def testCaptureHashTable(self):
# NOTE(mrry): We must use the V2 variants of `HashTable`
# etc. because these produce a `tf.resource`-typed output that is
# compatible with the in-graph function implementation.
default_val = -1
keys = constant_op.constant(["brain", "salad", "surgery"])
values = constant_op.constant([0, 1, 2], dtypes.int64)
table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), default_val)
input_sentences = dataset_ops.Dataset.from_tensor_slices(
["brain brain tank salad surgery", "surgery brain"])
iterator = dataset_ops.make_initializable_iterator(
input_sentences.map(
lambda x: string_ops.string_split([x]).values).map(
table.lookup))
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(table.initializer)
sess.run(init_op)
sess.run(get_next)
sess.run(get_next)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testCaptureHashTableInSharedIterator(self):
worker, _ = test_util.create_local_cluster(1, 1)
# NOTE(mrry): We must use the V2 variants of `HashTable`
# etc. because these produce a `tf.resource`-typed output that is
# compatible with the in-graph function implementation.
default_val = -1
keys = constant_op.constant(["brain", "salad", "surgery"])
values = constant_op.constant([0, 1, 2], dtypes.int64)
table = lookup_ops.HashTable(lookup_ops.KeyValueTensorInitializer(
keys, values),
default_val,
shared_name="shared_table")
input_sentences = dataset_ops.Dataset.from_tensor_slices(
["brain brain tank salad surgery", "surgery brain"])
iterator = (input_sentences.map(
lambda x: string_ops.string_split([x]).values).map(
table.lookup).make_initializable_iterator(
shared_name="shared_iterator"))
init_op = iterator.initializer
get_next = iterator.get_next()
with session.Session(worker[0].target) as sess:
sess.run(table.init)
sess.run(init_op)
self.assertAllEqual([0, 0, -1, 1, 2], sess.run(get_next))
with session.Session(worker[0].target) as sess:
self.assertAllEqual([2, 0], sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def test_filter_input_subsample_vocab(self):
"""Tests input filtering based on vocab subsampling."""
# The outputs are non-deterministic, so set random seed to help ensure
# that the outputs remain constant for testing.
random_seed.set_random_seed(42)
input_tensor = tf.constant([
# keep_prob = (sqrt(30/(0.05*100)) + 1) * (0.05*100/30) = 0.57.
b"the",
b"answer", # Not in vocab. (Always discarded)
b"to", # keep_prob = 0.75.
b"life", # keep_prob > 1. (Always kept)
b"and", # keep_prob = 0.48.
b"universe" # Below vocab threshold of 3. (Always discarded)
])
keys = tf.constant([b"and", b"life", b"the", b"to", b"universe"])
values = tf.constant([40, 8, 30, 20, 2], tf.dtypes.int64)
vocab_freq_table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), -1)
output = skip_gram_ops._filter_input(
input_tensor=input_tensor,
vocab_freq_table=vocab_freq_table,
vocab_min_count=3,
vocab_subsampling=0.05,
corpus_size=tf.math.reduce_sum(values),
seed=9)
self.assertAllEqual([b"the", b"to", b"life", b"and"], output)
def _create_table(vocab, num_oov=1):
init = lookup_ops.KeyValueTensorInitializer(
vocab,
math_ops.range(
array_ops.size(vocab, out_type=dtypes.int64), dtype=dtypes.int64),
key_dtype=dtypes.string,
value_dtype=dtypes.int64)
return lookup_ops.StaticVocabularyTableV1(
init, num_oov, lookup_key_dtype=dtypes.string)
def test_skip_gram_sample_errors(self):
"""Tests various errors raised by skip_gram_sample()."""
