partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | FeaturesDict.save_metadata | See base class for details. | tensorflow_datasets/core/features/feature.py | def save_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Recursively save all child features
for feature_key, feature in six.iteritems(self._feature_dict):
if feature_name:
feature_key = '-'.join((feature_name, feature_key))
feature.save_metadata(data_dir, feature_name=feature_key) | def save_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Recursively save all child features
for feature_key, feature in six.iteritems(self._feature_dict):
if feature_name:
feature_key = '-'.join((feature_name, feature_key))
feature.save_metadata(data_dir, feature_name=feature_key) | [
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train | Tensor.encode_example | See base class for details. | tensorflow_datasets/core/features/feature.py | def encode_example(self, example_data):
"""See base class for details."""
np_dtype = np.dtype(self._dtype.as_numpy_dtype)
# Convert to numpy if possible
if not isinstance(example_data, np.ndarray):
example_data = np.array(example_data, dtype=np_dtype)
# Ensure the shape and dtype match
if example_data.dtype != np_dtype:
raise ValueError('Dtype {} do not match {}'.format(
example_data.dtype, np_dtype))
utils.assert_shape_match(example_data.shape, self._shape)
# For booleans, convert to integer (tf.train.Example does not support bool)
if example_data.dtype == np.bool_:
example_data = example_data.astype(int)
return example_data | def encode_example(self, example_data):
"""See base class for details."""
np_dtype = np.dtype(self._dtype.as_numpy_dtype)
# Convert to numpy if possible
if not isinstance(example_data, np.ndarray):
example_data = np.array(example_data, dtype=np_dtype)
# Ensure the shape and dtype match
if example_data.dtype != np_dtype:
raise ValueError('Dtype {} do not match {}'.format(
example_data.dtype, np_dtype))
utils.assert_shape_match(example_data.shape, self._shape)
# For booleans, convert to integer (tf.train.Example does not support bool)
if example_data.dtype == np.bool_:
example_data = example_data.astype(int)
return example_data | [
"See",
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"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/feature.py#L548-L562 | [
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train | Tensor.decode_example | See base class for details. | tensorflow_datasets/core/features/feature.py | def decode_example(self, tfexample_data):
"""See base class for details."""
# TODO(epot): Support dynamic shape
if self.shape.count(None) < 2:
# Restore the shape if possible. TF Example flattened it.
shape = [-1 if i is None else i for i in self.shape]
tfexample_data = tf.reshape(tfexample_data, shape)
if tfexample_data.dtype != self.dtype:
tfexample_data = tf.dtypes.cast(tfexample_data, self.dtype)
return tfexample_data | def decode_example(self, tfexample_data):
"""See base class for details."""
# TODO(epot): Support dynamic shape
if self.shape.count(None) < 2:
# Restore the shape if possible. TF Example flattened it.
shape = [-1 if i is None else i for i in self.shape]
tfexample_data = tf.reshape(tfexample_data, shape)
if tfexample_data.dtype != self.dtype:
tfexample_data = tf.dtypes.cast(tfexample_data, self.dtype)
return tfexample_data | [
"See",
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"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/feature.py#L564-L573 | [
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train | CelebA._process_celeba_config_file | Unpack the celeba config file.
The file starts with the number of lines, and a header.
Afterwards, there is a configuration for each file: one per line.
Args:
file_path: Path to the file with the configuration.
Returns:
keys: names of the attributes
values: map from the file name to the list of attribute values for
this file. | tensorflow_datasets/image/celeba.py | def _process_celeba_config_file(self, file_path):
"""Unpack the celeba config file.
The file starts with the number of lines, and a header.
Afterwards, there is a configuration for each file: one per line.
Args:
file_path: Path to the file with the configuration.
Returns:
keys: names of the attributes
values: map from the file name to the list of attribute values for
this file.
"""
with tf.io.gfile.GFile(file_path) as f:
data_raw = f.read()
lines = data_raw.split("\n")
keys = lines[1].strip().split()
values = {}
# Go over each line (skip the last one, as it is empty).
for line in lines[2:-1]:
row_values = line.strip().split()
# Each row start with the 'file_name' and then space-separated values.
values[row_values[0]] = [int(v) for v in row_values[1:]]
return keys, values | def _process_celeba_config_file(self, file_path):
"""Unpack the celeba config file.
The file starts with the number of lines, and a header.
Afterwards, there is a configuration for each file: one per line.
Args:
file_path: Path to the file with the configuration.
Returns:
keys: names of the attributes
values: map from the file name to the list of attribute values for
this file.
"""
with tf.io.gfile.GFile(file_path) as f:
data_raw = f.read()
lines = data_raw.split("\n")
keys = lines[1].strip().split()
values = {}
# Go over each line (skip the last one, as it is empty).
for line in lines[2:-1]:
row_values = line.strip().split()
# Each row start with the 'file_name' and then space-separated values.
values[row_values[0]] = [int(v) for v in row_values[1:]]
return keys, values | [
"Unpack",
"the",
"celeba",
"config",
"file",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/celeba.py#L150-L175 | [
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train | CelebA._generate_examples | Yields examples. | tensorflow_datasets/image/celeba.py | def _generate_examples(self, file_id, extracted_dirs):
"""Yields examples."""
filedir = os.path.join(extracted_dirs["img_align_celeba"],
"img_align_celeba")
img_list_path = extracted_dirs["list_eval_partition"]
landmarks_path = extracted_dirs["landmarks_celeba"]
attr_path = extracted_dirs["list_attr_celeba"]
with tf.io.gfile.GFile(img_list_path) as f:
files = [
line.split()[0]
for line in f.readlines()
if int(line.split()[1]) == file_id
]
attributes = self._process_celeba_config_file(attr_path)
landmarks = self._process_celeba_config_file(landmarks_path)
for file_name in sorted(files):
path = os.path.join(filedir, file_name)
yield {
"image": path,
"landmarks": {
k: v for k, v in zip(landmarks[0], landmarks[1][file_name])
},
"attributes": {
# atributes value are either 1 or -1, so convert to bool
k: v > 0 for k, v in zip(attributes[0], attributes[1][file_name])
},
} | def _generate_examples(self, file_id, extracted_dirs):
"""Yields examples."""
filedir = os.path.join(extracted_dirs["img_align_celeba"],
"img_align_celeba")
img_list_path = extracted_dirs["list_eval_partition"]
landmarks_path = extracted_dirs["landmarks_celeba"]
attr_path = extracted_dirs["list_attr_celeba"]
with tf.io.gfile.GFile(img_list_path) as f:
files = [
line.split()[0]
for line in f.readlines()
if int(line.split()[1]) == file_id
]
attributes = self._process_celeba_config_file(attr_path)
landmarks = self._process_celeba_config_file(landmarks_path)
for file_name in sorted(files):
path = os.path.join(filedir, file_name)
yield {
"image": path,
"landmarks": {
k: v for k, v in zip(landmarks[0], landmarks[1][file_name])
},
"attributes": {
# atributes value are either 1 or -1, so convert to bool
k: v > 0 for k, v in zip(attributes[0], attributes[1][file_name])
},
} | [
"Yields",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/celeba.py#L177-L207 | [
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train | QuickdrawBitmap._generate_examples | Generate QuickDraw bitmap examples.
Given a list of file paths with data for each class label, generate examples
in a random order.
Args:
file_paths: (dict of {str: str}) the paths to files containing the data,
indexed by label.
Yields:
The QuickDraw examples, as defined in the dataset info features. | tensorflow_datasets/image/quickdraw.py | def _generate_examples(self, file_paths):
"""Generate QuickDraw bitmap examples.
Given a list of file paths with data for each class label, generate examples
in a random order.
Args:
file_paths: (dict of {str: str}) the paths to files containing the data,
indexed by label.
Yields:
The QuickDraw examples, as defined in the dataset info features.
"""
for label, path in sorted(file_paths.items(), key=lambda x: x[0]):
with tf.io.gfile.GFile(path, "rb") as f:
class_images = np.load(f)
for np_image in class_images:
yield {
"image": np_image.reshape(_QUICKDRAW_IMAGE_SHAPE),
"label": label,
} | def _generate_examples(self, file_paths):
"""Generate QuickDraw bitmap examples.
Given a list of file paths with data for each class label, generate examples
in a random order.
Args:
file_paths: (dict of {str: str}) the paths to files containing the data,
indexed by label.
Yields:
The QuickDraw examples, as defined in the dataset info features.
"""
for label, path in sorted(file_paths.items(), key=lambda x: x[0]):
with tf.io.gfile.GFile(path, "rb") as f:
class_images = np.load(f)
for np_image in class_images:
yield {
"image": np_image.reshape(_QUICKDRAW_IMAGE_SHAPE),
"label": label,
} | [
"Generate",
"QuickDraw",
"bitmap",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/quickdraw.py#L97-L117 | [
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train | ensure_tf_install | Attempt to import tensorflow, and ensure its version is sufficient.
Raises:
ImportError: if either tensorflow is not importable or its version is
inadequate. | tensorflow_datasets/core/tf_compat.py | def ensure_tf_install(): # pylint: disable=g-statement-before-imports
"""Attempt to import tensorflow, and ensure its version is sufficient.
Raises:
ImportError: if either tensorflow is not importable or its version is
inadequate.
"""
try:
import tensorflow as tf
except ImportError:
# Print more informative error message, then reraise.
print("\n\nFailed to import TensorFlow. Please note that TensorFlow is not "
"installed by default when you install TensorFlow Datasets. This is "
"so that users can decide whether to install the GPU-enabled "
"TensorFlow package. To use TensorFlow Datasets, please install the "
"most recent version of TensorFlow, by following instructions at "
"https://tensorflow.org/install.\n\n")
raise
tf_version = distutils.version.LooseVersion(tf.__version__)
v_1_12 = distutils.version.LooseVersion("1.12.0")
if tf_version < v_1_12:
raise ImportError(
"This version of TensorFlow Datasets requires TensorFlow "
"version >= {required}; Detected an installation of version {present}. "
"Please upgrade TensorFlow to proceed.".format(
required="1.12.0",
present=tf.__version__))
_patch_tf(tf) | def ensure_tf_install(): # pylint: disable=g-statement-before-imports
"""Attempt to import tensorflow, and ensure its version is sufficient.
Raises:
ImportError: if either tensorflow is not importable or its version is
inadequate.
"""
try:
import tensorflow as tf
except ImportError:
# Print more informative error message, then reraise.
print("\n\nFailed to import TensorFlow. Please note that TensorFlow is not "
"installed by default when you install TensorFlow Datasets. This is "
"so that users can decide whether to install the GPU-enabled "
"TensorFlow package. To use TensorFlow Datasets, please install the "
"most recent version of TensorFlow, by following instructions at "
"https://tensorflow.org/install.\n\n")
raise
tf_version = distutils.version.LooseVersion(tf.__version__)
v_1_12 = distutils.version.LooseVersion("1.12.0")
if tf_version < v_1_12:
raise ImportError(
"This version of TensorFlow Datasets requires TensorFlow "
"version >= {required}; Detected an installation of version {present}. "
"Please upgrade TensorFlow to proceed.".format(
required="1.12.0",
present=tf.__version__))
_patch_tf(tf) | [
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"and",
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"version",
"is",
"sufficient",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/tf_compat.py#L39-L67 | [
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train | _patch_tf | Patch TF to maintain compatibility across versions. | tensorflow_datasets/core/tf_compat.py | def _patch_tf(tf):
"""Patch TF to maintain compatibility across versions."""
global TF_PATCH
if TF_PATCH:
return
v_1_12 = distutils.version.LooseVersion("1.12.0")
v_1_13 = distutils.version.LooseVersion("1.13.0")
v_2 = distutils.version.LooseVersion("2.0.0")
tf_version = distutils.version.LooseVersion(tf.__version__)
if v_1_12 <= tf_version < v_1_13:
# TODO(b/123930850): remove when 1.13 is stable.
TF_PATCH = "tf1_12"
_patch_for_tf1_12(tf)
elif v_1_13 <= tf_version < v_2:
TF_PATCH = "tf1_13"
_patch_for_tf1_13(tf)
else:
TF_PATCH = "tf2"
_patch_for_tf2(tf) | def _patch_tf(tf):
"""Patch TF to maintain compatibility across versions."""
global TF_PATCH
if TF_PATCH:
return
v_1_12 = distutils.version.LooseVersion("1.12.0")
v_1_13 = distutils.version.LooseVersion("1.13.0")
v_2 = distutils.version.LooseVersion("2.0.0")
tf_version = distutils.version.LooseVersion(tf.__version__)
if v_1_12 <= tf_version < v_1_13:
# TODO(b/123930850): remove when 1.13 is stable.
TF_PATCH = "tf1_12"
_patch_for_tf1_12(tf)
elif v_1_13 <= tf_version < v_2:
TF_PATCH = "tf1_13"
_patch_for_tf1_13(tf)
else:
TF_PATCH = "tf2"
_patch_for_tf2(tf) | [
"Patch",
"TF",
"to",
"maintain",
"compatibility",
"across",
"versions",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/tf_compat.py#L70-L89 | [
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train | _patch_for_tf1_12 | Monkey patch tf 1.12 so tfds can use it. | tensorflow_datasets/core/tf_compat.py | def _patch_for_tf1_12(tf):
"""Monkey patch tf 1.12 so tfds can use it."""
tf.io.gfile = tf.gfile
tf.io.gfile.copy = tf.gfile.Copy
tf.io.gfile.exists = tf.gfile.Exists
tf.io.gfile.glob = tf.gfile.Glob
tf.io.gfile.isdir = tf.gfile.IsDirectory
tf.io.gfile.listdir = tf.gfile.ListDirectory
tf.io.gfile.makedirs = tf.gfile.MakeDirs
tf.io.gfile.mkdir = tf.gfile.MkDir
tf.io.gfile.remove = tf.gfile.Remove
tf.io.gfile.rename = tf.gfile.Rename
tf.io.gfile.rmtree = tf.gfile.DeleteRecursively
tf.io.gfile.stat = tf.gfile.Stat
tf.io.gfile.walk = tf.gfile.Walk
tf.io.gfile.GFile = tf.gfile.GFile
tf.data.experimental = tf.contrib.data
tf.compat.v1 = types.ModuleType("tf.compat.v1")
tf.compat.v1.assert_greater = tf.assert_greater
tf.compat.v1.placeholder = tf.placeholder
tf.compat.v1.ConfigProto = tf.ConfigProto
tf.compat.v1.Session = tf.Session
tf.compat.v1.enable_eager_execution = tf.enable_eager_execution
tf.compat.v1.io = tf.io
tf.compat.v1.data = tf.data
tf.compat.v1.data.Dataset = tf.data.Dataset
tf.compat.v1.data.make_one_shot_iterator = (
lambda ds: ds.make_one_shot_iterator())
tf.compat.v1.train = tf.train
tf.compat.v1.global_variables_initializer = tf.global_variables_initializer
tf.compat.v1.test = tf.test
tf.compat.v1.test.get_temp_dir = tf.test.get_temp_dir
tf.nest = tf.contrib.framework.nest | def _patch_for_tf1_12(tf):
"""Monkey patch tf 1.12 so tfds can use it."""
tf.io.gfile = tf.gfile
tf.io.gfile.copy = tf.gfile.Copy
tf.io.gfile.exists = tf.gfile.Exists
tf.io.gfile.glob = tf.gfile.Glob
tf.io.gfile.isdir = tf.gfile.IsDirectory
tf.io.gfile.listdir = tf.gfile.ListDirectory
tf.io.gfile.makedirs = tf.gfile.MakeDirs
tf.io.gfile.mkdir = tf.gfile.MkDir
tf.io.gfile.remove = tf.gfile.Remove
tf.io.gfile.rename = tf.gfile.Rename
tf.io.gfile.rmtree = tf.gfile.DeleteRecursively
tf.io.gfile.stat = tf.gfile.Stat
tf.io.gfile.walk = tf.gfile.Walk
tf.io.gfile.GFile = tf.gfile.GFile
tf.data.experimental = tf.contrib.data
tf.compat.v1 = types.ModuleType("tf.compat.v1")
tf.compat.v1.assert_greater = tf.assert_greater
tf.compat.v1.placeholder = tf.placeholder
tf.compat.v1.ConfigProto = tf.ConfigProto
tf.compat.v1.Session = tf.Session
tf.compat.v1.enable_eager_execution = tf.enable_eager_execution
tf.compat.v1.io = tf.io
tf.compat.v1.data = tf.data
tf.compat.v1.data.Dataset = tf.data.Dataset
tf.compat.v1.data.make_one_shot_iterator = (
lambda ds: ds.make_one_shot_iterator())
tf.compat.v1.train = tf.train
tf.compat.v1.global_variables_initializer = tf.global_variables_initializer
tf.compat.v1.test = tf.test
tf.compat.v1.test.get_temp_dir = tf.test.get_temp_dir
tf.nest = tf.contrib.framework.nest | [
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"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/tf_compat.py#L100-L132 | [
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... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _patch_for_tf1_13 | Monkey patch tf 1.13 so tfds can use it. | tensorflow_datasets/core/tf_compat.py | def _patch_for_tf1_13(tf):
"""Monkey patch tf 1.13 so tfds can use it."""
if not hasattr(tf.io.gfile, "GFile"):
tf.io.gfile.GFile = tf.gfile.GFile
if not hasattr(tf, "nest"):
tf.nest = tf.contrib.framework.nest
if not hasattr(tf.compat, "v2"):
tf.compat.v2 = types.ModuleType("tf.compat.v2")
tf.compat.v2.data = types.ModuleType("tf.compat.v2.data")
from tensorflow.python.data.ops import dataset_ops
tf.compat.v2.data.Dataset = dataset_ops.DatasetV2
if not hasattr(tf.compat.v2.data.Dataset, "output_shapes"):
from tensorflow.python.data.ops import dataset_ops
if hasattr(dataset_ops, "get_legacy_output_shapes"):
tf.compat.v2.data.Dataset.output_shapes = property(
dataset_ops.get_legacy_output_shapes)
tf.compat.v2.data.Dataset.output_types = property(
dataset_ops.get_legacy_output_types) | def _patch_for_tf1_13(tf):
"""Monkey patch tf 1.13 so tfds can use it."""
