INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
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
... | 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 decorato... |
Returns file name of file at given url. | 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' |
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>` | 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 = [
... |
Walk a Librispeech directory and yield examples. | 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_... |
Returns download urls for this config. | 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")... |
Conversion class name string => integer. | 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:
... |
Conversion integer => class name string. | 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... |
See base class for details. | 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) |
See base class for details. | 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) |
Builds token counts from generator. | 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 = co... |
Validate arguments for SubwordTextEncoder.build_from_corpus. | 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 "
... |
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. | 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<st... |
Encodes text into a list of integers. | 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) |
Decodes a list of integers into text. | 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
... |
Convert a single token to a list of integer 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_subwor... |
Encode a single token byte-wise into integer ids. | 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... |
Converts a subword integer ID to a subword string. | 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 ... |
Greedily split token into 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 ... |
Initializes the encoder from a list of subwords. | 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.
... |
Save the vocabulary to a file. | 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]
s... |
Extracts list of subwords from file. | 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) |
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 th... | 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... |
Generate features given the directory path.
Args:
file_path: path where the csv file is stored
Yields:
The features, per 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_magnitud... |
Generate Cats vs Dogs images and labels given a directory path. | 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("JF... |
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. | 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 = r... |
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. | 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.
"""
wi... |
Returns splits. | 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_... |
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. | 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 d... |
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_fi... | 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 informa... |
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[d... | 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)... |
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_... | 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 ... |
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: `t... | 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 d... |
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.
gra... | 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.Data... |
Loads the images and latent values into Numpy arrays. | 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... |
Discretizes array values to class labels. | 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_... |
Generate examples for the Shapes3d dataset.
Args:
filepath: path to the Shapes3d hdf5 file.
Yields:
Dictionaries with images and the different labels. | 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 b... |
Strips formatting and unwanted sections from raw page content. | 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.IG... |
Build PCollection of examples in the raw (text) form. | 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 ... |
Generate data for a given 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): Re... |
See base class for details. | 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:
rais... |
Yields (labels, np_image) tuples. | 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=o... |
Returns SplitGenerators. | 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.l... |
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. | 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.... |
Requires function to be called using keyword arguments. | 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_positiona... |
Returns arguments of fn with default=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)
if val is REQUIRED_ARG] |
Download a file from GCS, optionally to a file. | 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 V... |
List all files in GCS bucket. | 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 |
Return paths to GCS files in the given dataset directory. | 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(pr... |
If the dataset is available on the GCS bucket gs://tfds-data/datasets. | 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 |
Run kaggle command with subprocess. | 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(... |
List of competition files. | 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 s... |
Returns 'kaggle://' urls. | 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
] |
Downloads competition file to output_dir. | def download_file(self, fname, output_dir):
"""Downloads competition file to output_dir."""
if fname not in self.competition_files: # pylint: disable=unsupported-membership-test
raise ValueError("%s is not one of the competition's "
"files: %s" % (fname, self.competition_files))
... |
Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label. | def _generate_examples(self, images_dir_path):
"""Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label.
"""
parent_dir = tf.io.gfile.listdir(images_... |
Returns dict {'dataset_name': 'path/to/checksums/file'}. | def _checksum_paths():
"""Returns dict {'dataset_name': 'path/to/checksums/file'}."""
dataset2path = {}
for dir_path in _CHECKSUM_DIRS:
for fname in _list_dir(dir_path):
if not fname.endswith(_CHECKSUM_SUFFIX):
continue
fpath = os.path.join(dir_path, fname)
dataset_name = fname[:-len... |
Returns path to where checksums are stored for a given dataset. | def _get_path(dataset_name):
"""Returns path to where checksums are stored for a given dataset."""
path = _checksum_paths().get(dataset_name, None)
if path:
return path
msg = ('No checksums file could be find for dataset %s. Please create one in '
'one of: %s') % (dataset_name, ', '.join(_CHECKSUM_... |
Returns {URL: (size, checksum)}s stored within file. | def _get_sizes_checksums(checksums_path):
"""Returns {URL: (size, checksum)}s stored within file."""
checksums = {}
for line in _read_file(checksums_path).split('\n'):
if not line:
continue
# URL might have spaces inside, but size and checksum will not.
url, size, checksum = line.rsplit(' ', 2)
... |
Returns dict associating URL to (size, sha256). | def get_all_sizes_checksums():
"""Returns dict associating URL to (size, sha256)."""