input_tensor = constant_op.constant([b"the", b"quick", b"brown"])
invalid_skips = (
# min_skips and max_skips must be >= 0.
(-1, 2),
(1, -2),
# min_skips must be <= max_skips.
(2, 1))
for min_skips, max_skips in invalid_skips:
with self.assertRaises(errors.InvalidArgumentError):
text.skip_gram_sample(input_tensor,
min_skips=min_skips,
max_skips=max_skips)
# Eager tensor must be rank 1
with self.assertRaises(errors.InvalidArgumentError):
invalid_tensor = constant_op.constant([[b"the"], [b"quick"],
[b"brown"]])
text.skip_gram_sample(invalid_tensor)
# vocab_freq_table must be provided if vocab_min_count,
# vocab_subsampling, or corpus_size is specified.
dummy_input = constant_op.constant([""])
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=None,
vocab_min_count=1)
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=None,
vocab_subsampling=1e-5)
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=None,
corpus_size=100)
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=None,
vocab_subsampling=1e-5,
corpus_size=100)
# vocab_subsampling and corpus_size must both be present or absent.
dummy_table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer([b"foo"], [10]), -1)
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=dummy_table,
vocab_subsampling=None,
corpus_size=100)
with self.assertRaises(ValueError):
text.skip_gram_sample(dummy_input,
vocab_freq_table=dummy_table,
vocab_subsampling=1e-5,
corpus_size=None)
def get_graph_def():
with ops.Graph().as_default() as g:
x = constant_op.constant([2, 9], name="x")
keys = constant_op.constant([1, 2], name="keys")
values = constant_op.constant([3, 4], name="values")
default = constant_op.constant(-1, name="default")
table = lookup_ops.StaticHashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), default)
_ = table.lookup(x)
return g.as_graph_def()
def testDistributeLookupTable(self):
cluster = data_service_test_base.TestCluster(num_workers=1)
keys_tensor = constant_op.constant([1, 2])
vals_tensor = constant_op.constant([11, 12])
table = lookup_ops.StaticHashTable(
lookup_ops.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1)
ds = dataset_ops.Dataset.range(3, output_type=dtypes.int32)
ds = ds.map(table.lookup)
ds = self.make_distributed_dataset(ds, cluster)
self.assertDatasetProduces(ds, [-1, 11, 12])
def test_filter_input_filter_vocab(self):
"""
Tests input filtering based on vocab frequency table and thresholds.
"""
input_tensor = tf.constant(
[b"the", b"answer", b"to", b"life", b"and", b"universe"])
keys = tf.constant([b"and", b"life", b"the", b"to",
b"universe"])
values = tf.constant([0, 1, 2, 3, 4], tf.dtypes.int64)
vocab_freq_table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), -1)
# No vocab_freq_table specified - output should be the same as input
no_table_output = skip_gram_ops._filter_input(
input_tensor=input_tensor,
vocab_freq_table=None,
vocab_min_count=None,
vocab_subsampling=None,
corpus_size=None,
seed=None)
self.assertAllEqual(input_tensor, no_table_output)
# vocab_freq_table specified, but no vocab_min_count - output should
# have filtered out tokens not in the table (b"answer").
table_output = skip_gram_ops._filter_input(
input_tensor=input_tensor,
vocab_freq_table=vocab_freq_table,
vocab_min_count=None,
vocab_subsampling=None,
corpus_size=None,
seed=None)
self.assertAllEqual([b"the", b"to", b"life", b"and", b"universe"],
table_output)