if not hasattr(tf.io.gfile, "GFile"):
tf.io.gfile.GFile = tf.gfile.GFile
if not hasattr(tf, "nest"):
tf.nest = tf.contrib.framework.nest
if not hasattr(tf.compat, "v2"):
tf.compat.v2 = types.ModuleType("tf.compat.v2")
tf.compat.v2.data = types.ModuleType("tf.compat.v2.data")
from tensorflow.python.data.ops import dataset_ops
tf.compat.v2.data.Dataset = dataset_ops.DatasetV2
if not hasattr(tf.compat.v2.data.Dataset, "output_shapes"):
from tensorflow.python.data.ops import dataset_ops
if hasattr(dataset_ops, "get_legacy_output_shapes"):
tf.compat.v2.data.Dataset.output_shapes = property(
dataset_ops.get_legacy_output_shapes)
tf.compat.v2.data.Dataset.output_types = property(
dataset_ops.get_legacy_output_types) | [
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train | is_dataset | Whether ds is a Dataset. Compatible across TF versions. | tensorflow_datasets/core/tf_compat.py | def is_dataset(ds):
"""Whether ds is a Dataset. Compatible across TF versions."""
import tensorflow as tf
from tensorflow_datasets.core.utils import py_utils
dataset_types = [tf.data.Dataset]
v1_ds = py_utils.rgetattr(tf, "compat.v1.data.Dataset", None)
v2_ds = py_utils.rgetattr(tf, "compat.v2.data.Dataset", None)
if v1_ds is not None:
dataset_types.append(v1_ds)
if v2_ds is not None:
dataset_types.append(v2_ds)
return isinstance(ds, tuple(dataset_types)) | def is_dataset(ds):
"""Whether ds is a Dataset. Compatible across TF versions."""
import tensorflow as tf
from tensorflow_datasets.core.utils import py_utils
dataset_types = [tf.data.Dataset]
v1_ds = py_utils.rgetattr(tf, "compat.v1.data.Dataset", None)
v2_ds = py_utils.rgetattr(tf, "compat.v2.data.Dataset", None)
if v1_ds is not None:
dataset_types.append(v1_ds)
if v2_ds is not None:
dataset_types.append(v2_ds)
return isinstance(ds, tuple(dataset_types)) | [
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train | TedMultiTranslate._generate_examples | This function returns the examples in the raw (text) form. | tensorflow_datasets/translate/ted_multi.py | def _generate_examples(self, data_file):
"""This function returns the examples in the raw (text) form."""
with tf.io.gfile.GFile(data_file) as f:
reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
# Everything in the row except for 'talk_name' will be a translation.
# Missing/incomplete translations will contain the string "__NULL__" or
# "_ _ NULL _ _".
yield {
'translations': {
lang: text
for lang, text in six.iteritems(row)
if lang != 'talk_name' and _is_translation_complete(text)
},
'talk_name': row['talk_name']
} | def _generate_examples(self, data_file):
"""This function returns the examples in the raw (text) form."""
with tf.io.gfile.GFile(data_file) as f:
reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
# Everything in the row except for 'talk_name' will be a translation.
# Missing/incomplete translations will contain the string "__NULL__" or
# "_ _ NULL _ _".
yield {
'translations': {
lang: text
for lang, text in six.iteritems(row)
if lang != 'talk_name' and _is_translation_complete(text)
},
'talk_name': row['talk_name']
} | [
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train | MultiNLI._generate_examples | Generate mnli examples.
Args:
filepath: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings | tensorflow_datasets/text/multi_nli.py | def _generate_examples(self, filepath):
"""Generate mnli examples.
Args:
filepath: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(tf.io.gfile.GFile(filepath, "rb")):
if idx == 0:
continue # skip header
line = tf.compat.as_text(line.strip())
split_line = line.split("\t")
# Examples not marked with a three out of five consensus are marked with
# "-" and should not be used in standard evaluations.
if split_line[0] == "-":
continue
# Works for both splits even though dev has some extra human labels.
yield {
"premise": split_line[5],
"hypothesis": split_line[6],
"label": split_line[0]
} | def _generate_examples(self, filepath):
"""Generate mnli examples.
Args:
filepath: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(tf.io.gfile.GFile(filepath, "rb")):
if idx == 0:
continue # skip header
line = tf.compat.as_text(line.strip())
split_line = line.split("\t")
# Examples not marked with a three out of five consensus are marked with
# "-" and should not be used in standard evaluations.
if split_line[0] == "-":
continue
# Works for both splits even though dev has some extra human labels.
yield {
"premise": split_line[5],
"hypothesis": split_line[6],
"label": split_line[0]
} | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/multi_nli.py#L148-L171 | [
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train | ImageLabelFolder._split_generators | Returns SplitGenerators from the folder names. | tensorflow_datasets/image/image_folder.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators from the folder names."""
# At data creation time, parse the folder to deduce number of splits,
# labels, image size,
# The splits correspond to the high level folders
split_names = list_folders(dl_manager.manual_dir)
# Extract all label names and associated images
split_label_images = {} # dict[split_name][label_name] = list(img_paths)
for split_name in split_names:
split_dir = os.path.join(dl_manager.manual_dir, split_name)
split_label_images[split_name] = {
label_name: list_imgs(os.path.join(split_dir, label_name))
for label_name in list_folders(split_dir)
}
# Merge all label names from all splits to get the final list of labels
# Sorted list for determinism
labels = [split.keys() for split in split_label_images.values()]
labels = list(sorted(set(itertools.chain(*labels))))
# Could improve the automated encoding format detection
# Extract the list of all image paths
image_paths = [
image_paths
for label_images in split_label_images.values()
for image_paths in label_images.values()
]
if any(f.lower().endswith(".png") for f in itertools.chain(*image_paths)):
encoding_format = "png"
else:
encoding_format = "jpeg"
# Update the info.features. Those info will be automatically resored when
# the dataset is re-created
self.info.features["image"].set_encoding_format(encoding_format)
self.info.features["label"].names = labels
def num_examples(label_images):
return sum(len(imgs) for imgs in label_images.values())
# Define the splits
return [
tfds.core.SplitGenerator(
name=split_name,
# The number of shards is a dynamic function of the total
# number of images (between 0-10)
num_shards=min(10, max(num_examples(label_images) // 1000, 1)),
gen_kwargs=dict(label_images=label_images,),
) for split_name, label_images in split_label_images.items()
] | def _split_generators(self, dl_manager):
"""Returns SplitGenerators from the folder names."""
# At data creation time, parse the folder to deduce number of splits,
# labels, image size,
# The splits correspond to the high level folders
split_names = list_folders(dl_manager.manual_dir)
# Extract all label names and associated images
split_label_images = {} # dict[split_name][label_name] = list(img_paths)
for split_name in split_names:
split_dir = os.path.join(dl_manager.manual_dir, split_name)
split_label_images[split_name] = {
label_name: list_imgs(os.path.join(split_dir, label_name))
for label_name in list_folders(split_dir)
}
# Merge all label names from all splits to get the final list of labels
# Sorted list for determinism
labels = [split.keys() for split in split_label_images.values()]
labels = list(sorted(set(itertools.chain(*labels))))
# Could improve the automated encoding format detection
# Extract the list of all image paths
image_paths = [
image_paths
for label_images in split_label_images.values()
for image_paths in label_images.values()
]
if any(f.lower().endswith(".png") for f in itertools.chain(*image_paths)):
encoding_format = "png"
else:
encoding_format = "jpeg"
# Update the info.features. Those info will be automatically resored when
# the dataset is re-created
self.info.features["image"].set_encoding_format(encoding_format)
self.info.features["label"].names = labels
def num_examples(label_images):
return sum(len(imgs) for imgs in label_images.values())
# Define the splits
return [
tfds.core.SplitGenerator(
name=split_name,
# The number of shards is a dynamic function of the total
# number of images (between 0-10)
num_shards=min(10, max(num_examples(label_images) // 1000, 1)),
gen_kwargs=dict(label_images=label_images,),
) for split_name, label_images in split_label_images.items()
] | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/image_folder.py#L103-L154 | [
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train | ImageLabelFolder._generate_examples | Generate example for each image in the dict. | tensorflow_datasets/image/image_folder.py | def _generate_examples(self, label_images):
"""Generate example for each image in the dict."""
for label, image_paths in label_images.items():
for image_path in image_paths:
yield {
"image": image_path,
"label": label,
} | def _generate_examples(self, label_images):
"""Generate example for each image in the dict."""
for label, image_paths in label_images.items():
for image_path in image_paths:
yield {
"image": image_path,
"label": label,
} | [
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train | create_dataset_file | Create a new dataset from a template. | tensorflow_datasets/scripts/create_new_dataset.py | def create_dataset_file(root_dir, data):
"""Create a new dataset from a template."""
file_path = os.path.join(root_dir, '{dataset_type}', '{dataset_name}.py')
context = (
_HEADER + _DATASET_DEFAULT_IMPORTS + _CITATION
+ _DESCRIPTION + _DATASET_DEFAULTS
)
with gfile.GFile(file_path.format(**data), 'w') as f:
f.write(context.format(**data)) | def create_dataset_file(root_dir, data):
"""Create a new dataset from a template."""
file_path = os.path.join(root_dir, '{dataset_type}', '{dataset_name}.py')
context = (
_HEADER + _DATASET_DEFAULT_IMPORTS + _CITATION
+ _DESCRIPTION + _DATASET_DEFAULTS
)
with gfile.GFile(file_path.format(**data), 'w') as f:
f.write(context.format(**data)) | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/scripts/create_new_dataset.py#L155-L164 | [
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train | add_the_init | Append the new dataset file to the __init__.py. | tensorflow_datasets/scripts/create_new_dataset.py | def add_the_init(root_dir, data):
"""Append the new dataset file to the __init__.py."""
init_file = os.path.join(root_dir, '{dataset_type}', '__init__.py')
context = (
'from tensorflow_datasets.{dataset_type}.{dataset_name} import '
'{dataset_cls} # {TODO} Sort alphabetically\n'
)
with gfile.GFile(init_file.format(**data), 'a') as f:
f.write(context.format(**data)) | def add_the_init(root_dir, data):
"""Append the new dataset file to the __init__.py."""
init_file = os.path.join(root_dir, '{dataset_type}', '__init__.py')
context = (
'from tensorflow_datasets.{dataset_type}.{dataset_name} import '
'{dataset_cls} # {TODO} Sort alphabetically\n'
)
with gfile.GFile(init_file.format(**data), 'a') as f:
f.write(context.format(**data)) | [
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train | SvhnCropped._generate_examples | Generate examples as dicts.
Args:
filepath: `str` path of the file to process.
Yields:
Generator yielding the next samples | tensorflow_datasets/image/svhn.py | def _generate_examples(self, filepath):
"""Generate examples as dicts.
Args:
filepath: `str` path of the file to process.
Yields:
Generator yielding the next samples
"""
with tf.io.gfile.GFile(filepath, "rb") as f:
data = tfds.core.lazy_imports.scipy.io.loadmat(f)
# Maybe should shuffle ?
assert np.max(data["y"]) <= 10 # Sanity check
assert np.min(data["y"]) > 0
for image, label in zip(np.rollaxis(data["X"], -1), data["y"]):
yield {
"image": image,
"label": label % 10, # digit 0 is saved as 0 (instead of 10)
} | def _generate_examples(self, filepath):
"""Generate examples as dicts.
Args:
filepath: `str` path of the file to process.
Yields:
Generator yielding the next samples
"""
with tf.io.gfile.GFile(filepath, "rb") as f:
data = tfds.core.lazy_imports.scipy.io.loadmat(f)
# Maybe should shuffle ?
assert np.max(data["y"]) <= 10 # Sanity check
assert np.min(data["y"]) > 0
for image, label in zip(np.rollaxis(data["X"], -1), data["y"]):
yield {
"image": image,
"label": label % 10, # digit 0 is saved as 0 (instead of 10)
} | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/svhn.py#L92-L113 | [
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train | Chexpert._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/chexpert.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path = dl_manager.manual_dir
train_path = os.path.join(path, _TRAIN_DIR)
val_path = os.path.join(path, _VALIDATION_DIR)
if not tf.io.gfile.exists(train_path) or not tf.io.gfile.exists(val_path):
msg = ("You must download the dataset folder from CheXpert"
"website manually and place it into %s." % path)
raise AssertionError(msg)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=100,
gen_kwargs={
"imgs_path": path, # Relative img path is provided in csv
"csv_path": os.path.join(path, _TRAIN_LABELS_FNAME)
},
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=10,
gen_kwargs={
"imgs_path": path,
"csv_path": os.path.join(path, _VALIDATION_LABELS_FNAME)
},
),
] | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path = dl_manager.manual_dir
train_path = os.path.join(path, _TRAIN_DIR)
val_path = os.path.join(path, _VALIDATION_DIR)
if not tf.io.gfile.exists(train_path) or not tf.io.gfile.exists(val_path):
msg = ("You must download the dataset folder from CheXpert"
"website manually and place it into %s." % path)
raise AssertionError(msg)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=100,
gen_kwargs={
"imgs_path": path, # Relative img path is provided in csv
"csv_path": os.path.join(path, _TRAIN_LABELS_FNAME)
},
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=10,
gen_kwargs={
"imgs_path": path,
"csv_path": os.path.join(path, _VALIDATION_LABELS_FNAME)
},
),
] | [
"Returns",
"SplitGenerators",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/chexpert.py#L93-L121 | [
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train | Chexpert._generate_examples | Yields examples. | tensorflow_datasets/image/chexpert.py | def _generate_examples(self, imgs_path, csv_path):
"""Yields examples."""
with tf.io.gfile.GFile(csv_path) as csv_f:
reader = csv.DictReader(csv_f)
# Get keys for each label from csv
label_keys = reader.fieldnames[5:]
data = []
for row in reader:
# Get image based on indicated path in csv
name = row["Path"]
labels = [_LABELS[row[key]] for key in label_keys]
data.append((name, labels))
for name, labels in data:
yield {
"name": name,
"image": os.path.join(imgs_path, name),
"label": labels
} | def _generate_examples(self, imgs_path, csv_path):
"""Yields examples."""
with tf.io.gfile.GFile(csv_path) as csv_f:
reader = csv.DictReader(csv_f)
# Get keys for each label from csv
label_keys = reader.fieldnames[5:]
data = []
for row in reader:
# Get image based on indicated path in csv
name = row["Path"]
labels = [_LABELS[row[key]] for key in label_keys]
data.append((name, labels))
for name, labels in data:
yield {
"name": name,
"image": os.path.join(imgs_path, name),
"label": labels
} | [
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train | _make_builder_configs | Construct a list of BuilderConfigs.
Construct a list of 60 Imagenet2012CorruptedConfig objects, corresponding to
the 12 corruption types, with each type having 5 severities.
Returns:
A list of 60 Imagenet2012CorruptedConfig objects. | tensorflow_datasets/image/imagenet2012_corrupted.py | def _make_builder_configs():
"""Construct a list of BuilderConfigs.
Construct a list of 60 Imagenet2012CorruptedConfig objects, corresponding to
the 12 corruption types, with each type having 5 severities.
Returns:
A list of 60 Imagenet2012CorruptedConfig objects.
"""
config_list = []
for each_corruption in TYPE_LIST:
for each_severity in range(1, 6):
name_str = each_corruption + '_' + str(each_severity)
version_str = '0.0.1'
description_str = 'corruption type = ' + each_corruption + ', severity = '
description_str += str(each_severity)
config_list.append(
Imagenet2012CorruptedConfig(
name=name_str,
version=version_str,
description=description_str,
corruption_type=each_corruption,
severity=each_severity,
))
return config_list | def _make_builder_configs():
"""Construct a list of BuilderConfigs.
Construct a list of 60 Imagenet2012CorruptedConfig objects, corresponding to
the 12 corruption types, with each type having 5 severities.
Returns:
A list of 60 Imagenet2012CorruptedConfig objects.
"""
config_list = []
for each_corruption in TYPE_LIST:
for each_severity in range(1, 6):
name_str = each_corruption + '_' + str(each_severity)
version_str = '0.0.1'
description_str = 'corruption type = ' + each_corruption + ', severity = '
description_str += str(each_severity)
config_list.append(
Imagenet2012CorruptedConfig(
name=name_str,
version=version_str,
description=description_str,
corruption_type=each_corruption,
severity=each_severity,
))
return config_list | [
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train | Imagenet2012Corrupted._split_generators | Return the validation split of ImageNet2012.
Args:
dl_manager: download manager object.
Returns:
validation split. | tensorflow_datasets/image/imagenet2012_corrupted.py | def _split_generators(self, dl_manager):
"""Return the validation split of ImageNet2012.
Args:
dl_manager: download manager object.
Returns:
validation split.
"""
splits = super(Imagenet2012Corrupted, self)._split_generators(dl_manager)
validation = splits[1]
return [validation] | def _split_generators(self, dl_manager):
"""Return the validation split of ImageNet2012.
Args:
dl_manager: download manager object.
Returns:
validation split.
"""
splits = super(Imagenet2012Corrupted, self)._split_generators(dl_manager)
validation = splits[1]
return [validation] | [
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train | Imagenet2012Corrupted._generate_examples_validation | Generate corrupted imagenet validation data.
Apply corruptions to the raw images according to self.corruption_type.
Args:
archive: an iterator for the raw dataset.
labels: a dictionary that maps the file names to imagenet labels.
Yields:
dictionary with the file name, an image file objective, and label of each
imagenet validation data. | tensorflow_datasets/image/imagenet2012_corrupted.py | def _generate_examples_validation(self, archive, labels):
"""Generate corrupted imagenet validation data.
Apply corruptions to the raw images according to self.corruption_type.
Args:
archive: an iterator for the raw dataset.
labels: a dictionary that maps the file names to imagenet labels.
Yields:
dictionary with the file name, an image file objective, and label of each
imagenet validation data.
"""
# Get the current random seeds.
numpy_st0 = np.random.get_state()
# Set new random seeds.
np.random.seed(135)
logging.warning('Overwriting cv2 RNG seed.')
tfds.core.lazy_imports.cv2.setRNGSeed(357)
for example in super(Imagenet2012Corrupted,
self)._generate_examples_validation(archive, labels):
with tf.Graph().as_default():
tf_img = tf.image.decode_jpeg(example['image'].read(), channels=3)
image_np = tfds.as_numpy(tf_img)
example['image'] = self._get_corrupted_example(image_np)
yield example
# Reset the seeds back to their original values.
np.random.set_state(numpy_st0) | def _generate_examples_validation(self, archive, labels):
"""Generate corrupted imagenet validation data.
Apply corruptions to the raw images according to self.corruption_type.
Args:
archive: an iterator for the raw dataset.
labels: a dictionary that maps the file names to imagenet labels.
Yields:
dictionary with the file name, an image file objective, and label of each
imagenet validation data.
"""
# Get the current random seeds.
numpy_st0 = np.random.get_state()
# Set new random seeds.
np.random.seed(135)
logging.warning('Overwriting cv2 RNG seed.')
tfds.core.lazy_imports.cv2.setRNGSeed(357)
for example in super(Imagenet2012Corrupted,
self)._generate_examples_validation(archive, labels):
with tf.Graph().as_default():
tf_img = tf.image.decode_jpeg(example['image'].read(), channels=3)
image_np = tfds.as_numpy(tf_img)
example['image'] = self._get_corrupted_example(image_np)
yield example
# Reset the seeds back to their original values.
np.random.set_state(numpy_st0) | [
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train | Imagenet2012Corrupted._get_corrupted_example | Return corrupted images.
