB5x9rb5ogcksMC5wHCadcXoo67gVr/+3GFySh3134zi6rw==
dependencies:
has "^1.0.3"
is-module@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/is-module/-/is-module-1.0.0.tgz#3258fb69f78c14d5b815d664336b4cffb6441591"
integrity sha512-51ypPSPCoTEIN9dy5Oy+h4pShgJmPCygKfyRCISBI+JoWT/2oJvK8QPxmwv7b/p239jXrm9M1mlQbyKJ5A152g==
is-reference@1.2.1:
version "1.2.1"
resolved "https://registry.yarnpkg.com/is-reference/-/is-reference-1.2.1.tgz#8b2dac0b371f4bc994fdeaba9eb542d03002d0b7"
integrity sha512-U82MsXXiFIrjCK4otLT+o2NA2Cd2g5MLoOVXUZjIOhLurrRxpEXzI8O0KZHr3IjLvlAH1kTPYSuqer5T9ZVBKQ==
dependencies:
"@types/estree" "*"
js2xmlparser@^4.0.2:
version "4.0.2"
resolved "https://registry.yarnpkg.com/js2xmlparser/-/js2xmlparser-4.0.2.tgz#2a1fdf01e90585ef2ae872a01bc169c6a8d5e60a"
@ -372,9 +485,9 @@ lodash@^4.17.14, lodash@^4.17.15:
integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
long@^5.0.0:
version "5.2.0"
resolved "https://registry.yarnpkg.com/long/-/long-5.2.0.tgz#2696dadf4b4da2ce3f6f6b89186085d94d52fd61"
integrity sha512-9RTUNjK60eJbx3uz+TEGF7fUr29ZDxR5QzXcyDpeSfeH28S9ycINflOgOlppit5U+4kNTe83KQnMEerw7GmE8w==
version "5.2.1"
resolved "https://registry.yarnpkg.com/long/-/long-5.2.1.tgz#e27595d0083d103d2fa2c20c7699f8e0c92b897f"
integrity sha512-GKSNGeNAtw8IryjjkhZxuKB3JzlcLTwjtiQCHKvqQet81I93kXslhDQruGI/QsddO83mcDToBVy7GqGS/zYf/A==
lru-cache@^6.0.0:
version "6.0.0"
@ -383,6 +496,13 @@ lru-cache@^6.0.0:
dependencies:
yallist "^4.0.0"
magic-string@^0.26.4:
version "0.26.7"
resolved "https://registry.yarnpkg.com/magic-string/-/magic-string-0.26.7.tgz#caf7daf61b34e9982f8228c4527474dac8981d6f"
integrity sha512-hX9XH3ziStPoPhJxLq1syWuZMxbDvGNbVchfrdCtanC7D13888bMFow61x8axrx+GfHLtVeAx2kxL7tTGRl+Ow==
dependencies:
sourcemap-codec "^1.4.8"
markdown-it-anchor@^8.4.1:
version "8.6.5"
resolved "https://registry.yarnpkg.com/markdown-it-anchor/-/markdown-it-anchor-8.6.5.tgz#30c4bc5bbff327f15ce3c429010ec7ba75e7b5f8"
@ -400,9 +520,9 @@ markdown-it@^12.3.2:
uc.micro "^1.0.5"
marked@^4.0.10:
version "4.2.1"
resolved "https://registry.yarnpkg.com/marked/-/marked-4.2.1.tgz#eaa32594e45b4e58c02e4d118531fd04345de3b4"
integrity sha512-VK1/jNtwqDLvPktNpL0Fdg3qoeUZhmRsuiIjPEy/lHwXW4ouLoZfO4XoWd4ClDt+hupV1VLpkZhEovjU0W/kqA==
version "4.2.2"
resolved "https://registry.yarnpkg.com/marked/-/marked-4.2.2.tgz#1d2075ad6cdfe42e651ac221c32d949a26c0672a"
integrity sha512-JjBTFTAvuTgANXx82a5vzK9JLSMoV6V3LBVn4Uhdso6t7vXrGx7g1Cd2r6NYSsxrYbQGFCMqBDhFHyK5q2UvcQ==
mdurl@^1.0.1:
version "1.0.1"
@ -457,6 +577,16 @@ path-is-absolute@^1.0.0:
resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
integrity sha512-AVbw3UJ2e9bq64vSaS9Am0fje1Pa8pbGqTTsmXfaIiMpnr5DlDhfJOuLj9Sf95ZPVDAUerDfEk88MPmPe7UCQg==
path-parse@^1.0.7:
version "1.0.7"
resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
picomatch@^2.3.1:
version "2.3.1"
resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42"
integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
prelude-ls@~1.1.2:
version "1.1.2"
resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54"
@ -503,6 +633,15 @@ requizzle@^0.2.3:
dependencies:
lodash "^4.17.14"
resolve@^1.22.1:
version "1.22.1"
resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.22.1.tgz#27cb2ebb53f91abb49470a928bba7558066ac177"
integrity sha512-nBpuuYuY5jFsli/JIs1oldw6fOQCBioohqWZg/2hiaOybXOft4lonv85uDOKXdf8rhyK159cxU5cDcK/NKk8zw==
dependencies:
is-core-module "^2.9.0"
path-parse "^1.0.7"
supports-preserve-symlinks-flag "^1.0.0"
rimraf@^3.0.0:
version "3.0.2"
resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a"
@ -510,6 +649,13 @@ rimraf@^3.0.0:
dependencies:
glob "^7.1.3"
rollup@^2.3.0:
version "2.79.1"
resolved "https://registry.yarnpkg.com/rollup/-/rollup-2.79.1.tgz#bedee8faef7c9f93a2647ac0108748f497f081c7"
integrity sha512-uKxbd0IhMZOhjAiD5oAFp7BqvkA4Dv47qpOCtaNvng4HBwdbWtdOh8f5nZNuk2rp51PMGk3bzfWu5oayNEuYnw==
optionalDependencies:
fsevents "~2.3.2"
semver@5.6.0:
version "5.6.0"
resolved "https://registry.yarnpkg.com/semver/-/semver-5.6.0.tgz#7e74256fbaa49c75aa7c7a205cc22799cac80004"
@ -535,6 +681,11 @@ source-map@^0.6.0, source-map@~0.6.1:
resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
sourcemap-codec@^1.4.8:
version "1.4.8"
resolved "https://registry.yarnpkg.com/sourcemap-codec/-/sourcemap-codec-1.4.8.tgz#ea804bd94857402e6992d05a38ef1ae35a9ab4c4"
integrity sha512-9NykojV5Uih4lgo5So5dtw+f0JgJX30KCNI8gwhz2J9A15wD0Ml6tjHKwf6fTSa6fAdVBdZeNOs9eJ71qCk8vA==
strip-json-comments@^3.1.0:
version "3.1.1"
resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
@ -547,6 +698,11 @@ supports-color@^7.1.0:
dependencies:
has-flag "^4.0.0"
supports-preserve-symlinks-flag@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
taffydb@2.6.2:
version "2.6.2"
resolved "https://registry.yarnpkg.com/taffydb/-/taffydb-2.6.2.tgz#7cbcb64b5a141b6a2efc2c5d2c67b4e150b2a268"