# vocab_freq_table and vocab_min_count specified - output should have
# filtered out tokens whose frequencies are below the threshold
# (b"and": 0, b"life": 1).
threshold_output = skip_gram_ops._filter_input(
input_tensor=input_tensor,
vocab_freq_table=vocab_freq_table,
vocab_min_count=2,
vocab_subsampling=None,
corpus_size=None,
seed=None)
self.assertAllEqual([b"the", b"to", b"universe"], threshold_output)
def make_initializer(self, init_source, vals):
if init_source == "textfile":
file = os.path.join(self.get_temp_dir(), "text_file_initializer")
with open(file, "w") as f:
f.write("\n".join(str(v) for v in vals) + "\n")
return lookup_ops.TextFileInitializer(
filename=file,
key_dtype=dtypes.int64,
key_index=lookup_ops.TextFileIndex.LINE_NUMBER,
value_dtype=dtypes.int64,
value_index=lookup_ops.TextFileIndex.WHOLE_LINE)
elif init_source == "keyvaluetensor":
keys_tensor = constant_op.constant(
list(range(len(vals))), dtype=dtypes.int64)
vals_tensor = constant_op.constant(vals)
return lookup_ops.KeyValueTensorInitializer(keys_tensor, vals_tensor)
else:
raise ValueError("Unrecognized init_source: " + init_source)
def read_tf_vocab(input_file, UNK):
"""Read vocabulary and return a tf hashtable"""
if input_file is None:
return None
keys, values = [], []
fin = tf.io.gfile.GFile(input_file, 'r')
for line in fin:
word = split(strip(line))[0]
keys.append(word)
values.append(len(values))
fin.close()
UNK_ID = keys.index(UNK)
initializer = lookup_ops.KeyValueTensorInitializer(tf.constant(keys),
tf.constant(values))
vocab_table = lookup_ops.HashTable(initializer, UNK_ID)
return initializer, vocab_table
def testStaticHashTableDatasetFnHostTrainingLoop(self, enable_packed_var):
self._dataset_fn_tracing_count = 0
strategy = get_tpu_strategy(enable_packed_var)
with strategy.scope():
vals = [0, 1, 2]
keys_tensor = constant_op.constant(
list(range(len(vals))), dtype=dtypes.int64)
vals_tensor = constant_op.constant(vals)
initializer = lookup_ops.KeyValueTensorInitializer(
keys_tensor, vals_tensor)
per_worker_table = lookup_ops.StaticHashTable(
initializer, default_value=-1)
@def_function.function
def dataset_fn(input_context):
tensor = constant_op.constant([0, 1, 3], dtype=dtypes.int64)
global_batch_size = 2
batch_size = input_context.get_per_replica_batch_size(global_batch_size)
dataset = dataset_ops.Dataset.from_tensors(tensor).repeat().batch(
batch_size, drop_remainder=True)
dataset = dataset.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
dataset = dataset.prefetch(2) # This prefetches 2 batches per device.
dataset = dataset.map(per_worker_table.lookup)
self._dataset_fn_tracing_count += 1
return dataset
dist_iterator = iter(
strategy.experimental_distribute_datasets_from_function(dataset_fn))
@def_function.function
def step_fn(inputs):
# inputs should be [0, 1, -1]
return math_ops.reduce_sum(inputs)
def train_steps(iterator, steps):
for _ in math_ops.range(steps):
strategy.run(step_fn, args=(next(iterator),))
train_steps(dist_iterator, steps=5)
self.assertEqual(self._dataset_fn_tracing_count, 1)
def __init__(self, init_source, filepath):
vals = [0, 1, 2]
if init_source == "textfile":
with open(filepath, "w") as f:
f.write("\n".join(str(v) for v in vals) + "\n")
self.initializer = lookup_ops.TextFileInitializer(
filepath, dtypes.int64, lookup_ops.TextFileIndex.LINE_NUMBER,
dtypes.int64, lookup_ops.TextFileIndex.WHOLE_LINE)
else:
keys_tensor = constant_op.constant(
list(range(len(vals))), dtype=dtypes.int64)
vals_tensor = constant_op.constant(vals)
self.initializer = lookup_ops.KeyValueTensorInitializer(
keys_tensor, vals_tensor)
self.table = lookup_ops.StaticHashTable(
self.initializer, default_value=-2)
def testDetokenizeFailsForSparseVocab(self):
vocab = ["a", "##b", "##c"]
ids = [0, 10, 20]
init = lookup_ops.KeyValueTensorInitializer(vocab,
ids,
key_dtype=dtypes.string,
value_dtype=dtypes.int64)
table = lookup_ops.StaticVocabularyTableV1(
init, num_oov_buckets=1, lookup_key_dtype=dtypes.string)
self.evaluate(table.initializer)
tokenizer = WordpieceTokenizer(table)
words = ragged_factory_ops.constant([["abb", "abc"], ["abcbc"]])
subwords_ids = tokenizer.tokenize(words)
with self.assertRaisesRegex(errors_impl.InvalidArgumentError,
"detokenize.*?dense on the interval"):
result = tokenizer.detokenize(subwords_ids)