Args:
x: numpy array, uncorrupted image.
Returns:
numpy array, corrupted images. | tensorflow_datasets/image/imagenet2012_corrupted.py | def _get_corrupted_example(self, x):
"""Return corrupted images.
Args:
x: numpy array, uncorrupted image.
Returns:
numpy array, corrupted images.
"""
corruption_type = self.builder_config.corruption_type
severity = self.builder_config.severity
return {
'gaussian_noise': corruptions.gaussian_noise,
'shot_noise': corruptions.shot_noise,
'impulse_noise': corruptions.impulse_noise,
'defocus_blur': corruptions.defocus_blur,
'frosted_glass_blur': corruptions.frosted_glass_blur,
'zoom_blur': corruptions.zoom_blur,
'fog': corruptions.fog,
'brightness': corruptions.brightness,
'contrast': corruptions.contrast,
'elastic': corruptions.elastic,
'pixelate': corruptions.pixelate,
'jpeg_compression': corruptions.jpeg_compression,
}[corruption_type](x, severity) | def _get_corrupted_example(self, x):
"""Return corrupted images.
Args:
x: numpy array, uncorrupted image.
Returns:
numpy array, corrupted images.
"""
corruption_type = self.builder_config.corruption_type
severity = self.builder_config.severity
return {
'gaussian_noise': corruptions.gaussian_noise,
'shot_noise': corruptions.shot_noise,
'impulse_noise': corruptions.impulse_noise,
'defocus_blur': corruptions.defocus_blur,
'frosted_glass_blur': corruptions.frosted_glass_blur,
'zoom_blur': corruptions.zoom_blur,
'fog': corruptions.fog,
'brightness': corruptions.brightness,
'contrast': corruptions.contrast,
'elastic': corruptions.elastic,
'pixelate': corruptions.pixelate,
'jpeg_compression': corruptions.jpeg_compression,
}[corruption_type](x, severity) | [
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train | assert_shape_match | Ensure the shape1 match the pattern given by shape2.
Ex:
assert_shape_match((64, 64, 3), (None, None, 3))
Args:
shape1 (tuple): Static shape
shape2 (tuple): Dynamic shape (can contain None) | tensorflow_datasets/core/utils/tf_utils.py | def assert_shape_match(shape1, shape2):
"""Ensure the shape1 match the pattern given by shape2.
Ex:
assert_shape_match((64, 64, 3), (None, None, 3))
Args:
shape1 (tuple): Static shape
shape2 (tuple): Dynamic shape (can contain None)
"""
shape1 = tf.TensorShape(shape1)
shape2 = tf.TensorShape(shape2)
if shape1.ndims is None or shape2.ndims is None:
raise ValueError('Shapes must have known rank. Got %s and %s.' %
(shape1.ndims, shape2.ndims))
shape1.assert_same_rank(shape2)
shape1.assert_is_compatible_with(shape2) | def assert_shape_match(shape1, shape2):
"""Ensure the shape1 match the pattern given by shape2.
Ex:
assert_shape_match((64, 64, 3), (None, None, 3))
Args:
shape1 (tuple): Static shape
shape2 (tuple): Dynamic shape (can contain None)
"""
shape1 = tf.TensorShape(shape1)
shape2 = tf.TensorShape(shape2)
if shape1.ndims is None or shape2.ndims is None:
raise ValueError('Shapes must have known rank. Got %s and %s.' %
(shape1.ndims, shape2.ndims))
shape1.assert_same_rank(shape2)
shape1.assert_is_compatible_with(shape2) | [
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train | raw_nogpu_session | tf.Session, hiding GPUs. | tensorflow_datasets/core/utils/tf_utils.py | def raw_nogpu_session(graph=None):
"""tf.Session, hiding GPUs."""
config = tf.compat.v1.ConfigProto(device_count={'GPU': 0})
return tf.compat.v1.Session(config=config, graph=graph) | def raw_nogpu_session(graph=None):
"""tf.Session, hiding GPUs."""
config = tf.compat.v1.ConfigProto(device_count={'GPU': 0})
return tf.compat.v1.Session(config=config, graph=graph) | [
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train | maybe_with_graph | Eager-compatible Graph().as_default() yielding the graph. | tensorflow_datasets/core/utils/tf_utils.py | def maybe_with_graph(graph=None, create_if_none=True):
"""Eager-compatible Graph().as_default() yielding the graph."""
if tf.executing_eagerly():
yield None
else:
if graph is None and create_if_none:
graph = tf.Graph()
if graph is None:
yield None
else:
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"""Eager-compatible Graph().as_default() yielding the graph."""
if tf.executing_eagerly():
yield None
else:
if graph is None and create_if_none:
graph = tf.Graph()
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train | TFGraphRunner.run | Execute the given TensorFlow function. | tensorflow_datasets/core/utils/tf_utils.py | def run(self, fct, input_):
"""Execute the given TensorFlow function."""
# TF 2.0
if tf.executing_eagerly():
return fct(input_).numpy()
# TF 1.0
else:
# Should compile the function if this is the first time encountered
if not isinstance(input_, np.ndarray):
input_ = np.array(input_)
run_args = RunArgs(fct=fct, input=input_)
signature = self._build_signature(run_args)
if signature not in self._graph_run_cache:
graph_run = self._build_graph_run(run_args)
self._graph_run_cache[signature] = graph_run
else:
graph_run = self._graph_run_cache[signature]
# Then execute the cached graph
return graph_run.session.run(
graph_run.output,
feed_dict={graph_run.placeholder: input_},
) | def run(self, fct, input_):
"""Execute the given TensorFlow function."""
# TF 2.0
if tf.executing_eagerly():
return fct(input_).numpy()
# TF 1.0
else:
# Should compile the function if this is the first time encountered
if not isinstance(input_, np.ndarray):
input_ = np.array(input_)
run_args = RunArgs(fct=fct, input=input_)
signature = self._build_signature(run_args)
if signature not in self._graph_run_cache:
graph_run = self._build_graph_run(run_args)
self._graph_run_cache[signature] = graph_run
else:
graph_run = self._graph_run_cache[signature]
# Then execute the cached graph
return graph_run.session.run(
graph_run.output,
feed_dict={graph_run.placeholder: input_},
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train | TFGraphRunner._build_graph_run | Create a new graph for the given args. | tensorflow_datasets/core/utils/tf_utils.py | def _build_graph_run(self, run_args):
"""Create a new graph for the given args."""
# Could try to use tfe.py_func(fct) but this would require knowing
# information about the signature of the function.
# Create a new graph:
with tf.Graph().as_default() as g:
# Create placeholder
input_ = run_args.input
placeholder = tf.compat.v1.placeholder(
dtype=input_.dtype, shape=input_.shape)
output = run_args.fct(placeholder)
return GraphRun(
session=raw_nogpu_session(g),
graph=g,
placeholder=placeholder,
output=output,
) | def _build_graph_run(self, run_args):
"""Create a new graph for the given args."""
# Could try to use tfe.py_func(fct) but this would require knowing
# information about the signature of the function.
# Create a new graph:
with tf.Graph().as_default() as g:
# Create placeholder
input_ = run_args.input
placeholder = tf.compat.v1.placeholder(
dtype=input_.dtype, shape=input_.shape)
output = run_args.fct(placeholder)
return GraphRun(
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train | TFGraphRunner._build_signature | Create a unique signature for each fct/inputs. | tensorflow_datasets/core/utils/tf_utils.py | def _build_signature(self, run_args):
"""Create a unique signature for each fct/inputs."""
return (id(run_args.fct), run_args.input.dtype, run_args.input.shape) | def _build_signature(self, run_args):
"""Create a unique signature for each fct/inputs."""
return (id(run_args.fct), run_args.input.dtype, run_args.input.shape) | [
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train | Video.encode_example | Converts the given image into a dict convertible to tf example. | tensorflow_datasets/core/features/video_feature.py | def encode_example(self, video_or_path_or_fobj):
"""Converts the given image into a dict convertible to tf example."""
if isinstance(video_or_path_or_fobj, six.string_types):
if not os.path.isfile(video_or_path_or_fobj):
_, video_temp_path = tempfile.mkstemp()
try:
tf.gfile.Copy(video_or_path_or_fobj, video_temp_path, overwrite=True)
encoded_video = self._ffmpeg_decode(video_temp_path)
finally:
os.unlink(video_temp_path)
else:
encoded_video = self._ffmpeg_decode(video_or_path_or_fobj)
elif hasattr(video_or_path_or_fobj, 'read'):
encoded_video = self._ffmpeg_decode(video_or_path_or_fobj)
else:
encoded_video = video_or_path_or_fobj
return super(Video, self).encode_example(encoded_video) | def encode_example(self, video_or_path_or_fobj):
"""Converts the given image into a dict convertible to tf example."""
if isinstance(video_or_path_or_fobj, six.string_types):
if not os.path.isfile(video_or_path_or_fobj):
_, video_temp_path = tempfile.mkstemp()
try:
tf.gfile.Copy(video_or_path_or_fobj, video_temp_path, overwrite=True)
encoded_video = self._ffmpeg_decode(video_temp_path)
finally:
os.unlink(video_temp_path)
else:
encoded_video = self._ffmpeg_decode(video_or_path_or_fobj)
elif hasattr(video_or_path_or_fobj, 'read'):
encoded_video = self._ffmpeg_decode(video_or_path_or_fobj)
else:
encoded_video = video_or_path_or_fobj
return super(Video, self).encode_example(encoded_video) | [
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train | RockPaperScissors._generate_examples | Generate rock, paper or scissors images and labels given the directory path.
Args:
archive: object that iterates over the zip.
Yields:
The image path and its corresponding label. | tensorflow_datasets/image/rock_paper_scissors.py | def _generate_examples(self, archive):
"""Generate rock, paper or scissors images and labels given the directory path.
Args:
archive: object that iterates over the zip.
Yields:
The image path and its corresponding label.
"""
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # if anything other than .png; skip
continue
label = res.group(2).lower()
yield {
"image": fobj,
"label": label,
} | def _generate_examples(self, archive):
"""Generate rock, paper or scissors images and labels given the directory path.
Args:
archive: object that iterates over the zip.
Yields:
The image path and its corresponding label.
"""
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # if anything other than .png; skip
continue
label = res.group(2).lower()
yield {
"image": fobj,
"label": label,
} | [
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train | Titanic._generate_examples | Generate features and target given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features and the target | tensorflow_datasets/structured/titanic.py | def _generate_examples(self, file_path):
"""Generate features and target given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features and the target
"""
with tf.io.gfile.GFile(file_path) as f:
raw_data = csv.DictReader(f)
for row in raw_data:
survive_val = row.pop("survived")
yield {
"survived": convert_to_label(survive_val, _SURVIVED_DICT),
"features": {
name: FEATURE_DICT[name][1](value)
for name, value in row.items()
}
} | def _generate_examples(self, file_path):
"""Generate features and target given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features and the target
"""
with tf.io.gfile.GFile(file_path) as f:
raw_data = csv.DictReader(f)
for row in raw_data:
survive_val = row.pop("survived")
yield {
"survived": convert_to_label(survive_val, _SURVIVED_DICT),
"features": {
name: FEATURE_DICT[name][1](value)
for name, value in row.items()
}
} | [
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train | pad_decr | Strip ID 0 and decrement ids by 1. | tensorflow_datasets/core/features/text/text_encoder.py | def pad_decr(ids):
"""Strip ID 0 and decrement ids by 1."""
if len(ids) < 1:
return list(ids)
if not any(ids):
return [] # all padding.
idx = -1
while not ids[idx]:
idx -= 1
if idx == -1:
ids = ids
else:
ids = ids[:idx + 1]
return [i - 1 for i in ids] | def pad_decr(ids):
"""Strip ID 0 and decrement ids by 1."""
if len(ids) < 1:
return list(ids)
if not any(ids):
return [] # all padding.
idx = -1
while not ids[idx]:
idx -= 1
if idx == -1:
ids = ids
else:
ids = ids[:idx + 1]
return [i - 1 for i in ids] | [
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train | _prepare_reserved_tokens | Prepare reserved tokens and a regex for splitting them out of strings. | tensorflow_datasets/core/features/text/text_encoder.py | def _prepare_reserved_tokens(reserved_tokens):
"""Prepare reserved tokens and a regex for splitting them out of strings."""
reserved_tokens = [tf.compat.as_text(tok) for tok in reserved_tokens or []]
dups = _find_duplicates(reserved_tokens)
if dups:
raise ValueError("Duplicates found in tokens: %s" % dups)
reserved_tokens_re = _make_reserved_tokens_re(reserved_tokens)
return reserved_tokens, reserved_tokens_re | def _prepare_reserved_tokens(reserved_tokens):
"""Prepare reserved tokens and a regex for splitting them out of strings."""
reserved_tokens = [tf.compat.as_text(tok) for tok in reserved_tokens or []]
dups = _find_duplicates(reserved_tokens)
if dups:
raise ValueError("Duplicates found in tokens: %s" % dups)
reserved_tokens_re = _make_reserved_tokens_re(reserved_tokens)
return reserved_tokens, reserved_tokens_re | [
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train | _make_reserved_tokens_re | Constructs compiled regex to parse out reserved tokens. | tensorflow_datasets/core/features/text/text_encoder.py | def _make_reserved_tokens_re(reserved_tokens):
"""Constructs compiled regex to parse out reserved tokens."""
if not reserved_tokens:
return None
escaped_tokens = [_re_escape(rt) for rt in reserved_tokens]
pattern = "(%s)" % "|".join(escaped_tokens)
reserved_tokens_re = _re_compile(pattern)
return reserved_tokens_re | def _make_reserved_tokens_re(reserved_tokens):
"""Constructs compiled regex to parse out reserved tokens."""
if not reserved_tokens:
return None
escaped_tokens = [_re_escape(rt) for rt in reserved_tokens]
pattern = "(%s)" % "|".join(escaped_tokens)
reserved_tokens_re = _re_compile(pattern)
return reserved_tokens_re | [
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train | write_lines_to_file | Writes lines to file prepended by header and metadata. | tensorflow_datasets/core/features/text/text_encoder.py | def write_lines_to_file(cls_name, filename, lines, metadata_dict):
"""Writes lines to file prepended by header and metadata."""
metadata_dict = metadata_dict or {}
header_line = "%s%s" % (_HEADER_PREFIX, cls_name)
metadata_line = "%s%s" % (_METADATA_PREFIX,
json.dumps(metadata_dict, sort_keys=True))
with tf.io.gfile.GFile(filename, "wb") as f:
for line in [header_line, metadata_line]:
f.write(tf.compat.as_bytes(line))
f.write(tf.compat.as_bytes("\n"))
if lines:
f.write(tf.compat.as_bytes("\n".join(lines)))
f.write(tf.compat.as_bytes("\n")) | def write_lines_to_file(cls_name, filename, lines, metadata_dict):
"""Writes lines to file prepended by header and metadata."""
metadata_dict = metadata_dict or {}
header_line = "%s%s" % (_HEADER_PREFIX, cls_name)
metadata_line = "%s%s" % (_METADATA_PREFIX,
json.dumps(metadata_dict, sort_keys=True))
with tf.io.gfile.GFile(filename, "wb") as f:
for line in [header_line, metadata_line]:
f.write(tf.compat.as_bytes(line))
f.write(tf.compat.as_bytes("\n"))
if lines:
f.write(tf.compat.as_bytes("\n".join(lines)))
f.write(tf.compat.as_bytes("\n")) | [
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train | read_lines_from_file | Read lines from file, parsing out header and metadata. | tensorflow_datasets/core/features/text/text_encoder.py | def read_lines_from_file(cls_name, filename):
"""Read lines from file, parsing out header and metadata."""
with tf.io.gfile.GFile(filename, "rb") as f:
lines = [tf.compat.as_text(line)[:-1] for line in f]
header_line = "%s%s" % (_HEADER_PREFIX, cls_name)
if lines[0] != header_line:
raise ValueError("File {fname} does not seem to have been created from "
"{name}.save_to_file.".format(
fname=filename, name=cls_name))
metadata_dict = json.loads(lines[1][len(_METADATA_PREFIX):])
return lines[2:], metadata_dict | def read_lines_from_file(cls_name, filename):
"""Read lines from file, parsing out header and metadata."""
with tf.io.gfile.GFile(filename, "rb") as f:
lines = [tf.compat.as_text(line)[:-1] for line in f]
header_line = "%s%s" % (_HEADER_PREFIX, cls_name)
if lines[0] != header_line:
raise ValueError("File {fname} does not seem to have been created from "
"{name}.save_to_file.".format(
fname=filename, name=cls_name))
metadata_dict = json.loads(lines[1][len(_METADATA_PREFIX):])
return lines[2:], metadata_dict | [
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train | Tokenizer.tokenize | Splits a string into tokens. | tensorflow_datasets/core/features/text/text_encoder.py | def tokenize(self, s):
"""Splits a string into tokens."""
s = tf.compat.as_text(s)
if self.reserved_tokens:
# First split out the reserved tokens
substrs = self._reserved_tokens_re.split(s)
else:
substrs = [s]
toks = []
for substr in substrs:
if substr in self.reserved_tokens:
toks.append(substr)
else:
toks.extend(self._alphanum_re.split(substr))
# Filter out empty strings
toks = [t for t in toks if t]
return toks | def tokenize(self, s):
"""Splits a string into tokens."""
s = tf.compat.as_text(s)
if self.reserved_tokens:
# First split out the reserved tokens
substrs = self._reserved_tokens_re.split(s)
else:
substrs = [s]
toks = []
for substr in substrs:
if substr in self.reserved_tokens:
toks.append(substr)
else:
toks.extend(self._alphanum_re.split(substr))
# Filter out empty strings
toks = [t for t in toks if t]
return toks | [
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train | slice_to_percent_mask | Convert a python slice [15:50] into a list[bool] mask of 100 elements. | tensorflow_datasets/core/splits.py | def slice_to_percent_mask(slice_value):
"""Convert a python slice [15:50] into a list[bool] mask of 100 elements."""
if slice_value is None:
slice_value = slice(None)
# Select only the elements of the slice
selected = set(list(range(100))[slice_value])
# Create the binary mask
return [i in selected for i in range(100)] | def slice_to_percent_mask(slice_value):
"""Convert a python slice [15:50] into a list[bool] mask of 100 elements."""
if slice_value is None:
slice_value = slice(None)
# Select only the elements of the slice
selected = set(list(range(100))[slice_value])
# Create the binary mask
return [i in selected for i in range(100)] | [
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train | get_shard_id2num_examples | Return the mapping shard_id=>num_examples, assuming round-robin. | tensorflow_datasets/core/splits.py | def get_shard_id2num_examples(num_shards, total_num_examples):
"""Return the mapping shard_id=>num_examples, assuming round-robin."""