sizes_checksums = {}
for path in _checksum_paths().values():
data = _get_sizes_checksums(path)
for url, size_checksum in data.items():
if (url in sizes_checksums and
sizes_checksums[url] != size_checksum):... |
Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the responsibility of the caller not to call function multiple t... | def store_checksums(dataset_name, sizes_checksums):
"""Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the res... |
Guess extraction method, given file name (or path). | def _guess_extract_method(fname):
"""Guess extraction method, given file name (or path)."""
for method, extensions in _EXTRACTION_METHOD_TO_EXTS:
for ext in extensions:
if fname.endswith(ext):
return method
return ExtractMethod.NO_EXTRACT |
Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep useful info from url.
1- Drop generic [su... | def _sanitize_url(url, max_length):
"""Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep use... |
Returns name of file for (url, checksum).
The max length of linux and windows filenames is 255 chars.
Windows however expects short paths (260 chars), so we limit the file name
to an arbitrary 90 chars.
Naming pattern: '${url}${checksum}'.
- url: url sanitized and shortened to 46 chars.
- checksum: base... | def get_dl_fname(url, checksum):
"""Returns name of file for (url, checksum).
The max length of linux and windows filenames is 255 chars.
Windows however expects short paths (260 chars), so we limit the file name
to an arbitrary 90 chars.
Naming pattern: '${url}${checksum}'.
- url: url sanitized and shor... |
Returns name of temp dir for given url. | def get_dl_dirname(url):
"""Returns name of temp dir for given url."""
checksum = hashlib.sha256(tf.compat.as_bytes(url)).hexdigest()
return get_dl_fname(url, checksum) |
Returns info dict or None. | def _read_info(info_path):
"""Returns info dict or None."""
if not tf.io.gfile.exists(info_path):
return None
with tf.io.gfile.GFile(info_path) as info_f:
return json.load(info_f) |
Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
data (`dataset_name`) is only for human consumption.
Args:
resource: resource for which to write the INFO file.
... | def write_info_file(resource, path, dataset_name, original_fname):
"""Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
data (`dataset_name`) is only for human consumption... |
Returns `ExtractMethod` to use on resource at path. Cannot be None. | def get_extract_method(path):
"""Returns `ExtractMethod` to use on resource at path. Cannot be None."""
info_path = _get_info_path(path)
info = _read_info(info_path)
fname = info.get('original_fname', path) if info else path
return _guess_extract_method(fname) |
Returns whether the resource exists locally, at `resource.path`. | def exists_locally(cls, path):
"""Returns whether the resource exists locally, at `resource.path`."""
# If INFO file doesn't exist, consider resource does NOT exist, as it would
# prevent guessing the `extract_method`.
return (tf.io.gfile.exists(path) and
tf.io.gfile.exists(_get_info_path(pa... |
Returns SplitGenerators. | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
root_url = "http://images.cocodataset.org/"
urls = {
# Train/validation set
"train_images": "zips/train2014.zip",
"val_images": "zips/val2014.zip",
"trainval_annotations": "annotations/annotations_trainva... |
Generate examples as dicts.
Args:
image_dir: `str`, directory containing the images
annotation_dir: `str`, directory containing
split_type: `str`, <split_name><year> (ex: train2014)
has_annotation: `bool`, when False (for the testing set), the annotations
are not recorded
Yield... | def _generate_examples(
self, image_dir, annotation_dir, split_type, has_annotation=True):
"""Generate examples as dicts.
Args:
image_dir: `str`, directory containing the images
annotation_dir: `str`, directory containing
split_type: `str`, <split_name><year> (ex: train2014)
has_a... |
Conversion string => encoded list[int]. | def str2ints(self, str_value):
"""Conversion string => encoded list[int]."""
if not self._encoder:
raise ValueError(
"Text.str2ints is not available because encoder hasn't been defined.")
return self._encoder.encode(str_value) |
Conversion list[int] => decoded string. | def ints2str(self, int_values):
"""Conversion list[int] => decoded string."""
if not self._encoder:
raise ValueError(
"Text.ints2str is not available because encoder hasn't been defined.")
return self._encoder.decode(int_values) |
Call SubwordTextEncoder.build_from_corpus is encoder_cls is such. | def maybe_build_from_corpus(self, corpus_generator, **kwargs):
"""Call SubwordTextEncoder.build_from_corpus is encoder_cls is such."""