self.evaluate(result)
def read_tf_vocab(input_file, UNK):
"""Read vocabulary and return a tf hashtable"""
if input_file is None:
return None
keys, values = [], []
if input_file.endswith('.gz'):
f = tf.gfile.Open(input_file, 'r')
fin = gzip.GzipFile(fileobj=f)
else:
fin = tf.gfile.Open(input_file, 'r')
for line in fin:
word = split(strip(line))[0]
keys.append(word)
values.append(len(values))
fin.close()
UNK_ID = keys.index(UNK)
vocab_table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(tf.constant(keys),
tf.constant(values)), UNK_ID)
return vocab_table
def testDistributeLookupTable(self, init_from_file):
cluster = data_service_test_base.TestCluster(num_workers=1)
if init_from_file:
file = os.path.join(self.get_temp_dir(), "distribute_lookup_table")
with open(file, "w") as f:
f.write("10\n11\n")
initializer = lookup_ops.TextFileInitializer(
file, dtypes.int64, lookup_ops.TextFileIndex.LINE_NUMBER,
dtypes.int64, lookup_ops.TextFileIndex.WHOLE_LINE)
else:
keys_tensor = constant_op.constant([0, 1], dtype=dtypes.int64)
vals_tensor = constant_op.constant([10, 11])
initializer = lookup_ops.KeyValueTensorInitializer(
keys_tensor, vals_tensor)
table = lookup_ops.StaticHashTable(initializer, -1)
ds = dataset_ops.Dataset.range(3)
ds = ds.map(table.lookup)
ds = self.make_distributed_dataset(ds, cluster)
self.evaluate(lookup_ops.tables_initializer())
self.assertDatasetProduces(ds, [10, 11, -1],
requires_initialization=True)
def _make_model_with_tables(self):
default_val = -1
keys = constant_op.constant(["brain", "salad", "surgery"])
values = constant_op.constant([0, 1, 2], dtypes.int64)
table1_initializer = lookup_ops.KeyValueTensorInitializer(keys, values)
table1 = lookup_ops.HashTable(table1_initializer, default_val)
table2_file = self._make_asset("test\nfoo\nbrain\n")
table2_initializer = lookup_ops.TextFileIdTableInitializer(table2_file)
table2 = lookup_ops.HashTable(table2_initializer, default_val)
def _make_lookup_function(table):
signature = [tensor_spec.TensorSpec(None, dtypes.string)]
return def_function.function(input_signature=signature)(
lambda x: table.lookup(x)) # pylint: disable=unnecessary-lambda
root = tracking.AutoTrackable()
root.table1 = table1
root.lookup1 = _make_lookup_function(table1)
root.table2 = table2
root.lookup2 = _make_lookup_function(table2)
return root
def read_tf_vocab_inverse(input_file, UNK):
"""Read vocabulary (token->id) and return a tf hashtable (id->token)"""
if input_file is None:
return None
keys, values = [], []
if input_file.endswith('.gz'):
f = tf.io.gfile.GFile(input_file, 'r')
fin = gzip.GzipFile(fileobj=f)
else:
fin = tf.io.gfile.GFile(input_file, 'r')
for line in fin:
word = split(strip(line))[0]
keys.append(len(keys))
values.append(word)
fin.close()
initializer = lookup_ops.KeyValueTensorInitializer(tf.constant(keys),
tf.constant(values))
vocab_table = lookup_ops.HashTable(initializer, UNK)
return initializer, vocab_table
def testLookupTableGraphSerialization(self, init_from_file):
if init_from_file:
file = os.path.join(self.get_temp_dir(),
"lookup_table_graph_serialize")
with open(file, "w") as f:
f.write("10\n11\n")
initializer = lookup_ops.TextFileInitializer(
file, dtypes.int64, lookup_ops.TextFileIndex.LINE_NUMBER,
dtypes.int64, lookup_ops.TextFileIndex.WHOLE_LINE)
else:
keys_tensor = constant_op.constant([0, 1], dtype=dtypes.int64)
vals_tensor = constant_op.constant([10, 11])
initializer = lookup_ops.KeyValueTensorInitializer(
keys_tensor, vals_tensor)
table = lookup_ops.StaticHashTable(initializer, -1)
dataset = dataset_ops.Dataset.range(3)
dataset = dataset.map(table.lookup)
self.evaluate(lookup_ops.tables_initializer())
round_tripped = self.graphRoundTrip(dataset)
del table
del dataset
self.assertDatasetProduces(round_tripped, [10, 11, -1],
requires_initialization=True)
def collecting_function(x):
_ = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer([], []), 0.0, name="t1")
return x
def keyValueTensorInitializer(self, vals):
keys_tensor = constant_op.constant(
list(range(len(vals))), dtype=dtypes.int64)
vals_tensor = constant_op.constant(vals)
return lookup_ops.KeyValueTensorInitializer(keys_tensor, vals_tensor)
def create_infer_model(model_creator, hparams, scope=None, extra_args=None):
"""Create inference model."""