# TODO(b/130353071): This has the strong assumption that the shards have
# been written in a round-robin fashion. This assumption does not hold, for
# instance, with Beam generation. The mapping shard_id=>num_examples
# should be computed during generation.
# Minimum number of example per shards
num_example_in_shard = total_num_examples // num_shards
shard_id2num_examples = [num_example_in_shard for _ in range(num_shards)]
# If there are remaining examples, we add them to the first shards
for shard_id in range(total_num_examples % num_shards):
shard_id2num_examples[shard_id] += 1
return shard_id2num_examples | def get_shard_id2num_examples(num_shards, total_num_examples):
"""Return the mapping shard_id=>num_examples, assuming round-robin."""
# TODO(b/130353071): This has the strong assumption that the shards have
# been written in a round-robin fashion. This assumption does not hold, for
# instance, with Beam generation. The mapping shard_id=>num_examples
# should be computed during generation.
# Minimum number of example per shards
num_example_in_shard = total_num_examples // num_shards
shard_id2num_examples = [num_example_in_shard for _ in range(num_shards)]
# If there are remaining examples, we add them to the first shards
for shard_id in range(total_num_examples % num_shards):
shard_id2num_examples[shard_id] += 1
return shard_id2num_examples | [
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train | compute_mask_offsets | Return the list of offsets associated with each shards.
Args:
shard_id2num_examples: `list[int]`, mapping shard_id=>num_examples
Returns:
mask_offsets: `list[int]`, offset to skip for each of the shard | tensorflow_datasets/core/splits.py | def compute_mask_offsets(shard_id2num_examples):
"""Return the list of offsets associated with each shards.
Args:
shard_id2num_examples: `list[int]`, mapping shard_id=>num_examples
Returns:
mask_offsets: `list[int]`, offset to skip for each of the shard
"""
total_num_examples = sum(shard_id2num_examples)
mask_offsets = []
total_num_examples = 0
for num_examples_in_shard in shard_id2num_examples:
# The offset (nb of examples to skip in the next shard) correspond to the
# number of examples remaining in the current shard
mask_offsets.append(total_num_examples % 100)
total_num_examples += num_examples_in_shard
return mask_offsets | def compute_mask_offsets(shard_id2num_examples):
"""Return the list of offsets associated with each shards.
Args:
shard_id2num_examples: `list[int]`, mapping shard_id=>num_examples
Returns:
mask_offsets: `list[int]`, offset to skip for each of the shard
"""
total_num_examples = sum(shard_id2num_examples)
mask_offsets = []
total_num_examples = 0
for num_examples_in_shard in shard_id2num_examples:
# The offset (nb of examples to skip in the next shard) correspond to the
# number of examples remaining in the current shard
mask_offsets.append(total_num_examples % 100)
total_num_examples += num_examples_in_shard
return mask_offsets | [
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train | check_splits_equals | Check that the two split dicts have the same names and num_shards. | tensorflow_datasets/core/splits.py | def check_splits_equals(splits1, splits2):
"""Check that the two split dicts have the same names and num_shards."""
if set(splits1) ^ set(splits2): # Name intersection should be null
return False
for _, (split1, split2) in utils.zip_dict(splits1, splits2):
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"""Check that the two split dicts have the same names and num_shards."""
if set(splits1) ^ set(splits2): # Name intersection should be null
return False
for _, (split1, split2) in utils.zip_dict(splits1, splits2):
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train | SplitDict.add | Add the split info. | tensorflow_datasets/core/splits.py | def add(self, split_info):
"""Add the split info."""
if split_info.name in self:
raise ValueError("Split {} already present".format(split_info.name))
# TODO(epot): Make sure this works with Named splits correctly.
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train | SplitDict.from_proto | Returns a new SplitDict initialized from the `repeated_split_infos`. | tensorflow_datasets/core/splits.py | def from_proto(cls, repeated_split_infos):
"""Returns a new SplitDict initialized from the `repeated_split_infos`."""
split_dict = cls()
for split_info_proto in repeated_split_infos:
split_info = SplitInfo()
split_info.CopyFrom(split_info_proto)
split_dict.add(split_info)
return split_dict | def from_proto(cls, repeated_split_infos):
"""Returns a new SplitDict initialized from the `repeated_split_infos`."""
split_dict = cls()
for split_info_proto in repeated_split_infos:
split_info = SplitInfo()
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split_dict.add(split_info)
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train | SplitDict.to_proto | Returns a list of SplitInfo protos that we have. | tensorflow_datasets/core/splits.py | def to_proto(self):
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# Return the proto.SplitInfo, sorted by name
return sorted((s.get_proto() for s in self.values()), key=lambda s: s.name) | def to_proto(self):
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train | Squad._generate_examples | This function returns the examples in the raw (text) form. | tensorflow_datasets/text/squad.py | def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
with tf.io.gfile.GFile(filepath) as f:
squad = json.load(f)
for article in squad["data"]:
if "title" in article:
title = article["title"].strip()
else:
title = ""
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context = paragraph["context"].strip()
for qa in paragraph["qas"]:
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id_ = qa["id"]
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answers = [answer["text"].strip() for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
example = {
"title": title,
"context": context,
"question": question,
"id": id_,
"answer_starts": answer_starts,
"answers": answers,
}
yield {
"question": example["question"],
# TODO(b/121176753): return all the answers.
"first_answer": example["answers"][0],
"context": example["context"]
} | def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
with tf.io.gfile.GFile(filepath) as f:
squad = json.load(f)
for article in squad["data"]:
if "title" in article:
title = article["title"].strip()
else:
title = ""
for paragraph in article["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
example = {
"title": title,
"context": context,
"question": question,
"id": id_,
"answer_starts": answer_starts,
"answers": answers,
}
yield {
"question": example["question"],
# TODO(b/121176753): return all the answers.
"first_answer": example["answers"][0],
"context": example["context"]
} | [
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train | ParaCrawl._generate_examples | This function returns the examples in the raw (text) form. | tensorflow_datasets/translate/para_crawl.py | def _generate_examples(self, data_file):
"""This function returns the examples in the raw (text) form."""
target_language = self.builder_config.target_language
with tf.io.gfile.GFile(data_file) as f:
for i, line in enumerate(f):
line_parts = line.strip().split("\t")
if len(line_parts) != 2:
raise ValueError(("Wrong data format in line {}. The line '{}' does "
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source, target = line_parts[0].strip(), line_parts[1].strip()
yield {"en": source, target_language: target} | def _generate_examples(self, data_file):
"""This function returns the examples in the raw (text) form."""
target_language = self.builder_config.target_language
with tf.io.gfile.GFile(data_file) as f:
for i, line in enumerate(f):
line_parts = line.strip().split("\t")
if len(line_parts) != 2:
raise ValueError(("Wrong data format in line {}. The line '{}' does "
"not have exactly one delimiter.").format(i, line))
source, target = line_parts[0].strip(), line_parts[1].strip()
yield {"en": source, target_language: target} | [
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train | build_synchronize_decorator | Returns a decorator which prevents concurrent calls to functions.
Usage:
synchronized = build_synchronize_decorator()
@synchronized
def read_value():
...
@synchronized
def write_value(x):
...
Returns:
make_threadsafe (fct): The decorator which lock all functions to which it
is applied under a same lock | tensorflow_datasets/core/download/util.py | def build_synchronize_decorator():
"""Returns a decorator which prevents concurrent calls to functions.
Usage:
synchronized = build_synchronize_decorator()
@synchronized
def read_value():
...
@synchronized
def write_value(x):
...
Returns:
make_threadsafe (fct): The decorator which lock all functions to which it
is applied under a same lock
"""
lock = threading.Lock()
def lock_decorator(fn):
@functools.wraps(fn)
def lock_decorated(*args, **kwargs):
with lock:
return fn(*args, **kwargs)
return lock_decorated
return lock_decorator | def build_synchronize_decorator():
"""Returns a decorator which prevents concurrent calls to functions.
Usage:
synchronized = build_synchronize_decorator()
@synchronized
def read_value():
...
@synchronized
def write_value(x):
...
Returns:
make_threadsafe (fct): The decorator which lock all functions to which it
is applied under a same lock
"""
lock = threading.Lock()
def lock_decorator(fn):
@functools.wraps(fn)
def lock_decorated(*args, **kwargs):
with lock:
return fn(*args, **kwargs)
return lock_decorated
return lock_decorator | [
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train | get_file_name | Returns file name of file at given url. | tensorflow_datasets/core/download/util.py | def get_file_name(url):
"""Returns file name of file at given url."""
return os.path.basename(urllib.parse.urlparse(url).path) or 'unknown_name' | def get_file_name(url):
"""Returns file name of file at given url."""
return os.path.basename(urllib.parse.urlparse(url).path) or 'unknown_name' | [
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train | _make_builder_configs | Make built-in Librispeech BuilderConfigs.
Uses 4 text encodings (plain text, bytes, subwords with 8k vocab, subwords
with 32k vocab) crossed with the data subsets (clean100, clean360, all).
Returns:
`list<tfds.audio.LibrispeechConfig>` | tensorflow_datasets/audio/librispeech.py | def _make_builder_configs():
"""Make built-in Librispeech BuilderConfigs.
Uses 4 text encodings (plain text, bytes, subwords with 8k vocab, subwords
with 32k vocab) crossed with the data subsets (clean100, clean360, all).
Returns:
`list<tfds.audio.LibrispeechConfig>`
"""
text_encoder_configs = [
None,
tfds.features.text.TextEncoderConfig(
name="bytes", encoder=tfds.features.text.ByteTextEncoder()),
tfds.features.text.TextEncoderConfig(
name="subwords8k",
encoder_cls=tfds.features.text.SubwordTextEncoder,
vocab_size=2**13),
tfds.features.text.TextEncoderConfig(
name="subwords32k",
encoder_cls=tfds.features.text.SubwordTextEncoder,
vocab_size=2**15),
]
version = "0.1.0"
configs = []
for text_encoder_config in text_encoder_configs:
for data in _DATA_OPTIONS:
config = LibrispeechConfig(
version=version, text_encoder_config=text_encoder_config, data=data)
configs.append(config)
return configs | def _make_builder_configs():
"""Make built-in Librispeech BuilderConfigs.
Uses 4 text encodings (plain text, bytes, subwords with 8k vocab, subwords
with 32k vocab) crossed with the data subsets (clean100, clean360, all).
Returns:
`list<tfds.audio.LibrispeechConfig>`
"""
text_encoder_configs = [
None,
tfds.features.text.TextEncoderConfig(
name="bytes", encoder=tfds.features.text.ByteTextEncoder()),
tfds.features.text.TextEncoderConfig(
name="subwords8k",
encoder_cls=tfds.features.text.SubwordTextEncoder,
vocab_size=2**13),
tfds.features.text.TextEncoderConfig(
name="subwords32k",
encoder_cls=tfds.features.text.SubwordTextEncoder,
vocab_size=2**15),
]
version = "0.1.0"
configs = []
for text_encoder_config in text_encoder_configs:
for data in _DATA_OPTIONS:
config = LibrispeechConfig(
version=version, text_encoder_config=text_encoder_config, data=data)
configs.append(config)
return configs | [
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train | _walk_librispeech_dir | Walk a Librispeech directory and yield examples. | tensorflow_datasets/audio/librispeech.py | def _walk_librispeech_dir(directory):
"""Walk a Librispeech directory and yield examples."""
directory = os.path.join(directory, "LibriSpeech")
for path, _, files in tf.io.gfile.walk(directory):
if not files:
continue
transcript_file = [f for f in files if f.endswith(".txt")]
if not transcript_file:
continue
assert len(transcript_file) == 1
transcript_file, = transcript_file
transcripts = {}
with tf.io.gfile.GFile(os.path.join(path, transcript_file)) as f:
for line in f:
line = line.strip()
key, transcript = line.split(" ", 1)
transcripts[key] = transcript
audio_files = [f for f in files if not f.endswith(".txt")]
for audio_file in audio_files:
assert audio_file.endswith(".flac")
key = audio_file[:-len(".flac")]
transcript = transcripts[key]
speaker_id, chapter_id = [int(el) for el in key.split("-")[:2]]
yield LibrispeechExample(
speaker_id=speaker_id,
chapter_id=chapter_id,
audio_file=os.path.join(path, audio_file),
transcript=transcript) | def _walk_librispeech_dir(directory):
"""Walk a Librispeech directory and yield examples."""
directory = os.path.join(directory, "LibriSpeech")
for path, _, files in tf.io.gfile.walk(directory):
if not files:
continue
transcript_file = [f for f in files if f.endswith(".txt")]
if not transcript_file:
continue
assert len(transcript_file) == 1
transcript_file, = transcript_file
transcripts = {}
with tf.io.gfile.GFile(os.path.join(path, transcript_file)) as f:
for line in f:
line = line.strip()
key, transcript = line.split(" ", 1)
transcripts[key] = transcript
audio_files = [f for f in files if not f.endswith(".txt")]
for audio_file in audio_files:
assert audio_file.endswith(".flac")
key = audio_file[:-len(".flac")]
transcript = transcripts[key]
speaker_id, chapter_id = [int(el) for el in key.split("-")[:2]]
yield LibrispeechExample(
speaker_id=speaker_id,
chapter_id=chapter_id,
audio_file=os.path.join(path, audio_file),
transcript=transcript) | [
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train | LibrispeechConfig.download_urls | Returns download urls for this config. | tensorflow_datasets/audio/librispeech.py | def download_urls(self):
"""Returns download urls for this config."""
urls = {
tfds.Split.TRAIN: ["train_clean100"],
tfds.Split.VALIDATION: ["dev_clean"],
tfds.Split.TEST: ["test_clean"],
}
if self.data in ["all", "clean360"]:
urls[tfds.Split.TRAIN].append("train_clean360")
if self.data == "all":
urls[tfds.Split.TRAIN].extend(["train_clean360", "train_other500"])
urls[tfds.Split.VALIDATION].append("dev_other")
urls[tfds.Split.TEST].append("test_other")
urls = {
split: [_DL_URLS[name] for name in names
] for split, names in urls.items()
}
return urls | def download_urls(self):
"""Returns download urls for this config."""
urls = {
tfds.Split.TRAIN: ["train_clean100"],
tfds.Split.VALIDATION: ["dev_clean"],
tfds.Split.TEST: ["test_clean"],
}
if self.data in ["all", "clean360"]:
urls[tfds.Split.TRAIN].append("train_clean360")
if self.data == "all":
urls[tfds.Split.TRAIN].extend(["train_clean360", "train_other500"])
urls[tfds.Split.VALIDATION].append("dev_other")
urls[tfds.Split.TEST].append("test_other")
urls = {
split: [_DL_URLS[name] for name in names
] for split, names in urls.items()
}
return urls | [
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train | ClassLabel.str2int | Conversion class name string => integer. | tensorflow_datasets/core/features/class_label_feature.py | def str2int(self, str_value):
"""Conversion class name string => integer."""
str_value = tf.compat.as_text(str_value)
if self._str2int:
return self._str2int[str_value]
# No names provided, try to integerize
failed_parse = False
try:
int_value = int(str_value)
except ValueError:
failed_parse = True
if failed_parse or not 0 <= int_value < self._num_classes:
raise ValueError("Invalid string class label %s" % str_value)
return int_value | def str2int(self, str_value):
"""Conversion class name string => integer."""
str_value = tf.compat.as_text(str_value)
if self._str2int:
return self._str2int[str_value]
# No names provided, try to integerize
failed_parse = False
try:
int_value = int(str_value)
except ValueError:
failed_parse = True
if failed_parse or not 0 <= int_value < self._num_classes:
raise ValueError("Invalid string class label %s" % str_value)
return int_value | [
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train | ClassLabel.int2str | Conversion integer => class name string. | tensorflow_datasets/core/features/class_label_feature.py | def int2str(self, int_value):
"""Conversion integer => class name string."""
if self._int2str:
# Maybe should support batched np array/eager tensors, to allow things
# like
# out_ids = model(inputs)
# labels = cifar10.info.features['label'].int2str(out_ids)
return self._int2str[int_value]
# No names provided, return str(int)
if not 0 <= int_value < self._num_classes:
raise ValueError("Invalid integer class label %d" % int_value)
return tf.compat.as_text(str(int_value)) | def int2str(self, int_value):
"""Conversion integer => class name string."""
if self._int2str:
# Maybe should support batched np array/eager tensors, to allow things
# like
# out_ids = model(inputs)
# labels = cifar10.info.features['label'].int2str(out_ids)
return self._int2str[int_value]
# No names provided, return str(int)
if not 0 <= int_value < self._num_classes:
raise ValueError("Invalid integer class label %d" % int_value)
return tf.compat.as_text(str(int_value)) | [
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train | ClassLabel.save_metadata | See base class for details. | tensorflow_datasets/core/features/class_label_feature.py | def save_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Save names if defined
if self._str2int is not None:
names_filepath = _get_names_filepath(data_dir, feature_name)
_write_names_to_file(names_filepath, self.names) | def save_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Save names if defined
if self._str2int is not None:
names_filepath = _get_names_filepath(data_dir, feature_name)
_write_names_to_file(names_filepath, self.names) | [
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train | ClassLabel.load_metadata | See base class for details. | tensorflow_datasets/core/features/class_label_feature.py | def load_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Restore names if defined
names_filepath = _get_names_filepath(data_dir, feature_name)
if tf.io.gfile.exists(names_filepath):
self.names = _load_names_from_file(names_filepath) | def load_metadata(self, data_dir, feature_name=None):
"""See base class for details."""