if self._encoder_cls is not text_lib.SubwordTextEncoder:
return
if self.encoder:
return
vocab_size = self._encoder_config.vocab_size
self.encoder = text... |
Sharded filenames given prefix and number of shards. | def sharded_filenames(filename_prefix, num_shards):
"""Sharded filenames given prefix and number of shards."""
shard_suffix = "%05d-of-%05d"
return [
"%s-%s" % (filename_prefix, shard_suffix % (i, num_shards))
for i in range(num_shards)
] |
Walk an Omniglot directory and yield examples. | def _walk_omniglot_dir(directory):
"""Walk an Omniglot directory and yield examples."""
directory = os.path.join(directory, tf.io.gfile.listdir(directory)[0])
alphabets = sorted(tf.io.gfile.listdir(directory))
for alphabet in alphabets:
alphabet_dir = os.path.join(directory, alphabet)
characters = sorte... |
Get alphabet and label names, union across all dirs. | def _get_names(dirs):
"""Get alphabet and label names, union across all dirs."""
alphabets = set()
label_names = {}
for d in dirs:
for example in _walk_omniglot_dir(d):
alphabet, alphabet_char_id, label, _ = example
alphabets.add(alphabet)
label_name = "%s_%d" % (alphabet, alphabet_char_id... |
Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string. | def size_str(size_in_bytes):
"""Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string.
"""
... |
Add a progression bar for the current download. | def tqdm(self):
"""Add a progression bar for the current download."""
async_tqdm = utils.async_tqdm
with async_tqdm(total=0, desc='Dl Completed...', unit=' url') as pbar_url:
with async_tqdm(total=0, desc='Dl Size...', unit=' MiB') as pbar_dl_size:
self._pbar_url = pbar_url
self._pbar_... |
Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
Returns:
Promise obj -> (`str`, int): (downloaded object checksum, size in bytes). | def download(self, url, destination_path):
"""Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
Returns:
Promise obj -> (`str`, int): (downl... |
Download with Kaggle API. | def _sync_kaggle_download(self, kaggle_url, destination_path):
"""Download with Kaggle API."""
kaggle_file = kaggle.KaggleFile.from_url(kaggle_url)
downloader = self.kaggle_downloader(kaggle_file.competition)
filepath = downloader.download_file(kaggle_file.filename, destination_path)
dl_size = tf.i... |
Returns url, possibly with confirmation token. | def _get_drive_url(self, url, session):
"""Returns url, possibly with confirmation token."""
response = session.get(url, stream=True)
if response.status_code != 200:
raise DownloadError(
'Failed to get url %s. HTTP code: %d.' % (url, response.status_code))
for k, v in response.cookies.it... |
Synchronous version of `download` method. | def _sync_download(self, url, destination_path):
"""Synchronous version of `download` method."""
proxies = {
'http': os.environ.get('TFDS_HTTP_PROXY', None),
'https': os.environ.get('TFDS_HTTPS_PROXY', None),
'ftp': os.environ.get('TFDS_FTP_PROXY', None)
}
if kaggle.KaggleFile.is... |
Resize an image to have (roughly) the given number of target pixels.
Args:
image_fobj: File object containing the original image.
target_pixels: If given, number of pixels that the image must have.
Returns:
A file object. | def _resize_image_if_necessary(image_fobj, target_pixels=None):
"""Resize an image to have (roughly) the given number of target pixels.
Args:
image_fobj: File object containing the original image.
target_pixels: If given, number of pixels that the image must have.
Returns:
A file object.
"""
if ... |
Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan image directory, don't set labels.
csv_usage: optional, subset of examples fro... | def _generate_examples(self, images_dir_path, csv_path=None, csv_usage=None):
"""Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan... |
Return the list of files and reading mask of the files to read. | def _slice_split_info_to_instruction_dicts(self, list_sliced_split_info):
"""Return the list of files and reading mask of the files to read."""
instruction_dicts = []
for sliced_split_info in list_sliced_split_info:
mask = splits_lib.slice_to_percent_mask(sliced_split_info.slice_value)
# Comput... |
Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generate the
filenames
Returns:
filenames: (li... | def _build_split_filenames(self, split_info_list):
"""Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generat... |
Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays | def _generate_examples(self, data_path):
"""Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays
"""
with tf.io.gfile.GFile(data_path, "rb") as fp:
images = np.load(fp)
images = np.transpose(images, (1, 0, 2,... |
Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions. | def _parse_single_video(self, example_proto):
"""Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions.