graph = tf.Graph()
src_vocab_file = hparams.src_vocab_file
tgt_vocab_file = hparams.tgt_vocab_file
# REvo added
tgt_table = codecs.open(src_vocab_file, 'r').readlines()
tmp_ids = []
tmp_words = []
for i in range(len(tgt_table)):
tmp_ids.append(i)
tmp_words.append(tgt_table[i].strip())
with graph.as_default(), tf.container(scope or "infer"):
# Constant vocab table
src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
src_vocab_file, tgt_vocab_file, hparams.share_vocab)
# reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(
# tgt_vocab_file, default_value=vocab_utils.UNK, name="reverse_table")
# added
vals = tf.constant(tmp_words, dtype=tf.string)
keys = tf.constant(tmp_ids, dtype=tf.int64)
reverse_tgt_vocab_table = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, vals),
"<unk>",
name="reverse_table")
#
# debug
print("SRC:", src_vocab_table)
print("SRC type:", type(src_vocab_table))
#
src_placeholder = tf.placeholder(shape=[None],
dtype=tf.string,
name="src_place")
# batch_size_placeholder = tf.placeholder(shape=[], dtype=tf.int64, name="batch_place")
batch_size_placeholder = tf.constant(1,
dtype=tf.int64,
name="batch_place")
src_dataset = tf.data.Dataset.from_tensor_slices(src_placeholder)
iterator = iterator_utils.get_infer_iterator(
src_dataset,
src_vocab_table,
batch_size=batch_size_placeholder,
eos=hparams.eos,
src_max_len=hparams.src_max_len_infer)
model = model_creator(
hparams,
iterator=iterator,
mode=tf.contrib.learn.ModeKeys.INFER,
source_vocab_table=src_vocab_table,
target_vocab_table=tgt_vocab_table,
reverse_target_vocab_table=reverse_tgt_vocab_table,
scope=scope,
extra_args=extra_args)
# Debug
# with tf.Session() as sess:
# # init
# sess.run(
# iterator.initializer,
# feed_dict={
# src_placeholder: iterator.infer_data,
# batch_size_placeholder: 64
# })
# value = sess.run(iterator.source)
# print ("value:", value)
# sys.exit()
return InferModel(graph=graph,
model=model,
src_placeholder=src_placeholder,
batch_size_placeholder=batch_size_placeholder,
iterator=iterator,
insert_op=(src_vocab_table.init, tgt_vocab_table.init,
reverse_tgt_vocab_table.init))
def build_dataset(file_pattern,
input_config,
batch_size,
include_labels=True,
reverse_time_series_prob=0,
shuffle_filenames=False,
shuffle_values_buffer=0,
repeat=1,
use_tpu=False):
"""Builds an input pipeline that reads a dataset from sharded TFRecord files.
Args:
file_pattern: File pattern matching input TFRecord files, e.g.
"/tmp/train-?????-of-00100". May also be a comma-separated list of file
patterns.
input_config: ConfigDict containing feature and label specifications.
batch_size: The number of examples per batch.
include_labels: Whether to read labels from the input files.
reverse_time_series_prob: If > 0, the time series features will be randomly
reversed with this probability. Within a given example, either all time
series features will be reversed, or none will be reversed.
shuffle_filenames: Whether to shuffle the order of TFRecord files between
epochs.
shuffle_values_buffer: If > 0, shuffle examples using a buffer of this size.
repeat: The number of times to repeat the dataset. If None or -1 the dataset
will repeat indefinitely.
use_tpu: Whether to build the dataset for TPU.
Raises:
ValueError: If an input file pattern does not match any files, or if the
label IDs in input_config.label_map are not contiguous integers starting
at 0.
Returns:
A tf.data.Dataset object.