# Restore names if defined
names_filepath = _get_names_filepath(data_dir, feature_name)
if tf.io.gfile.exists(names_filepath):
self.names = _load_names_from_file(names_filepath) | [
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train | _token_counts_from_generator | Builds token counts from generator. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _token_counts_from_generator(generator, max_chars, reserved_tokens):
"""Builds token counts from generator."""
reserved_tokens = list(reserved_tokens) + [_UNDERSCORE_REPLACEMENT]
tokenizer = text_encoder.Tokenizer(
alphanum_only=False, reserved_tokens=reserved_tokens)
num_chars = 0
token_counts = collections.defaultdict(int)
for s in generator:
s = tf.compat.as_text(s)
if max_chars and (num_chars + len(s)) >= max_chars:
s = s[:(max_chars - num_chars)]
tokens = tokenizer.tokenize(s)
tokens = _prepare_tokens_for_encode(tokens)
for t in tokens:
token_counts[t] += 1
if max_chars:
num_chars += len(s)
if num_chars > max_chars:
break
return token_counts | def _token_counts_from_generator(generator, max_chars, reserved_tokens):
"""Builds token counts from generator."""
reserved_tokens = list(reserved_tokens) + [_UNDERSCORE_REPLACEMENT]
tokenizer = text_encoder.Tokenizer(
alphanum_only=False, reserved_tokens=reserved_tokens)
num_chars = 0
token_counts = collections.defaultdict(int)
for s in generator:
s = tf.compat.as_text(s)
if max_chars and (num_chars + len(s)) >= max_chars:
s = s[:(max_chars - num_chars)]
tokens = tokenizer.tokenize(s)
tokens = _prepare_tokens_for_encode(tokens)
for t in tokens:
token_counts[t] += 1
if max_chars:
num_chars += len(s)
if num_chars > max_chars:
break
return token_counts | [
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train | _validate_build_arguments | Validate arguments for SubwordTextEncoder.build_from_corpus. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _validate_build_arguments(max_subword_length, reserved_tokens,
target_vocab_size):
"""Validate arguments for SubwordTextEncoder.build_from_corpus."""
if max_subword_length <= 0:
raise ValueError(
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for t in reserved_tokens:
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raise ValueError(
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# Minimum vocab size = bytes + pad + 1
minimum_vocab_size = text_encoder.NUM_BYTES + 1 + 1
if target_vocab_size < minimum_vocab_size:
raise ValueError("target_vocab_size must be >= %d. Got %d" %
(minimum_vocab_size, target_vocab_size)) | def _validate_build_arguments(max_subword_length, reserved_tokens,
target_vocab_size):
"""Validate arguments for SubwordTextEncoder.build_from_corpus."""
if max_subword_length <= 0:
raise ValueError(
"max_subword_length must be > 0. Note that memory and compute for "
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for t in reserved_tokens:
if t.endswith("_") or not text_encoder.is_mixed_alphanum(t):
raise ValueError(
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"of alphanumeric and non-alphanumeric characters. For example, "
"'<EOS>'.")
# Minimum vocab size = bytes + pad + 1
minimum_vocab_size = text_encoder.NUM_BYTES + 1 + 1
if target_vocab_size < minimum_vocab_size:
raise ValueError("target_vocab_size must be >= %d. Got %d" %
(minimum_vocab_size, target_vocab_size)) | [
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train | _prepare_tokens_for_encode | Prepare tokens for encoding.
Tokens followed by a single space have "_" appended and the single space token
is dropped.
If a token is _UNDERSCORE_REPLACEMENT, it is broken up into 2 tokens.
Args:
tokens: `list<str>`, tokens to prepare.
Returns:
`list<str>` prepared tokens. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _prepare_tokens_for_encode(tokens):
"""Prepare tokens for encoding.
Tokens followed by a single space have "_" appended and the single space token
is dropped.
If a token is _UNDERSCORE_REPLACEMENT, it is broken up into 2 tokens.
Args:
tokens: `list<str>`, tokens to prepare.
Returns:
`list<str>` prepared tokens.
"""
prepared_tokens = []
def _prepare_token(t, next_t):
skip_next = False
t = _escape(t)
# If next token is a single space, add _ suffix to token and skip the
# empty space.
if next_t == " ":
t += "_"
skip_next = True
return t, skip_next
next_tokens = tokens[1:] + [None]
skip_single_token = False
for token, next_token in zip(tokens, next_tokens):
if skip_single_token:
skip_single_token = False
continue
# If the user-supplied string contains the underscore replacement string,
# break it into 2 tokens and encode those separately.
if token == _UNDERSCORE_REPLACEMENT:
t1, t2 = _UNDERSCORE_REPLACEMENT[:2], _UNDERSCORE_REPLACEMENT[2:]
t1, _ = _prepare_token(t1, None)
t2, _ = _prepare_token(t2, next_token)
prepared_tokens.append(t1)
prepared_tokens.append(t2)
continue
token, skip_single_token = _prepare_token(token, next_token)
prepared_tokens.append(token)
return prepared_tokens | def _prepare_tokens_for_encode(tokens):
"""Prepare tokens for encoding.
Tokens followed by a single space have "_" appended and the single space token
is dropped.
If a token is _UNDERSCORE_REPLACEMENT, it is broken up into 2 tokens.
Args:
tokens: `list<str>`, tokens to prepare.
Returns:
`list<str>` prepared tokens.
"""
prepared_tokens = []
def _prepare_token(t, next_t):
skip_next = False
t = _escape(t)
# If next token is a single space, add _ suffix to token and skip the
# empty space.
if next_t == " ":
t += "_"
skip_next = True
return t, skip_next
next_tokens = tokens[1:] + [None]
skip_single_token = False
for token, next_token in zip(tokens, next_tokens):
if skip_single_token:
skip_single_token = False
continue
# If the user-supplied string contains the underscore replacement string,
# break it into 2 tokens and encode those separately.
if token == _UNDERSCORE_REPLACEMENT:
t1, t2 = _UNDERSCORE_REPLACEMENT[:2], _UNDERSCORE_REPLACEMENT[2:]
t1, _ = _prepare_token(t1, None)
t2, _ = _prepare_token(t2, next_token)
prepared_tokens.append(t1)
prepared_tokens.append(t2)
continue
token, skip_single_token = _prepare_token(token, next_token)
prepared_tokens.append(token)
return prepared_tokens | [
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train | SubwordTextEncoder.encode | Encodes text into a list of integers. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def encode(self, s):
"""Encodes text into a list of integers."""
s = tf.compat.as_text(s)
tokens = self._tokenizer.tokenize(s)
tokens = _prepare_tokens_for_encode(tokens)
ids = []
for token in tokens:
ids.extend(self._token_to_ids(token))
return text_encoder.pad_incr(ids) | def encode(self, s):
"""Encodes text into a list of integers."""
s = tf.compat.as_text(s)
tokens = self._tokenizer.tokenize(s)
tokens = _prepare_tokens_for_encode(tokens)
ids = []
for token in tokens:
ids.extend(self._token_to_ids(token))
return text_encoder.pad_incr(ids) | [
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train | SubwordTextEncoder.decode | Decodes a list of integers into text. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def decode(self, ids):
"""Decodes a list of integers into text."""
ids = text_encoder.pad_decr(ids)
subword_ids = ids
del ids
subwords = []
# Some ids correspond to bytes. Because unicode characters are composed of
# possibly multiple bytes, we attempt to decode contiguous lists of bytes
# all together. Invalid byte sequences are replaced with the unicode
# replacement (i.e. unknown) character U+FFFD.
prev_bytes = []
def consume_prev_bytes():
if prev_bytes:
bytestr = b"".join(prev_bytes)
bytes_text = bytestr.decode("utf-8", "replace")
subwords.append(bytes_text)
return []
for subword_id in subword_ids:
subword = self._id_to_subword(subword_id)
if isinstance(subword, six.binary_type):
# Byte-encoded
prev_bytes.append(subword)
else:
# If there were bytes previously, convert to unicode.
prev_bytes = consume_prev_bytes()
trimmed, add_space = _trim_underscore_and_tell(subword)
subwords.append(trimmed)
if add_space:
subwords.append(" ")
# If there were trailing bytes, convert to unicode.
prev_bytes = consume_prev_bytes()
return tf.compat.as_text("".join(subwords)) | def decode(self, ids):
"""Decodes a list of integers into text."""
ids = text_encoder.pad_decr(ids)
subword_ids = ids
del ids
subwords = []
# Some ids correspond to bytes. Because unicode characters are composed of
# possibly multiple bytes, we attempt to decode contiguous lists of bytes
# all together. Invalid byte sequences are replaced with the unicode
# replacement (i.e. unknown) character U+FFFD.
prev_bytes = []
def consume_prev_bytes():
if prev_bytes:
bytestr = b"".join(prev_bytes)
bytes_text = bytestr.decode("utf-8", "replace")
subwords.append(bytes_text)
return []
for subword_id in subword_ids:
subword = self._id_to_subword(subword_id)
if isinstance(subword, six.binary_type):
# Byte-encoded
prev_bytes.append(subword)
else:
# If there were bytes previously, convert to unicode.
prev_bytes = consume_prev_bytes()
trimmed, add_space = _trim_underscore_and_tell(subword)
subwords.append(trimmed)
if add_space:
subwords.append(" ")
# If there were trailing bytes, convert to unicode.
prev_bytes = consume_prev_bytes()
return tf.compat.as_text("".join(subwords)) | [
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train | SubwordTextEncoder._token_to_ids | Convert a single token to a list of integer ids. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _token_to_ids(self, token):
"""Convert a single token to a list of integer ids."""
# Check cache
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._token_to_ids_cache[cache_location]
if cache_key == token:
return cache_value
subwords = self._token_to_subwords(token)
ids = []
for subword in subwords:
if subword == _UNDERSCORE_REPLACEMENT:
ids.append(len(self._subwords) + ord("_"))
continue
subword_id = self._subword_to_id.get(subword)
if subword_id is None:
# Byte-encode
ids.extend(self._byte_encode(subword))
else:
ids.append(subword_id)
# Update cache
self._token_to_ids_cache[cache_location] = (token, ids)
return ids | def _token_to_ids(self, token):
"""Convert a single token to a list of integer ids."""
# Check cache
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._token_to_ids_cache[cache_location]
if cache_key == token:
return cache_value
subwords = self._token_to_subwords(token)
ids = []
for subword in subwords:
if subword == _UNDERSCORE_REPLACEMENT:
ids.append(len(self._subwords) + ord("_"))
continue
subword_id = self._subword_to_id.get(subword)
if subword_id is None:
# Byte-encode
ids.extend(self._byte_encode(subword))
else:
ids.append(subword_id)
# Update cache
self._token_to_ids_cache[cache_location] = (token, ids)
return ids | [
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train | SubwordTextEncoder._byte_encode | Encode a single token byte-wise into integer ids. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _byte_encode(self, token):
"""Encode a single token byte-wise into integer ids."""
# Vocab ids for all bytes follow ids for the subwords
offset = len(self._subwords)
if token == "_":
return [len(self._subwords) + ord(" ")]
return [i + offset for i in list(bytearray(tf.compat.as_bytes(token)))] | def _byte_encode(self, token):
"""Encode a single token byte-wise into integer ids."""
# Vocab ids for all bytes follow ids for the subwords
offset = len(self._subwords)
if token == "_":
return [len(self._subwords) + ord(" ")]
return [i + offset for i in list(bytearray(tf.compat.as_bytes(token)))] | [
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train | SubwordTextEncoder._id_to_subword | Converts a subword integer ID to a subword string. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _id_to_subword(self, subword_id):
"""Converts a subword integer ID to a subword string."""
if subword_id < 0 or subword_id >= (self.vocab_size - 1):
raise ValueError("Received id %d which is invalid. Ids must be within "
"[0, %d)." % (subword_id + 1, self.vocab_size))
if 0 <= subword_id < len(self._subwords):
# Subword
return self._subwords[subword_id]
else:
# Byte
offset = len(self._subwords)
subword_id -= offset
bytestr = bytes(bytearray([subword_id]))
return bytestr | def _id_to_subword(self, subword_id):
"""Converts a subword integer ID to a subword string."""
if subword_id < 0 or subword_id >= (self.vocab_size - 1):
raise ValueError("Received id %d which is invalid. Ids must be within "
"[0, %d)." % (subword_id + 1, self.vocab_size))
if 0 <= subword_id < len(self._subwords):
# Subword
return self._subwords[subword_id]
else:
# Byte
offset = len(self._subwords)
subword_id -= offset
bytestr = bytes(bytearray([subword_id]))
return bytestr | [
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train | SubwordTextEncoder._token_to_subwords | Greedily split token into subwords. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _token_to_subwords(self, token):
"""Greedily split token into subwords."""
subwords = []
start = 0
while start < len(token):
subword = None
for end in range(
min(len(token), start + self._max_subword_len), start, -1):
candidate = token[start:end]
if (candidate in self._subword_to_id or
candidate == _UNDERSCORE_REPLACEMENT):
subword = candidate
subwords.append(subword)
start = end
break
# No subword match found. Consume a single (unicode) character.
if subword is None:
subwords.append(token[start])
start += 1
return subwords | def _token_to_subwords(self, token):
"""Greedily split token into subwords."""
subwords = []
start = 0
while start < len(token):
subword = None
for end in range(
min(len(token), start + self._max_subword_len), start, -1):
candidate = token[start:end]
if (candidate in self._subword_to_id or
candidate == _UNDERSCORE_REPLACEMENT):
subword = candidate
subwords.append(subword)
start = end
break
# No subword match found. Consume a single (unicode) character.
if subword is None:
subwords.append(token[start])
start += 1
return subwords | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/text/subword_text_encoder.py#L190-L211 | [
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train | SubwordTextEncoder._init_from_list | Initializes the encoder from a list of subwords. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def _init_from_list(self, subwords):
"""Initializes the encoder from a list of subwords."""
subwords = [tf.compat.as_text(s) for s in subwords if s]
self._subwords = subwords
# Note that internally everything is 0-indexed. Padding is dealt with at the
# end of encode and the beginning of decode.
self._subword_to_id = {s: i for i, s in enumerate(subwords)}
# We remember the maximum length of any subword to avoid having to
# check arbitrarily long strings.
self._max_subword_len = max(
len(_UNDERSCORE_REPLACEMENT), max([len(s) for s in subwords] or [1]))
# Initialize the cache
self._cache_size = 2**20
self._token_to_ids_cache = [(None, None)] * self._cache_size
# Setup tokenizer
# Reserved tokens are all tokens that are mixed alphanum and non-alphanum.
reserved_tokens = set([_UNDERSCORE_REPLACEMENT])
for t in self._subwords:
if text_encoder.is_mixed_alphanum(t):
reserved_tokens.add(t)
self._tokenizer = text_encoder.Tokenizer(
alphanum_only=False, reserved_tokens=reserved_tokens) | def _init_from_list(self, subwords):
"""Initializes the encoder from a list of subwords."""
subwords = [tf.compat.as_text(s) for s in subwords if s]
self._subwords = subwords
# Note that internally everything is 0-indexed. Padding is dealt with at the
# end of encode and the beginning of decode.
self._subword_to_id = {s: i for i, s in enumerate(subwords)}
# We remember the maximum length of any subword to avoid having to
# check arbitrarily long strings.
self._max_subword_len = max(
len(_UNDERSCORE_REPLACEMENT), max([len(s) for s in subwords] or [1]))
# Initialize the cache
self._cache_size = 2**20
self._token_to_ids_cache = [(None, None)] * self._cache_size
# Setup tokenizer
# Reserved tokens are all tokens that are mixed alphanum and non-alphanum.
reserved_tokens = set([_UNDERSCORE_REPLACEMENT])
for t in self._subwords:
if text_encoder.is_mixed_alphanum(t):
reserved_tokens.add(t)
self._tokenizer = text_encoder.Tokenizer(
alphanum_only=False, reserved_tokens=reserved_tokens) | [
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train | SubwordTextEncoder.save_to_file | Save the vocabulary to a file. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def save_to_file(self, filename_prefix):
"""Save the vocabulary to a file."""
# Wrap in single quotes to make it easier to see the full subword when
# it has spaces and make it easier to search with ctrl+f.
filename = self._filename(filename_prefix)
lines = ["'%s'" % s for s in self._subwords]
self._write_lines_to_file(filename, lines) | def save_to_file(self, filename_prefix):
"""Save the vocabulary to a file."""
# Wrap in single quotes to make it easier to see the full subword when
# it has spaces and make it easier to search with ctrl+f.
filename = self._filename(filename_prefix)
lines = ["'%s'" % s for s in self._subwords]
self._write_lines_to_file(filename, lines) | [
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train | SubwordTextEncoder.load_from_file | Extracts list of subwords from file. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def load_from_file(cls, filename_prefix):
"""Extracts list of subwords from file."""
filename = cls._filename(filename_prefix)
lines, _ = cls._read_lines_from_file(filename)
# Strip wrapping single quotes
vocab_list = [line[1:-1] for line in lines]
return cls(vocab_list=vocab_list) | def load_from_file(cls, filename_prefix):
"""Extracts list of subwords from file."""
filename = cls._filename(filename_prefix)
lines, _ = cls._read_lines_from_file(filename)
# Strip wrapping single quotes
vocab_list = [line[1:-1] for line in lines]
return cls(vocab_list=vocab_list) | [
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train | SubwordTextEncoder.build_from_corpus | Builds a `SubwordTextEncoder` based on the `corpus_generator`.
Args:
corpus_generator: generator yielding `str`, from which subwords will be
constructed.
target_vocab_size: `int`, approximate size of the vocabulary to create.
max_subword_length: `int`, maximum length of a subword. Note that memory
and compute scale quadratically in the length of the longest token.
max_corpus_chars: `int`, the maximum number of characters to consume from
`corpus_generator` for the purposes of building the subword vocabulary.
reserved_tokens: `list<str>`, list of tokens that will always be treated
as whole tokens and not split up. Note that these must contain a mix of
alphanumeric and non-alphanumeric characters (e.g. "<EOS>") and not end
in an underscore.
Returns:
`SubwordTextEncoder`. | tensorflow_datasets/core/features/text/subword_text_encoder.py | def build_from_corpus(cls,
corpus_generator,
target_vocab_size,
max_subword_length=20,
max_corpus_chars=None,
reserved_tokens=None):
"""Builds a `SubwordTextEncoder` based on the `corpus_generator`.
Args:
corpus_generator: generator yielding `str`, from which subwords will be
constructed.
target_vocab_size: `int`, approximate size of the vocabulary to create.
max_subword_length: `int`, maximum length of a subword. Note that memory
and compute scale quadratically in the length of the longest token.
max_corpus_chars: `int`, the maximum number of characters to consume from
`corpus_generator` for the purposes of building the subword vocabulary.
reserved_tokens: `list<str>`, list of tokens that will always be treated
as whole tokens and not split up. Note that these must contain a mix of
alphanumeric and non-alphanumeric characters (e.g. "<EOS>") and not end
in an underscore.
Returns:
`SubwordTextEncoder`.