"""
context_features = {
"game_duration_loops": tf.io.FixedLenFeature([... |
Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values. | def _generate_examples(self, filepath):
"""Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values.
"""
# Simultaneously iterating through the different data sets in the hdf5
#... |
Returns splits. | def _split_generators(self, dl_manager):
"""Returns splits."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
"image... |
Returns objects listed within given CSV files. | def _load_objects(csv_paths, csv_positions, prefix):
"""Returns objects listed within given CSV files."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_paths, csv_positions, prefix)
objects = collections.defaultdict(list)
for i, labels_path in enumerate(csv_paths):
with... |
Returns bounded boxes listed within given CSV file. | def _load_bboxes(csv_path, csv_positions, prefix):
"""Returns bounded boxes listed within given CSV file."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_path, csv_positions, prefix)
boxes = collections.defaultdict(list)
with tf.io.gfile.GFile(csv_path) as csv_f:
if cs... |
Returns SplitGenerators. | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
paths = dl_manager.download_and_extract(_URLS)
# Load labels from CSVs:
def load(names):
csv_positions = [0] * len(names)
return functools.partial(_load_objects, [paths[name] for name in names],
... |
Yields examples. | def _generate_examples(self, archive_paths, objects_getter, bboxes_getter,
prefixes=None):
"""Yields examples."""
trainable_classes = set(
self.info.features['objects_trainable']['label'].names)
for i, archive_path in enumerate(archive_paths):
prefix = prefixes[i] if p... |
Generate IMDB examples. | def _generate_examples(self, archive, directory):
"""Generate IMDB examples."""
reg = re.compile(os.path.join("^%s" % directory, "(?P<label>neg|pos)", ""))
for path, imdb_f in archive:
res = reg.match(path)
if not res:
continue
text = imdb_f.read().strip()
yield {
"... |
Get hashes of urls in file. | def _get_url_hashes(path):
"""Get hashes of urls in file."""
urls = _read_text_file(path)
def url_hash(u):
h = hashlib.sha1()
try:
u = u.encode('utf-8')
except UnicodeDecodeError:
logging.error('Cannot hash url: %s', u)
h.update(u)
return h.hexdigest()
return {url_hash(u): True f... |
Find files corresponding to urls. | def _find_files(dl_paths, publisher, url_dict):
"""Find files corresponding to urls."""
if publisher == 'cnn':
top_dir = os.path.join(dl_paths['cnn_stories'], 'cnn', 'stories')
elif publisher == 'dm':
top_dir = os.path.join(dl_paths['dm_stories'], 'dailymail', 'stories')
else:
logging.fatal('Unsuppo... |
Get filenames for a particular split. | def _subset_filenames(dl_paths, split):
"""Get filenames for a particular split."""
assert isinstance(dl_paths, dict), dl_paths
# Get filenames for a split.
if split == tfds.Split.TRAIN:
urls = _get_url_hashes(dl_paths['train_urls'])
elif split == tfds.Split.VALIDATION:
urls = _get_url_hashes(dl_paths... |
Get abstract (highlights) and article from a story file path. | def _get_art_abs(story_file):
"""Get abstract (highlights) and article from a story file path."""
# Based on https://github.com/abisee/cnn-dailymail/blob/master/
# make_datafiles.py
lines = _read_text_file(story_file)
# Lowercase everything
lines = [line.lower() for line in lines]
# Put periods on ... |
Export the results. | def exporter(directory, method, datasets):
"""Export the results."""
if method.lower() == 'json':
# Convert json_dict to a JSON styled string
json_string = json.dumps(datasets, indent=4)
savefile = open('{}/exported.json'.format(directory), 'w+')
savefile.write(json_string)
... |
Query archive.org. | def time_machine(host, mode):
"""Query archive.org."""
now = datetime.datetime.now()
to = str(now.year) + str(now.day) + str(now.month)
if now.month > 6:
fro = str(now.year) + str(now.day) + str(now.month - 6)
else:
fro = str(now.year - 1) + str(now.day) + str(now.month + 6)
url = "htt... |
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