"""
file_patterns = file_pattern.split(",")
filenames = []
for p in file_patterns:
matches = tf.io.gfile.glob(p)
if not matches:
raise ValueError("Found no input files matching %s" % p)
filenames.extend(matches)
tf.compat.v1.logging.info("Building input pipeline from %d files matching patterns: %s",
len(filenames), file_patterns)
if include_labels:
# Ensure that the label ids are contiguous integers starting at 0.
label_ids = set(input_config.label_map.values())
if label_ids != set(range(len(label_ids))):
raise ValueError(
"Label IDs must be contiguous integers starting at 0. Got: %s" %
label_ids)
# Create a HashTable mapping label strings to integer ids.
table_initializer = lookup_ops.KeyValueTensorInitializer(
keys=list(input_config.label_map.keys()),
values=list(input_config.label_map.values()),
key_dtype=tf.string,
value_dtype=tf.int32)
label_to_id = lookup_ops.HashTable(
table_initializer, default_value=-1)
def _example_parser(serialized_example):
"""Parses a single tf.Example into feature and label tensors."""
# Set specifications for parsing the features.
data_fields = {
feature_name: tf.io.FixedLenFeature([feature.length], tf.float32)
for feature_name, feature in input_config.features.items()
}
if include_labels:
data_fields[input_config.label_feature] = tf.io.FixedLenFeature([],
tf.string)
# Parse the features.
parsed_features = tf.io.parse_single_example(
serialized=serialized_example, features=data_fields)
if reverse_time_series_prob > 0:
# Randomly reverse time series features with probability
# reverse_time_series_prob.
should_reverse = tf.less(
tf.random.uniform([], 0, 1),
reverse_time_series_prob,
name="should_reverse")
# Reorganize outputs.
output = {}
for feature_name, value in parsed_features.items():
if include_labels and feature_name == input_config.label_feature:
label_id = label_to_id.lookup(value)
# Ensure that the label_id is nonnegative to verify a successful hash
# map lookup.
assert_known_label = tf.Assert(
tf.greater_equal(label_id, tf.cast(0, dtype=tf.int32)),
["Unknown label string:", value])
with tf.control_dependencies([assert_known_label]):
label_id = tf.identity(label_id)
# We use the plural name "labels" in the output due to batching.
output["labels"] = label_id
elif input_config.features[feature_name].is_time_series:
# Possibly reverse.
if reverse_time_series_prob > 0:
# pylint:disable=cell-var-from-loop
value = tf.cond(pred=should_reverse, true_fn=lambda: tf.reverse(value, axis=[0]),
false_fn=lambda: tf.identity(value))
# pylint:enable=cell-var-from-loop
if "time_series_features" not in output:
output["time_series_features"] = {}
output["time_series_features"][feature_name] = value
else:
if "aux_features" not in output:
output["aux_features"] = {}
output["aux_features"][feature_name] = value
return output
# Create a string dataset of filenames, and possibly shuffle.
filename_dataset = tf.data.Dataset.from_tensor_slices(filenames)
if len(filenames) > 1 and shuffle_filenames:
filename_dataset = filename_dataset.shuffle(len(filenames))
# Read serialized Example protos.
dataset = filename_dataset.flat_map(tf.data.TFRecordDataset)
# Possibly shuffle. Note that we shuffle before repeat(), so we only shuffle
# elements among each "epoch" of data, and not across epochs of data.
if shuffle_values_buffer > 0:
dataset = dataset.shuffle(shuffle_values_buffer)
# Repeat.
if repeat != 1:
dataset = dataset.repeat(repeat)
# Map the parser over the dataset.
dataset = dataset.map(_example_parser, num_parallel_calls=4)
# Batch results by up to batch_size.
dataset = dataset.batch(batch_size)
if repeat == -1 or repeat is None:
# The dataset repeats infinitely before batching, so each batch has the
# maximum number of elements.
dataset = set_batch_size(dataset, batch_size)
elif use_tpu:
# TPU requires all dimensions to be fixed. Since the dataset does not repeat
# infinitely before batching, the final batch may have fewer than batch_size
# elements. Therefore we pad to ensure that the final batch has batch_size
# elements.
dataset = pad_dataset_to_batch_size(dataset, batch_size)
# Prefetch a few batches.
dataset = dataset.prefetch(max(1, int(256 / batch_size)))
return dataset