"""
reserved_tokens = reserved_tokens or []
_validate_build_arguments(
max_subword_length=max_subword_length,
reserved_tokens=reserved_tokens,
target_vocab_size=target_vocab_size)
token_counts = _token_counts_from_generator(
generator=corpus_generator,
max_chars=max_corpus_chars,
reserved_tokens=reserved_tokens)
# Binary search on the minimum token count to build a vocabulary with
# approximately the right size
def _binary_search(min_token_count, max_token_count):
"""Binary search min_token_count to build SubwordTextEncoder vocab."""
candidate_min = (min_token_count + max_token_count) // 2
logging.info("SubwordTextEncoder build: trying min_token_count %d",
candidate_min)
encoder = cls._build_from_token_counts(
token_counts=token_counts,
min_token_count=candidate_min,
reserved_tokens=reserved_tokens,
num_iterations=4,
max_subword_length=max_subword_length)
vocab_size = encoder.vocab_size
# Being within 1% of the target vocab size is ok
target_achieved = (
abs(vocab_size - target_vocab_size) * 100 < target_vocab_size)
if (target_achieved or min_token_count >= max_token_count or
candidate_min <= 1):
# Search complete
return encoder
# Recurse
if vocab_size > target_vocab_size:
next_encoder = _binary_search(candidate_min + 1, max_token_count)
else:
next_encoder = _binary_search(min_token_count, candidate_min - 1)
# Return the one that's closest to the target_vocab_size
if (abs(vocab_size - target_vocab_size) <
abs(next_encoder.vocab_size - target_vocab_size)):
return encoder
else:
return next_encoder
# Get min and max token counts.
min_token_count = max(min(token_counts.values()), 1)
max_token_count = max(token_counts.values())
# Another option could be to do a binary search over *ranks* of the tokens.
return _binary_search(min_token_count, max_token_count) | def build_from_corpus(cls,
corpus_generator,
target_vocab_size,
max_subword_length=20,
max_corpus_chars=None,
reserved_tokens=None):
"""Builds a `SubwordTextEncoder` based on the `corpus_generator`.
Args:
corpus_generator: generator yielding `str`, from which subwords will be
constructed.
target_vocab_size: `int`, approximate size of the vocabulary to create.
max_subword_length: `int`, maximum length of a subword. Note that memory
and compute scale quadratically in the length of the longest token.
max_corpus_chars: `int`, the maximum number of characters to consume from
`corpus_generator` for the purposes of building the subword vocabulary.
reserved_tokens: `list<str>`, list of tokens that will always be treated
as whole tokens and not split up. Note that these must contain a mix of
alphanumeric and non-alphanumeric characters (e.g. "<EOS>") and not end
in an underscore.
Returns:
`SubwordTextEncoder`.
"""
reserved_tokens = reserved_tokens or []
_validate_build_arguments(
max_subword_length=max_subword_length,
reserved_tokens=reserved_tokens,
target_vocab_size=target_vocab_size)
token_counts = _token_counts_from_generator(
generator=corpus_generator,
max_chars=max_corpus_chars,
reserved_tokens=reserved_tokens)
# Binary search on the minimum token count to build a vocabulary with
# approximately the right size
def _binary_search(min_token_count, max_token_count):
"""Binary search min_token_count to build SubwordTextEncoder vocab."""
candidate_min = (min_token_count + max_token_count) // 2
logging.info("SubwordTextEncoder build: trying min_token_count %d",
candidate_min)
encoder = cls._build_from_token_counts(
token_counts=token_counts,
min_token_count=candidate_min,
reserved_tokens=reserved_tokens,
num_iterations=4,
max_subword_length=max_subword_length)
vocab_size = encoder.vocab_size
# Being within 1% of the target vocab size is ok
target_achieved = (
abs(vocab_size - target_vocab_size) * 100 < target_vocab_size)
if (target_achieved or min_token_count >= max_token_count or
candidate_min <= 1):
# Search complete
return encoder
# Recurse
if vocab_size > target_vocab_size:
next_encoder = _binary_search(candidate_min + 1, max_token_count)
else:
next_encoder = _binary_search(min_token_count, candidate_min - 1)
# Return the one that's closest to the target_vocab_size
if (abs(vocab_size - target_vocab_size) <
abs(next_encoder.vocab_size - target_vocab_size)):
return encoder
else:
return next_encoder
# Get min and max token counts.
min_token_count = max(min(token_counts.values()), 1)
max_token_count = max(token_counts.values())
# Another option could be to do a binary search over *ranks* of the tokens.
return _binary_search(min_token_count, max_token_count) | [
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train | Higgs._generate_examples | Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per row. | tensorflow_datasets/structured/higgs.py | def _generate_examples(self, file_path):
"""Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per row.
"""
fieldnames = [
'class_label', 'lepton_pT', 'lepton_eta', 'lepton_phi',
'missing_energy_magnitude', 'missing_energy_phi', 'jet_1_pt',
'jet_1_eta', 'jet_1_phi', 'jet_1_b-tag', 'jet_2_pt', 'jet_2_eta',
'jet_2_phi', 'jet_2_b-tag', 'jet_3_pt', 'jet_3_eta', 'jet_3_phi',
'jet_3_b-tag', 'jet_4_pt', 'jet_4_eta', 'jet_4_phi', 'jet_4_b-tag',
'm_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'
]
with tf.io.gfile.GFile(file_path) as csvfile:
reader = csv.DictReader(csvfile, fieldnames=fieldnames)
for row in reader:
yield row | def _generate_examples(self, file_path):
"""Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per row.
"""
fieldnames = [
'class_label', 'lepton_pT', 'lepton_eta', 'lepton_phi',
'missing_energy_magnitude', 'missing_energy_phi', 'jet_1_pt',
'jet_1_eta', 'jet_1_phi', 'jet_1_b-tag', 'jet_2_pt', 'jet_2_eta',
'jet_2_phi', 'jet_2_b-tag', 'jet_3_pt', 'jet_3_eta', 'jet_3_phi',
'jet_3_b-tag', 'jet_4_pt', 'jet_4_eta', 'jet_4_phi', 'jet_4_b-tag',
'm_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'
]
with tf.io.gfile.GFile(file_path) as csvfile:
reader = csv.DictReader(csvfile, fieldnames=fieldnames)
for row in reader:
yield row | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/structured/higgs.py#L122-L144 | [
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train | CatsVsDogs._generate_examples | Generate Cats vs Dogs images and labels given a directory path. | tensorflow_datasets/image/cats_vs_dogs.py | def _generate_examples(self, archive):
"""Generate Cats vs Dogs images and labels given a directory path."""
num_skipped = 0
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # README file, ...
continue
label = res.group(1).lower()
if tf.compat.as_bytes("JFIF") not in fobj.peek(10):
num_skipped += 1
continue
yield {
"image": fobj,
"image/filename": fname,
"label": label,
}
if num_skipped != _NUM_CORRUPT_IMAGES:
raise ValueError("Expected %d corrupt images, but found %d" % (
_NUM_CORRUPT_IMAGES, num_skipped))
logging.warning("%d images were corrupted and were skipped", num_skipped) | def _generate_examples(self, archive):
"""Generate Cats vs Dogs images and labels given a directory path."""
num_skipped = 0
for fname, fobj in archive:
res = _NAME_RE.match(fname)
if not res: # README file, ...
continue
label = res.group(1).lower()
if tf.compat.as_bytes("JFIF") not in fobj.peek(10):
num_skipped += 1
continue
yield {
"image": fobj,
"image/filename": fname,
"label": label,
}
if num_skipped != _NUM_CORRUPT_IMAGES:
raise ValueError("Expected %d corrupt images, but found %d" % (
_NUM_CORRUPT_IMAGES, num_skipped))
logging.warning("%d images were corrupted and were skipped", num_skipped) | [
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"conti... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _load_chunk | Loads a data chunk as specified by the paths.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Returns:
Tuple with the dat, cat, info_arrays. | tensorflow_datasets/image/smallnorb.py | def _load_chunk(dat_path, cat_path, info_path):
"""Loads a data chunk as specified by the paths.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Returns:
Tuple with the dat, cat, info_arrays.
"""
dat_array = read_binary_matrix(dat_path)
# Even if the image is gray scale, we need to add an extra channel dimension
# to be compatible with tfds.features.Image.
dat_array = np.expand_dims(dat_array, -1)
cat_array = read_binary_matrix(cat_path)
info_array = read_binary_matrix(info_path)
info_array = np.copy(info_array) # Make read-only buffer array writable.
# Azimuth values are 0, 2, 4, .., 34. We divide by 2 to get proper labels.
info_array[:, 2] = info_array[:, 2] / 2
return dat_array, cat_array, info_array | def _load_chunk(dat_path, cat_path, info_path):
"""Loads a data chunk as specified by the paths.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Returns:
Tuple with the dat, cat, info_arrays.
"""
dat_array = read_binary_matrix(dat_path)
# Even if the image is gray scale, we need to add an extra channel dimension
# to be compatible with tfds.features.Image.
dat_array = np.expand_dims(dat_array, -1)
cat_array = read_binary_matrix(cat_path)
info_array = read_binary_matrix(info_path)
info_array = np.copy(info_array) # Make read-only buffer array writable.
# Azimuth values are 0, 2, 4, .., 34. We divide by 2 to get proper labels.
info_array[:, 2] = info_array[:, 2] / 2
return dat_array, cat_array, info_array | [
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train | read_binary_matrix | Reads and returns binary formatted matrix stored in filename.
The file format is described on the data set page:
https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
Args:
filename: String with path to the file.
Returns:
Numpy array contained in the file. | tensorflow_datasets/image/smallnorb.py | def read_binary_matrix(filename):
"""Reads and returns binary formatted matrix stored in filename.
The file format is described on the data set page:
https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
Args:
filename: String with path to the file.
Returns:
Numpy array contained in the file.
"""
with tf.io.gfile.GFile(filename, "rb") as f:
s = f.read()
# Data is stored in little-endian byte order.
int32_dtype = np.dtype("int32").newbyteorder("<")
# The first 4 bytes contain a magic code that specifies the data type.
magic = int(np.frombuffer(s, dtype=int32_dtype, count=1))
if magic == 507333717:
data_dtype = np.dtype("uint8") # uint8 does not have a byte order.
elif magic == 507333716:
data_dtype = np.dtype("int32").newbyteorder("<")
else:
raise ValueError("Invalid magic value for data type!")
# The second 4 bytes contain an int32 with the number of dimensions of the
# stored array.
ndim = int(np.frombuffer(s, dtype=int32_dtype, count=1, offset=4))
# The next ndim x 4 bytes contain the shape of the array in int32.
dims = np.frombuffer(s, dtype=int32_dtype, count=ndim, offset=8)
# If the array has less than three dimensions, three int32 are still used to
# save the shape info (remaining int32 are simply set to 1). The shape info
# hence uses max(3, ndim) bytes.
bytes_used_for_shape_info = max(3, ndim) * 4
# The remaining bytes are the array.
data = np.frombuffer(
s, dtype=data_dtype, offset=8 + bytes_used_for_shape_info)
return data.reshape(tuple(dims)) | def read_binary_matrix(filename):
"""Reads and returns binary formatted matrix stored in filename.
The file format is described on the data set page:
https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
Args:
filename: String with path to the file.
Returns:
Numpy array contained in the file.
"""
with tf.io.gfile.GFile(filename, "rb") as f:
s = f.read()
# Data is stored in little-endian byte order.
int32_dtype = np.dtype("int32").newbyteorder("<")
# The first 4 bytes contain a magic code that specifies the data type.
magic = int(np.frombuffer(s, dtype=int32_dtype, count=1))
if magic == 507333717:
data_dtype = np.dtype("uint8") # uint8 does not have a byte order.
elif magic == 507333716:
data_dtype = np.dtype("int32").newbyteorder("<")
else:
raise ValueError("Invalid magic value for data type!")
# The second 4 bytes contain an int32 with the number of dimensions of the
# stored array.
ndim = int(np.frombuffer(s, dtype=int32_dtype, count=1, offset=4))
# The next ndim x 4 bytes contain the shape of the array in int32.
dims = np.frombuffer(s, dtype=int32_dtype, count=ndim, offset=8)
# If the array has less than three dimensions, three int32 are still used to
# save the shape info (remaining int32 are simply set to 1). The shape info
# hence uses max(3, ndim) bytes.
bytes_used_for_shape_info = max(3, ndim) * 4
# The remaining bytes are the array.
data = np.frombuffer(
s, dtype=data_dtype, offset=8 + bytes_used_for_shape_info)
return data.reshape(tuple(dims)) | [
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train | Smallnorb._split_generators | Returns splits. | tensorflow_datasets/image/smallnorb.py | def _split_generators(self, dl_manager):
"""Returns splits."""
filenames = {
"training_dat": _TRAINING_URL_TEMPLATE.format(type="dat"),
"training_cat": _TRAINING_URL_TEMPLATE.format(type="cat"),
"training_info": _TRAINING_URL_TEMPLATE.format(type="info"),
"testing_dat": _TESTING_URL_TEMPLATE.format(type="dat"),
"testing_cat": _TESTING_URL_TEMPLATE.format(type="cat"),
"testing_info": _TESTING_URL_TEMPLATE.format(type="info"),
}
files = dl_manager.download_and_extract(filenames)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=1,
gen_kwargs=dict(
dat_path=files["training_dat"],
cat_path=files["training_cat"],
info_path=files["training_info"])),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(
dat_path=files["testing_dat"],
cat_path=files["testing_cat"],
info_path=files["testing_info"])),
] | def _split_generators(self, dl_manager):
"""Returns splits."""
filenames = {
"training_dat": _TRAINING_URL_TEMPLATE.format(type="dat"),
"training_cat": _TRAINING_URL_TEMPLATE.format(type="cat"),
"training_info": _TRAINING_URL_TEMPLATE.format(type="info"),
"testing_dat": _TESTING_URL_TEMPLATE.format(type="dat"),
"testing_cat": _TESTING_URL_TEMPLATE.format(type="cat"),
"testing_info": _TESTING_URL_TEMPLATE.format(type="info"),
}
files = dl_manager.download_and_extract(filenames)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=1,
gen_kwargs=dict(
dat_path=files["training_dat"],
cat_path=files["training_cat"],
info_path=files["training_info"])),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs=dict(
dat_path=files["testing_dat"],
cat_path=files["testing_cat"],
info_path=files["testing_info"])),
] | [
"Returns",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/smallnorb.py#L86-L114 | [
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train | Smallnorb._generate_examples | Generate examples for the Smallnorb dataset.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Yields:
Dictionaries with images and the different labels. | tensorflow_datasets/image/smallnorb.py | def _generate_examples(self, dat_path, cat_path, info_path):
"""Generate examples for the Smallnorb dataset.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Yields:
Dictionaries with images and the different labels.
"""
dat_arr, cat_arr, info_arr = _load_chunk(dat_path, cat_path, info_path)
for image, category, info_vec in moves.zip(dat_arr, cat_arr, info_arr):
yield {
"image": image[0],
"image2": image[1],
"label_category": category,
"instance": info_vec[0],
"label_elevation": info_vec[1],
"label_azimuth": info_vec[2],
"label_lighting": info_vec[3],
} | def _generate_examples(self, dat_path, cat_path, info_path):
"""Generate examples for the Smallnorb dataset.
Args:
dat_path: Path to dat file of the chunk.
cat_path: Path to cat file of the chunk.
info_path: Path to info file of the chunk.
Yields:
Dictionaries with images and the different labels.
"""
dat_arr, cat_arr, info_arr = _load_chunk(dat_path, cat_path, info_path)
for image, category, info_vec in moves.zip(dat_arr, cat_arr, info_arr):
yield {
"image": image[0],
"image2": image[1],
"label_category": category,
"instance": info_vec[0],
"label_elevation": info_vec[1],
"label_azimuth": info_vec[2],
"label_lighting": info_vec[3],
} | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/smallnorb.py#L116-L138 | [
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train | build_dataset | Constructs a `tf.data.Dataset` from TFRecord files.
Args:
instruction_dicts: `list` of {'filepath':, 'mask':, 'offset_mask':}
containing the information about which files and which examples to use.
The boolean mask will be repeated and zipped with the examples from
filepath.
dataset_from_file_fn: function returning a `tf.data.Dataset` given a
filename.
shuffle_files: `bool`, Whether to shuffle the input filenames.
parallel_reads: `int`, how many files to read in parallel.
Returns:
`tf.data.Dataset` | tensorflow_datasets/core/dataset_utils.py | def build_dataset(instruction_dicts,
dataset_from_file_fn,
shuffle_files=False,
parallel_reads=64):
"""Constructs a `tf.data.Dataset` from TFRecord files.
Args:
instruction_dicts: `list` of {'filepath':, 'mask':, 'offset_mask':}
containing the information about which files and which examples to use.
The boolean mask will be repeated and zipped with the examples from
filepath.
dataset_from_file_fn: function returning a `tf.data.Dataset` given a
filename.
shuffle_files: `bool`, Whether to shuffle the input filenames.
parallel_reads: `int`, how many files to read in parallel.
Returns:
`tf.data.Dataset`
"""
# First case: All examples are taken (No value skipped)
if _no_examples_skipped(instruction_dicts):
# Only use the filenames as instruction
instruction_ds = tf.data.Dataset.from_tensor_slices([
d["filepath"] for d in instruction_dicts
])
build_ds_from_instruction = dataset_from_file_fn
# Second case: Use the instructions to read the examples
else:
instruction_ds = _build_instruction_ds(instruction_dicts)
build_ds_from_instruction = functools.partial(
_build_ds_from_instruction,
ds_from_file_fn=dataset_from_file_fn,
)
# If shuffle is True, we shuffle the instructions/shards
if shuffle_files:
instruction_ds = instruction_ds.shuffle(len(instruction_dicts))
# Use interleave to parallel read files and decode records
ds = instruction_ds.interleave(
build_ds_from_instruction,
cycle_length=parallel_reads,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
return ds | def build_dataset(instruction_dicts,
dataset_from_file_fn,
shuffle_files=False,
parallel_reads=64):
"""Constructs a `tf.data.Dataset` from TFRecord files.
Args:
instruction_dicts: `list` of {'filepath':, 'mask':, 'offset_mask':}
containing the information about which files and which examples to use.
The boolean mask will be repeated and zipped with the examples from
filepath.
dataset_from_file_fn: function returning a `tf.data.Dataset` given a
filename.
shuffle_files: `bool`, Whether to shuffle the input filenames.
parallel_reads: `int`, how many files to read in parallel.
Returns:
`tf.data.Dataset`
"""
# First case: All examples are taken (No value skipped)
if _no_examples_skipped(instruction_dicts):
# Only use the filenames as instruction
instruction_ds = tf.data.Dataset.from_tensor_slices([
d["filepath"] for d in instruction_dicts
])
build_ds_from_instruction = dataset_from_file_fn
# Second case: Use the instructions to read the examples
else:
instruction_ds = _build_instruction_ds(instruction_dicts)
build_ds_from_instruction = functools.partial(
_build_ds_from_instruction,
ds_from_file_fn=dataset_from_file_fn,
)
# If shuffle is True, we shuffle the instructions/shards
if shuffle_files:
instruction_ds = instruction_ds.shuffle(len(instruction_dicts))
# Use interleave to parallel read files and decode records
ds = instruction_ds.interleave(
build_ds_from_instruction,
cycle_length=parallel_reads,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
return ds | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_utils.py#L32-L76 | [
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train | _build_instruction_ds | Create a dataset containing individual instruction for each shard.
Each instruction is a dict:
```
{
"filepath": tf.Tensor(shape=(), dtype=tf.string),
"mask_offset": tf.Tensor(shape=(), dtype=tf.int64),
"mask": tf.Tensor(shape=(100,), dtype=tf.bool),
}
```
Args:
instructions: `list[dict]`, the list of instruction dict
Returns:
instruction_ds: The dataset containing the instruction. The dataset size is
the number of shard. | tensorflow_datasets/core/dataset_utils.py | def _build_instruction_ds(instructions):
"""Create a dataset containing individual instruction for each shard.
Each instruction is a dict:
```
{
"filepath": tf.Tensor(shape=(), dtype=tf.string),
"mask_offset": tf.Tensor(shape=(), dtype=tf.int64),
"mask": tf.Tensor(shape=(100,), dtype=tf.bool),
}
```
Args:
instructions: `list[dict]`, the list of instruction dict
Returns:
instruction_ds: The dataset containing the instruction. The dataset size is
the number of shard.
"""
# Transpose the list[dict] into dict[list]
tensor_inputs = {
# offset_mask need to be converted to int64 explicitly
k: np.array(vals, dtype=np.int64) if k == "mask_offset" else list(vals)
for k, vals in utils.zip_dict(*instructions)
}
return tf.data.Dataset.from_tensor_slices(tensor_inputs) | def _build_instruction_ds(instructions):
"""Create a dataset containing individual instruction for each shard.
Each instruction is a dict:
```
{
"filepath": tf.Tensor(shape=(), dtype=tf.string),
"mask_offset": tf.Tensor(shape=(), dtype=tf.int64),
"mask": tf.Tensor(shape=(100,), dtype=tf.bool),
}
```
Args:
instructions: `list[dict]`, the list of instruction dict
Returns:
instruction_ds: The dataset containing the instruction. The dataset size is
the number of shard.
"""
# Transpose the list[dict] into dict[list]
tensor_inputs = {
# offset_mask need to be converted to int64 explicitly
k: np.array(vals, dtype=np.int64) if k == "mask_offset" else list(vals)
for k, vals in utils.zip_dict(*instructions)
}
return tf.data.Dataset.from_tensor_slices(tensor_inputs) | [
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train | _build_mask_ds | Build the mask dataset to indicate which element to skip.
Args:
mask: `tf.Tensor`, binary mask to apply to all following elements. This
mask should have a length 100.
mask_offset: `tf.Tensor`, Integer specifying from how much the mask
should be shifted for the first element.
Returns:
mask_ds: `tf.data.Dataset`, a dataset returning False for examples to skip
and True for examples to keep. | tensorflow_datasets/core/dataset_utils.py | def _build_mask_ds(mask, mask_offset):
"""Build the mask dataset to indicate which element to skip.
Args:
mask: `tf.Tensor`, binary mask to apply to all following elements. This
mask should have a length 100.
mask_offset: `tf.Tensor`, Integer specifying from how much the mask
should be shifted for the first element.
Returns:
mask_ds: `tf.data.Dataset`, a dataset returning False for examples to skip
and True for examples to keep.
"""
mask_ds = tf.data.Dataset.from_tensor_slices(mask)
mask_ds = mask_ds.repeat()
mask_ds = mask_ds.skip(mask_offset)
return mask_ds | def _build_mask_ds(mask, mask_offset):
"""Build the mask dataset to indicate which element to skip.
Args:
mask: `tf.Tensor`, binary mask to apply to all following elements. This
mask should have a length 100.
mask_offset: `tf.Tensor`, Integer specifying from how much the mask
should be shifted for the first element.
Returns:
mask_ds: `tf.data.Dataset`, a dataset returning False for examples to skip
and True for examples to keep.
"""
mask_ds = tf.data.Dataset.from_tensor_slices(mask)
mask_ds = mask_ds.repeat()
mask_ds = mask_ds.skip(mask_offset)
return mask_ds | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_utils.py#L112-L128 | [
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train | _build_ds_from_instruction | Map an instruction to a real datasets for one particular shard.
Args:
instruction: A `dict` of `tf.Tensor` containing the instruction to load
the particular shard (filename, mask,...)
ds_from_file_fn: `fct`, function which returns the dataset associated to
the filename
Returns:
dataset: `tf.data.Dataset`, The shard loaded from the instruction | tensorflow_datasets/core/dataset_utils.py | def _build_ds_from_instruction(instruction, ds_from_file_fn):
"""Map an instruction to a real datasets for one particular shard.
Args:
instruction: A `dict` of `tf.Tensor` containing the instruction to load
the particular shard (filename, mask,...)
ds_from_file_fn: `fct`, function which returns the dataset associated to
the filename
Returns:
dataset: `tf.data.Dataset`, The shard loaded from the instruction
"""
# Create the example and mask ds for this particular shard
examples_ds = ds_from_file_fn(instruction["filepath"])
mask_ds = _build_mask_ds(
mask_offset=instruction["mask_offset"],
mask=instruction["mask"],
)
# Zip the mask and real examples
ds = tf.data.Dataset.zip((examples_ds, mask_ds))
# Filter according to the mask (only keep True)
ds = ds.filter(lambda example, mask: mask)
# Only keep the examples
ds = ds.map(lambda example, mask: example)
return ds | def _build_ds_from_instruction(instruction, ds_from_file_fn):
"""Map an instruction to a real datasets for one particular shard.
Args:
instruction: A `dict` of `tf.Tensor` containing the instruction to load
the particular shard (filename, mask,...)
ds_from_file_fn: `fct`, function which returns the dataset associated to
the filename
Returns:
dataset: `tf.data.Dataset`, The shard loaded from the instruction
"""
# Create the example and mask ds for this particular shard
examples_ds = ds_from_file_fn(instruction["filepath"])
mask_ds = _build_mask_ds(
mask_offset=instruction["mask_offset"],
mask=instruction["mask"],
)
# Zip the mask and real examples
ds = tf.data.Dataset.zip((examples_ds, mask_ds))
# Filter according to the mask (only keep True)
ds = ds.filter(lambda example, mask: mask)
# Only keep the examples
ds = ds.map(lambda example, mask: example)
return ds | [
"Map",
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"to",
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"real",
"datasets",
"for",
"one",
"particular",
"shard",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_utils.py#L131-L156 | [
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train | as_numpy | Converts a `tf.data.Dataset` to an iterable of NumPy arrays.
`as_numpy` converts a possibly nested structure of `tf.data.Dataset`s
and `tf.Tensor`s to iterables of NumPy arrays and NumPy arrays, respectively.
Args:
dataset: a possibly nested structure of `tf.data.Dataset`s and/or
`tf.Tensor`s.
graph: `tf.Graph`, optional, explicitly set the graph to use.
Returns:
A structure matching `dataset` where `tf.data.Dataset`s are converted to
generators of NumPy arrays and `tf.Tensor`s are converted to NumPy arrays. | tensorflow_datasets/core/dataset_utils.py | def as_numpy(dataset, graph=None):
"""Converts a `tf.data.Dataset` to an iterable of NumPy arrays.
`as_numpy` converts a possibly nested structure of `tf.data.Dataset`s
and `tf.Tensor`s to iterables of NumPy arrays and NumPy arrays, respectively.
Args:
dataset: a possibly nested structure of `tf.data.Dataset`s and/or
`tf.Tensor`s.
graph: `tf.Graph`, optional, explicitly set the graph to use.
Returns:
A structure matching `dataset` where `tf.data.Dataset`s are converted to
generators of NumPy arrays and `tf.Tensor`s are converted to NumPy arrays.
"""
nested_ds = dataset
del dataset
# Flatten
flat_ds = tf.nest.flatten(nested_ds)
flat_np = []
# Type check for Tensors and Datasets
for ds_el in flat_ds:
types = [type(el) for el in flat_ds]
types = tf.nest.pack_sequence_as(nested_ds, types)
if not (isinstance(ds_el, tf.Tensor) or tf_compat.is_dataset(ds_el)):
raise ValueError("Arguments to as_numpy must be tf.Tensors or "
"tf.data.Datasets. Got: %s" % types)
if tf.executing_eagerly():
# Eager mode
for ds_el in flat_ds:
if isinstance(ds_el, tf.Tensor):
np_el = ds_el.numpy()
elif tf_compat.is_dataset(ds_el):
np_el = _eager_dataset_iterator(ds_el)
else:
assert False
flat_np.append(np_el)
else:
# Graph mode
# First create iterators for datasets
with utils.maybe_with_graph(graph, create_if_none=False):
ds_iters = [
tf.compat.v1.data.make_one_shot_iterator(ds_el).get_next()
for ds_el in flat_ds if tf_compat.is_dataset(ds_el)
]
ds_iters = [_graph_dataset_iterator(ds_iter, graph) for ds_iter in ds_iters]
# Then create numpy arrays for tensors
with utils.nogpu_session(graph) as sess: # Shared session for tf.Tensor
# Calling sess.run once so that randomness is shared.
np_arrays = sess.run([tensor for tensor in flat_ds
if not tf_compat.is_dataset(tensor)])
# Merge the dataset iterators and np arrays
iter_ds = iter(ds_iters)
iter_array = iter(np_arrays)
flat_np = [
next(iter_ds) if tf_compat.is_dataset(ds_el) else next(iter_array)
for ds_el in flat_ds
]
# Nest
return tf.nest.pack_sequence_as(nested_ds, flat_np) | def as_numpy(dataset, graph=None):
"""Converts a `tf.data.Dataset` to an iterable of NumPy arrays.
`as_numpy` converts a possibly nested structure of `tf.data.Dataset`s
and `tf.Tensor`s to iterables of NumPy arrays and NumPy arrays, respectively.
Args:
dataset: a possibly nested structure of `tf.data.Dataset`s and/or
`tf.Tensor`s.
graph: `tf.Graph`, optional, explicitly set the graph to use.
Returns:
A structure matching `dataset` where `tf.data.Dataset`s are converted to
generators of NumPy arrays and `tf.Tensor`s are converted to NumPy arrays.
"""
nested_ds = dataset
del dataset
# Flatten
flat_ds = tf.nest.flatten(nested_ds)
flat_np = []
# Type check for Tensors and Datasets
for ds_el in flat_ds:
types = [type(el) for el in flat_ds]
types = tf.nest.pack_sequence_as(nested_ds, types)
if not (isinstance(ds_el, tf.Tensor) or tf_compat.is_dataset(ds_el)):
raise ValueError("Arguments to as_numpy must be tf.Tensors or "
"tf.data.Datasets. Got: %s" % types)
if tf.executing_eagerly():
# Eager mode
for ds_el in flat_ds:
if isinstance(ds_el, tf.Tensor):
np_el = ds_el.numpy()
elif tf_compat.is_dataset(ds_el):
np_el = _eager_dataset_iterator(ds_el)
else:
assert False
flat_np.append(np_el)
else:
# Graph mode
# First create iterators for datasets
with utils.maybe_with_graph(graph, create_if_none=False):
ds_iters = [
tf.compat.v1.data.make_one_shot_iterator(ds_el).get_next()
for ds_el in flat_ds if tf_compat.is_dataset(ds_el)
]
ds_iters = [_graph_dataset_iterator(ds_iter, graph) for ds_iter in ds_iters]
# Then create numpy arrays for tensors
with utils.nogpu_session(graph) as sess: # Shared session for tf.Tensor
# Calling sess.run once so that randomness is shared.
np_arrays = sess.run([tensor for tensor in flat_ds
if not tf_compat.is_dataset(tensor)])
# Merge the dataset iterators and np arrays
iter_ds = iter(ds_iters)
iter_array = iter(np_arrays)
flat_np = [
next(iter_ds) if tf_compat.is_dataset(ds_el) else next(iter_array)
for ds_el in flat_ds
]
# Nest
return tf.nest.pack_sequence_as(nested_ds, flat_np) | [
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_utils.py#L176-L242 | [
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train | _load_data | Loads the images and latent values into Numpy arrays. | tensorflow_datasets/image/shapes3d.py | def _load_data(filepath):
"""Loads the images and latent values into Numpy arrays."""
with h5py.File(filepath, "r") as h5dataset:
image_array = np.array(h5dataset["images"])
# The 'label' data set in the hdf5 file actually contains the float values
# and not the class labels.
values_array = np.array(h5dataset["labels"])
return image_array, values_array | def _load_data(filepath):
"""Loads the images and latent values into Numpy arrays."""
with h5py.File(filepath, "r") as h5dataset:
image_array = np.array(h5dataset["images"])
# The 'label' data set in the hdf5 file actually contains the float values
# and not the class labels.
values_array = np.array(h5dataset["labels"])
return image_array, values_array | [
"Loads",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/shapes3d.py#L151-L158 | [
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train | _discretize | Discretizes array values to class labels. | tensorflow_datasets/image/shapes3d.py | def _discretize(a):
"""Discretizes array values to class labels."""
arr = np.asarray(a)
index = np.argsort(arr)
inverse_index = np.zeros(arr.size, dtype=np.intp)
inverse_index[index] = np.arange(arr.size, dtype=np.intp)
arr = arr[index]
obs = np.r_[True, arr[1:] != arr[:-1]]
return obs.cumsum()[inverse_index] - 1 | def _discretize(a):
"""Discretizes array values to class labels."""
arr = np.asarray(a)
index = np.argsort(arr)
inverse_index = np.zeros(arr.size, dtype=np.intp)
inverse_index[index] = np.arange(arr.size, dtype=np.intp)
arr = arr[index]
obs = np.r_[True, arr[1:] != arr[:-1]]
return obs.cumsum()[inverse_index] - 1 | [
"Discretizes",
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"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/shapes3d.py#L163-L171 | [
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train | Shapes3d._generate_examples | Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels. | tensorflow_datasets/image/shapes3d.py | def _generate_examples(self, filepath):
"""Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels.
"""
# Simultaneously iterating through the different data sets in the hdf5
# file will be slow with a single file. Instead, we first load everything
# into memory before yielding the samples.
image_array, values_array = _load_data(filepath)
# We need to calculate the class labels from the float values in the file.
labels_array = np.zeros_like(values_array, dtype=np.int64)
for i in range(values_array.shape[1]):
labels_array[:, i] = _discretize(values_array[:, i]) # pylint: disable=unsupported-assignment-operation
for image, labels, values in moves.zip(image_array, labels_array,
values_array):
yield {
"image": image,
"label_floor_hue": labels[0],
"label_wall_hue": labels[1],
"label_object_hue": labels[2],
"label_scale": labels[3],
"label_shape": labels[4],
"label_orientation": labels[5],
"value_floor_hue": values[0],
"value_wall_hue": values[1],
"value_object_hue": values[2],
"value_scale": values[3],
"value_shape": values[4],
"value_orientation": values[5],
} | def _generate_examples(self, filepath):
"""Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels.
"""
# Simultaneously iterating through the different data sets in the hdf5
# file will be slow with a single file. Instead, we first load everything
# into memory before yielding the samples.
image_array, values_array = _load_data(filepath)
# We need to calculate the class labels from the float values in the file.
labels_array = np.zeros_like(values_array, dtype=np.int64)
for i in range(values_array.shape[1]):
labels_array[:, i] = _discretize(values_array[:, i]) # pylint: disable=unsupported-assignment-operation
for image, labels, values in moves.zip(image_array, labels_array,
values_array):
yield {
"image": image,
"label_floor_hue": labels[0],
"label_wall_hue": labels[1],
"label_object_hue": labels[2],
"label_scale": labels[3],
"label_shape": labels[4],
"label_orientation": labels[5],
"value_floor_hue": values[0],
"value_wall_hue": values[1],
"value_object_hue": values[2],
"value_scale": values[3],
"value_shape": values[4],
"value_orientation": values[5],
} | [
"Generate",
"examples",
"for",
"the",
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"dataset",
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] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/shapes3d.py#L113-L148 | [
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train | _parse_and_clean_wikicode | Strips formatting and unwanted sections from raw page content. | tensorflow_datasets/text/wikipedia.py | def _parse_and_clean_wikicode(raw_content):
"""Strips formatting and unwanted sections from raw page content."""
wikicode = tfds.core.lazy_imports.mwparserfromhell.parse(raw_content)
# Filters for references, tables, and file/image links.
re_rm_wikilink = re.compile(
"^(?:File|Image|Media):", flags=re.IGNORECASE | re.UNICODE)
def rm_wikilink(obj):
return bool(re_rm_wikilink.match(six.text_type(obj.title)))
def rm_tag(obj):
return six.text_type(obj.tag) in {"ref", "table"}
def rm_template(obj):
return obj.name.lower() in {
"reflist", "notelist", "notelist-ua", "notelist-lr", "notelist-ur",
"notelist-lg"}
def try_remove_obj(obj, section):
try:
section.remove(obj)
except ValueError:
# For unknown reasons, objects are sometimes not found.
pass
section_text = []
# Filter individual sections to clean.
for section in wikicode.get_sections(
flat=True, include_lead=True, include_headings=True):
for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_templates(matches=rm_template, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_tags(matches=rm_tag, recursive=True):
try_remove_obj(obj, section)
section_text.append(section.strip_code().strip())
return "\n\n".join(section_text) | def _parse_and_clean_wikicode(raw_content):
"""Strips formatting and unwanted sections from raw page content."""
wikicode = tfds.core.lazy_imports.mwparserfromhell.parse(raw_content)
# Filters for references, tables, and file/image links.
re_rm_wikilink = re.compile(
"^(?:File|Image|Media):", flags=re.IGNORECASE | re.UNICODE)
def rm_wikilink(obj):
return bool(re_rm_wikilink.match(six.text_type(obj.title)))
def rm_tag(obj):
return six.text_type(obj.tag) in {"ref", "table"}
def rm_template(obj):
return obj.name.lower() in {
"reflist", "notelist", "notelist-ua", "notelist-lr", "notelist-ur",
"notelist-lg"}
def try_remove_obj(obj, section):
try:
section.remove(obj)
except ValueError:
# For unknown reasons, objects are sometimes not found.
pass
section_text = []
# Filter individual sections to clean.
for section in wikicode.get_sections(
flat=True, include_lead=True, include_headings=True):
for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_templates(matches=rm_template, recursive=True):
try_remove_obj(obj, section)
for obj in section.ifilter_tags(matches=rm_tag, recursive=True):
try_remove_obj(obj, section)
section_text.append(section.strip_code().strip())
return "\n\n".join(section_text) | [
"Strips",
"formatting",
"and",
"unwanted",
"sections",
"from",
"raw",
"page",
"content",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/wikipedia.py#L234-L269 | [
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train | Wikipedia._build_pcollection | Build PCollection of examples in the raw (text) form. | tensorflow_datasets/text/wikipedia.py | def _build_pcollection(self, pipeline, filepaths, language):
"""Build PCollection of examples in the raw (text) form."""
beam = tfds.core.lazy_imports.apache_beam
def _extract_content(filepath):
"""Extracts article content from a single WikiMedia XML file."""
logging.info("generating examples from = %s", filepath)
with tf.io.gfile.GFile(filepath) as f:
for _, elem in etree.iterparse(f, events=("end",)):
if not elem.tag.endswith("page"):
continue
namespace = elem.tag[:-4]
title = elem.find("./{0}title".format(namespace)).text
ns = elem.find("./{0}ns".format(namespace)).text
# Filter pages that are not in the "main" namespace.
if ns != "0":
continue
raw_content = elem.find(
"./{0}revision/{0}text".format(namespace)).text
elem.clear()
# Filter redirects.
if raw_content is None or raw_content.lower().startswith("#redirect"):
beam.metrics.Metrics.counter(language, "filtered-redirects").inc()
continue
beam.metrics.Metrics.counter(language, "extracted-examples").inc()
yield (title, raw_content)
def _clean_content(inputs):
"""Cleans raw wikicode to extract text."""
title, raw_content = inputs
try:
text = _parse_and_clean_wikicode(raw_content)
except (
tfds.core.lazy_imports.mwparserfromhell.parser.ParserError) as e:
beam.metrics.Metrics.counter(language, "parser-error").inc()
logging.error("mwparserfromhell ParseError: %s", e)
return
beam.metrics.Metrics.counter(language, "cleaned-examples").inc()
yield {
"title": title,
"text": text
}
return (
pipeline
| beam.Create(filepaths)
| beam.FlatMap(_extract_content)
| beam.FlatMap(_clean_content)
) | def _build_pcollection(self, pipeline, filepaths, language):
"""Build PCollection of examples in the raw (text) form."""
beam = tfds.core.lazy_imports.apache_beam
def _extract_content(filepath):
"""Extracts article content from a single WikiMedia XML file."""
logging.info("generating examples from = %s", filepath)
with tf.io.gfile.GFile(filepath) as f:
for _, elem in etree.iterparse(f, events=("end",)):
if not elem.tag.endswith("page"):
continue
namespace = elem.tag[:-4]
title = elem.find("./{0}title".format(namespace)).text
ns = elem.find("./{0}ns".format(namespace)).text
# Filter pages that are not in the "main" namespace.
if ns != "0":
continue
raw_content = elem.find(
"./{0}revision/{0}text".format(namespace)).text
elem.clear()
# Filter redirects.
if raw_content is None or raw_content.lower().startswith("#redirect"):
beam.metrics.Metrics.counter(language, "filtered-redirects").inc()
continue
beam.metrics.Metrics.counter(language, "extracted-examples").inc()
yield (title, raw_content)
def _clean_content(inputs):
"""Cleans raw wikicode to extract text."""
title, raw_content = inputs
try:
text = _parse_and_clean_wikicode(raw_content)
except (
tfds.core.lazy_imports.mwparserfromhell.parser.ParserError) as e:
beam.metrics.Metrics.counter(language, "parser-error").inc()
logging.error("mwparserfromhell ParseError: %s", e)
return
beam.metrics.Metrics.counter(language, "cleaned-examples").inc()
yield {
"title": title,
"text": text
}
return (
pipeline
| beam.Create(filepaths)
| beam.FlatMap(_extract_content)
| beam.FlatMap(_clean_content)
) | [
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"raw",
"(",
"text",
")",
"form",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/wikipedia.py#L176-L231 | [
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"\"\"\"Extracts article conte... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | download_and_prepare | Generate data for a given dataset. | tensorflow_datasets/scripts/download_and_prepare.py | def download_and_prepare(builder):
"""Generate data for a given dataset."""
print("download_and_prepare for dataset {}...".format(builder.info.full_name))
dl_config = download_config()
if isinstance(builder, tfds.core.BeamBasedBuilder):
beam = tfds.core.lazy_imports.apache_beam
# TODO(b/129149715): Restore compute stats. Currently skipped because not
# beam supported.
dl_config.compute_stats = tfds.download.ComputeStatsMode.SKIP
dl_config.beam_options = beam.options.pipeline_options.PipelineOptions()
builder.download_and_prepare(
download_dir=FLAGS.download_dir,
download_config=dl_config,
)
termcolor.cprint(str(builder.info.as_proto), attrs=["bold"])
if FLAGS.debug:
dataset = builder.as_dataset(split=tfds.Split.TRAIN)
pdb.set_trace()
del dataset | def download_and_prepare(builder):
"""Generate data for a given dataset."""
print("download_and_prepare for dataset {}...".format(builder.info.full_name))
dl_config = download_config()
if isinstance(builder, tfds.core.BeamBasedBuilder):
beam = tfds.core.lazy_imports.apache_beam
# TODO(b/129149715): Restore compute stats. Currently skipped because not
# beam supported.
dl_config.compute_stats = tfds.download.ComputeStatsMode.SKIP
dl_config.beam_options = beam.options.pipeline_options.PipelineOptions()
builder.download_and_prepare(
download_dir=FLAGS.download_dir,
download_config=dl_config,
)
termcolor.cprint(str(builder.info.as_proto), attrs=["bold"])
if FLAGS.debug:
dataset = builder.as_dataset(split=tfds.Split.TRAIN)
pdb.set_trace()
del dataset | [
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train | BBoxFeature.encode_example | See base class for details. | tensorflow_datasets/core/features/bounding_boxes.py | def encode_example(self, bbox):
"""See base class for details."""
# Validate the coordinates
for coordinate in bbox:
if not isinstance(coordinate, float):
raise ValueError(
'BBox coordinates should be float. Got {}.'.format(bbox))
if not 0.0 <= coordinate <= 1.0:
raise ValueError(
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return super(BBoxFeature, self).encode_example(
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) | def encode_example(self, bbox):
"""See base class for details."""
# Validate the coordinates
for coordinate in bbox:
if not isinstance(coordinate, float):
raise ValueError(
'BBox coordinates should be float. Got {}.'.format(bbox))
if not 0.0 <= coordinate <= 1.0:
raise ValueError(
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if bbox.xmax < bbox.xmin or bbox.ymax < bbox.ymin:
raise ValueError(
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return super(BBoxFeature, self).encode_example(
[bbox.ymin, bbox.xmin, bbox.ymax, bbox.xmax]
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train | _load_data | Yields (labels, np_image) tuples. | tensorflow_datasets/image/cifar.py | def _load_data(path, labels_number=1):
"""Yields (labels, np_image) tuples."""
with tf.io.gfile.GFile(path, "rb") as f:
data = f.read()
offset = 0
max_offset = len(data) - 1
while offset < max_offset:
labels = np.frombuffer(data, dtype=np.uint8, count=labels_number,
offset=offset).reshape((labels_number,))
# 1 byte per label, 1024 * 3 = 3072 bytes for the image.
offset += labels_number
img = (np.frombuffer(data, dtype=np.uint8, count=3072, offset=offset)
.reshape((3, _CIFAR_IMAGE_SIZE, _CIFAR_IMAGE_SIZE))
.transpose((1, 2, 0))
)
offset += 3072
yield labels, img | def _load_data(path, labels_number=1):
"""Yields (labels, np_image) tuples."""
with tf.io.gfile.GFile(path, "rb") as f:
data = f.read()
offset = 0
max_offset = len(data) - 1
while offset < max_offset:
labels = np.frombuffer(data, dtype=np.uint8, count=labels_number,
offset=offset).reshape((labels_number,))
# 1 byte per label, 1024 * 3 = 3072 bytes for the image.
offset += labels_number
img = (np.frombuffer(data, dtype=np.uint8, count=3072, offset=offset)
.reshape((3, _CIFAR_IMAGE_SIZE, _CIFAR_IMAGE_SIZE))
.transpose((1, 2, 0))
)
offset += 3072
yield labels, img | [
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train | Cifar10._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/cifar.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
cifar_path = dl_manager.download_and_extract(self._cifar_info.url)
cifar_info = self._cifar_info
cifar_path = os.path.join(cifar_path, cifar_info.prefix)
# Load the label names
for label_key, label_file in zip(cifar_info.label_keys,
cifar_info.label_files):
labels_path = os.path.join(cifar_path, label_file)
with tf.io.gfile.GFile(labels_path) as label_f:
label_names = [name for name in label_f.read().split("\n") if name]
self.info.features[label_key].names = label_names
# Define the splits
def gen_filenames(filenames):
for f in filenames:
yield os.path.join(cifar_path, f)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs={"filepaths": gen_filenames(cifar_info.train_files)}),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs={"filepaths": gen_filenames(cifar_info.test_files)}),
] | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
cifar_path = dl_manager.download_and_extract(self._cifar_info.url)
cifar_info = self._cifar_info
cifar_path = os.path.join(cifar_path, cifar_info.prefix)
# Load the label names
for label_key, label_file in zip(cifar_info.label_keys,
cifar_info.label_files):
labels_path = os.path.join(cifar_path, label_file)
with tf.io.gfile.GFile(labels_path) as label_f:
label_names = [name for name in label_f.read().split("\n") if name]
self.info.features[label_key].names = label_names
# Define the splits
def gen_filenames(filenames):
for f in filenames:
yield os.path.join(cifar_path, f)
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs={"filepaths": gen_filenames(cifar_info.train_files)}),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=1,
gen_kwargs={"filepaths": gen_filenames(cifar_info.test_files)}),
] | [
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train | Cifar10._generate_examples | Generate CIFAR examples as dicts.
Shared across CIFAR-{10, 100}. Uses self._cifar_info as
configuration.
Args:
filepaths (list[str]): The files to use to generate the data.
Yields:
The cifar examples, as defined in the dataset info features. | tensorflow_datasets/image/cifar.py | def _generate_examples(self, filepaths):
"""Generate CIFAR examples as dicts.
Shared across CIFAR-{10, 100}. Uses self._cifar_info as
configuration.
Args:
filepaths (list[str]): The files to use to generate the data.
Yields:
The cifar examples, as defined in the dataset info features.
"""
label_keys = self._cifar_info.label_keys
for path in filepaths:
for labels, np_image in _load_data(path, len(label_keys)):
row = dict(zip(label_keys, labels))
row["image"] = np_image
yield row | def _generate_examples(self, filepaths):
"""Generate CIFAR examples as dicts.
Shared across CIFAR-{10, 100}. Uses self._cifar_info as
configuration.
Args:
filepaths (list[str]): The files to use to generate the data.
Yields:
The cifar examples, as defined in the dataset info features.
"""
label_keys = self._cifar_info.label_keys
for path in filepaths:
for labels, np_image in _load_data(path, len(label_keys)):
row = dict(zip(label_keys, labels))
row["image"] = np_image
yield row | [
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train | disallow_positional_args | Requires function to be called using keyword arguments. | tensorflow_datasets/core/api_utils.py | def disallow_positional_args(wrapped=None, allowed=None):
"""Requires function to be called using keyword arguments."""
# See
# https://wrapt.readthedocs.io/en/latest/decorators.html#decorators-with-optional-arguments
# for decorator pattern.
if wrapped is None:
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def disallow_positional_args_dec(fn, instance, args, kwargs):
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return fn(*args, **kwargs)
return disallow_positional_args_dec(wrapped) | def disallow_positional_args(wrapped=None, allowed=None):
"""Requires function to be called using keyword arguments."""
# See
# https://wrapt.readthedocs.io/en/latest/decorators.html#decorators-with-optional-arguments
# for decorator pattern.
if wrapped is None:
return functools.partial(disallow_positional_args, allowed=allowed)
@wrapt.decorator
def disallow_positional_args_dec(fn, instance, args, kwargs):
ismethod = instance is not None
_check_no_positional(fn, args, ismethod, allowed=allowed)
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return fn(*args, **kwargs)
return disallow_positional_args_dec(wrapped) | [
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train | _required_args | Returns arguments of fn with default=REQUIRED_ARG. | tensorflow_datasets/core/api_utils.py | def _required_args(fn):
"""Returns arguments of fn with default=REQUIRED_ARG."""
spec = getargspec(fn)
if not spec.defaults:
return []
arg_names = spec.args[-len(spec.defaults):]
return [name for name, val in zip(arg_names, spec.defaults)
if val is REQUIRED_ARG] | def _required_args(fn):
"""Returns arguments of fn with default=REQUIRED_ARG."""
spec = getargspec(fn)
if not spec.defaults:
return []
arg_names = spec.args[-len(spec.defaults):]
return [name for name, val in zip(arg_names, spec.defaults)
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train | download_gcs_file | Download a file from GCS, optionally to a file. | tensorflow_datasets/core/utils/gcs_utils.py | def download_gcs_file(path, out_fname=None, prefix_filter=None):
"""Download a file from GCS, optionally to a file."""
url = posixpath.join(GCS_BUCKET, path)
if prefix_filter:
url += "?prefix=%s" % prefix_filter
stream = bool(out_fname)
resp = requests.get(url, stream=stream)
if not resp.ok:
raise ValueError("GCS bucket inaccessible")
if out_fname:
with tf.io.gfile.GFile(out_fname, "wb") as f:
for chunk in resp.iter_content(1024):
f.write(chunk)
else:
return resp.content | def download_gcs_file(path, out_fname=None, prefix_filter=None):
"""Download a file from GCS, optionally to a file."""
url = posixpath.join(GCS_BUCKET, path)
if prefix_filter:
url += "?prefix=%s" % prefix_filter
stream = bool(out_fname)
resp = requests.get(url, stream=stream)
if not resp.ok:
raise ValueError("GCS bucket inaccessible")
if out_fname:
with tf.io.gfile.GFile(out_fname, "wb") as f:
for chunk in resp.iter_content(1024):
f.write(chunk)
else:
return resp.content | [
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train | gcs_files | List all files in GCS bucket. | tensorflow_datasets/core/utils/gcs_utils.py | def gcs_files(prefix_filter=None):
"""List all files in GCS bucket."""
top_level_xml_str = download_gcs_file("", prefix_filter=prefix_filter)
xml_root = ElementTree.fromstring(top_level_xml_str)
filenames = [el[0].text for el in xml_root if el.tag.endswith("Contents")]
return filenames | def gcs_files(prefix_filter=None):
"""List all files in GCS bucket."""
top_level_xml_str = download_gcs_file("", prefix_filter=prefix_filter)
xml_root = ElementTree.fromstring(top_level_xml_str)
filenames = [el[0].text for el in xml_root if el.tag.endswith("Contents")]
return filenames | [
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train | gcs_dataset_info_files | Return paths to GCS files in the given dataset directory. | tensorflow_datasets/core/utils/gcs_utils.py | def gcs_dataset_info_files(dataset_dir):
"""Return paths to GCS files in the given dataset directory."""
prefix = posixpath.join(GCS_DATASET_INFO_DIR, dataset_dir, "")
# Filter for this dataset
filenames = [el for el in gcs_files(prefix_filter=prefix)
if el.startswith(prefix) and len(el) > len(prefix)]
return filenames | def gcs_dataset_info_files(dataset_dir):
"""Return paths to GCS files in the given dataset directory."""
prefix = posixpath.join(GCS_DATASET_INFO_DIR, dataset_dir, "")
# Filter for this dataset
filenames = [el for el in gcs_files(prefix_filter=prefix)
if el.startswith(prefix) and len(el) > len(prefix)]
return filenames | [
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train | is_dataset_on_gcs | If the dataset is available on the GCS bucket gs://tfds-data/datasets. | tensorflow_datasets/core/utils/gcs_utils.py | def is_dataset_on_gcs(dataset_name):
"""If the dataset is available on the GCS bucket gs://tfds-data/datasets."""
dir_name = posixpath.join(GCS_DATASETS_DIR, dataset_name)
return len(gcs_files(prefix_filter=dir_name)) > 2 | def is_dataset_on_gcs(dataset_name):
"""If the dataset is available on the GCS bucket gs://tfds-data/datasets."""
dir_name = posixpath.join(GCS_DATASETS_DIR, dataset_name)
return len(gcs_files(prefix_filter=dir_name)) > 2 | [
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train | _run_kaggle_command | Run kaggle command with subprocess. | tensorflow_datasets/core/download/kaggle.py | def _run_kaggle_command(command_args, competition_name):
"""Run kaggle command with subprocess."""
try:
output = sp.check_output(command_args)
return tf.compat.as_text(output)
except sp.CalledProcessError as err:
output = err.output
_log_command_output(output, error=True)
if output.startswith(b"404"):
logging.error(_NOT_FOUND_ERR_MSG, competition_name)
raise
logging.error(_ERR_MSG, competition_name)
raise | def _run_kaggle_command(command_args, competition_name):
"""Run kaggle command with subprocess."""
try:
output = sp.check_output(command_args)
return tf.compat.as_text(output)
except sp.CalledProcessError as err:
output = err.output
_log_command_output(output, error=True)
if output.startswith(b"404"):
logging.error(_NOT_FOUND_ERR_MSG, competition_name)
raise
logging.error(_ERR_MSG, competition_name)
raise | [
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train | KaggleCompetitionDownloader.competition_files | List of competition files. | tensorflow_datasets/core/download/kaggle.py | def competition_files(self):
"""List of competition files."""
command = [
"kaggle",
"datasets" if "/" in self._competition_name else "competitions",
"files",
"-v",
self._competition_name,
]
output = _run_kaggle_command(command, self._competition_name)
return sorted([
line.split(",")[0] for line in output.split("\n")[1:] if line
]) | def competition_files(self):
"""List of competition files."""
command = [
"kaggle",
"datasets" if "/" in self._competition_name else "competitions",
"files",
"-v",
self._competition_name,
]
output = _run_kaggle_command(command, self._competition_name)
return sorted([
line.split(",")[0] for line in output.split("\n")[1:] if line
]) | [
"List",
"of",
"competition",
"files",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/kaggle.py#L96-L108 | [
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"else",
"\"competitions\"",
",",
"\"files\"",
",",
"\"-v\"",
",",
"self",
".",
"_competition_name",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | KaggleCompetitionDownloader.competition_urls | Returns 'kaggle://' urls. | tensorflow_datasets/core/download/kaggle.py | def competition_urls(self):
"""Returns 'kaggle://' urls."""
return [
KaggleFile(self._competition_name, fname).to_url()
for fname in self.competition_files # pylint: disable=not-an-iterable
] | def competition_urls(self):
"""Returns 'kaggle://' urls."""
return [
KaggleFile(self._competition_name, fname).to_url()
for fname in self.competition_files # pylint: disable=not-an-iterable
] | [
"Returns",
"kaggle",
":",
"//",
"urls",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/kaggle.py#L111-L116 | [
"def",
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"competition_files",
"# pylint: disable=not-an-iterable",
"]"
] | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
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