def _ParseProcessor(processor):
"""Parses python callable `processor` into a TF concrete function."""
output_tmpl = py_utils.NestedMap()
@tf.function(autograph=False)
def _FlatOutputProcessor(source_id, record):
"""Returns a flattened list of 'processor(inputs)'."""
processor_spec = tf_inspect.getargspec(processor)
tf.logging.debug('GenericInput.processor.argspec=%s', processor_spec)
processor_args = set(processor_spec.args) - set(['self'])
if len(processor_args) == 1:
output, bucketing_key = processor(record)
elif processor_args == set(['source_id', 'record']):
output, bucketing_key = processor(source_id=source_id,
record=record)
else:
raise ValueError(
'GenericInput: processor should take either a single arg '
'or two args named as "source_id" and "record". '
'Actual: %s' % processor_args)
if isinstance(output, list):
assert output
assert all(isinstance(x, tf.Tensor)
for x in output), '{}'.format(output)
else:
assert isinstance(output, py_utils.NestedMap), '{}'.format(output)
assert output
assert all(isinstance(x, tf.Tensor)
for x in output.Flatten()), '{}'.format(
output.DebugString())
bucketing_key = tf.cast(bucketing_key, tf.int32)
tf.logging.debug('Processor outputs=%s bucketing_key=%s', output,
bucketing_key)
output_tmpl.out_values = output
flat_output_tmpl = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_output_tmpl)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
py_utils.GetExtraInputs(), py_utils.GetExtraArgs(),
py_utils.GetExtraVars())
assert not py_utils.GetExtraArgs(), (
'fns {} is not pure: extra_args={}'.format(
processor, py_utils.GetExtraArgs()))
return flat_output_tmpl + [bucketing_key]
with py_utils.GlobalStepContext(None):
# Hide global_step tensor from being captured by _FlatOutputProcessor.
proc_fn = _FlatOutputProcessor.get_concrete_function(
tf.TensorSpec([], tf.int32), tf.TensorSpec([], tf.string))
out_types = [
tf.DType(a.type) for a in proc_fn.function_def.signature.output_arg
]
assert out_types[-1] == tf.int32, ('%s is not expected.' % out_types[-1])
return proc_fn, out_types, output_tmpl
class FakeTF2ImageModule(tf.train.Checkpoint):
def __init__(self, output_dim=768):
# Counts the number of times the layer has been run with training=True.
self.counter = tf.Variable(initial_value=0,
dtype=tf.int32,
name='counter',
use_resource=True)
self.output_dim = output_dim
# "Reusable" SavedModel metadata expected by KerasLayer.
self.variables = [self.counter]
self.trainable_variables = []
self.regularization_losses = []
@tf.function(input_signature=[
tf.TensorSpec(shape=[None, None, None, None], dtype=tf.float32),
tf.TensorSpec(shape=[], dtype=tf.bool)
])
def __call__(self, images, training=False):
if training:
self.counter.assign_add(1)
return tf.ones([tf.shape(images)[0], self.output_dim])
def testPopulatesFeaturesInMetadata(self):
spec = dataset_spec.TFRecordDatasetSpec(
# Create a file so Dataset.list_files() won't complain if the test is
# ever switched to eager.
split_paths={'train': self.create_tempfile().full_path},
schema={
'quizybuck':
tf.io.FixedLenFeature([42], tf.int64),
'x':
tf.io.FixedLenSequenceFeature([3],
tf.float32,
allow_missing=True)
},
label_fn=None)
expected_features = {
'quizybuck': tf.TensorSpec([42], tf.int64),
'x': tf.TensorSpec([None, 3], tf.float32)
}
self.assertSameStructure(spec.meta.features, expected_features)
# Check that these match the features in the actual `tf.data.Dataset`.
self.assertSameStructure(spec.Read('train').element_spec, expected_features)
def GenericInput(processor, **kwargs):
"""Builds a generic input pipeline.
Example usage::
def ParseRecord(record):
# Given a tf.string record, return a (NestedMap, bucketing key) pair.
feature_map = ...
features = tf.io.parse_single_example(record, feature_map)
# Each example is represented by a NestedMap of tensors (without a
# batch dimension).
example = py_utils.NestedMap(field1=..., field2=...)
# bucketing_key is a scalar convertible to tf.int32.
# Use 1 if all examples are of the same size.
bucketing_key = 1
return example, bucketing_key
input_batch, bucket_keys = GenericInput(ParseRecord, file_pattern=..., ...)
# input_batch is a NestedMap of tensors, where dim 0 of each tensor
# represents the batch dimension.
input_batch.field1 = ...
ParseRecord can also take both 'source_id' and 'record' as inputs (the arg
names must be exactly 'source_id' and 'record'):
def ParseRecord(source_id, record):
# Given a tf.int32 source_id and a tf.string record, return a (NestedMap,
# bucketing key) pair.
example = py_utils.NestedMap(source_id=source_id, ...)
...
return example, bucketing_key
input_batch, bucket_keys = GenericInput(ParseRecord, file_pattern=..., ...)
Args:
processor: a function that takes either a tf.string record or a
(source_id: tf.int32, record: tf.string) pair as input and returns a tuple
(output, bucketing_key). `output` must be a NestedMap or a list of tensors
representing an example. `bucketing_key` must be a scalar convertible to
a tf.int32 tensor that represents the bucketing key (e.g., sequence
length for sequence inputs). If `bucketing_key` is a negative number,
the record is dropped.
**kwargs: additional keyword args for x_ops.generic_input.
Returns:
A tuple of (outputs, bucket_keys):
- outputs: a NestedMap or a list of tensors, similar to `processor`'s
return, except every tensor will have an additional dimension 0 that
represents the batch dimension.
- bucket_keys: a tf.int32 vector.
"""
output_tmpl = py_utils.NestedMap()
@tf.function(autograph=False)
def _FlatOutputProcessor(source_id, record):
"""Returns a flattened list of 'processor(inputs)'."""
processor_spec = tf_inspect.getargspec(processor)
tf.logging.debug('GenericInput.processor.argspec=%s', processor_spec)
processor_args = set(processor_spec.args) - set(['self'])
if len(processor_args) == 1:
output, bucketing_key = processor(record)
elif processor_args == set(['source_id', 'record']):
output, bucketing_key = processor(source_id=source_id,
record=record)
else:
raise ValueError(
'GenericInput: processor should take either a single arg '
'or two args named as "source_id" and "record". '
'Actual: %s' % processor_args)
if isinstance(output, list):
assert output
assert all(isinstance(x, tf.Tensor)
for x in output), '{}'.format(output)
else:
assert isinstance(output, py_utils.NestedMap), '{}'.format(output)
assert output
assert all(isinstance(x, tf.Tensor)
for x in output.Flatten()), '{}'.format(
output.DebugString())
bucketing_key = tf.cast(bucketing_key, tf.int32)
tf.logging.debug('Processor outputs=%s bucketing_key=%s', output,
bucketing_key)
output_tmpl.out_values = output
flat_output_tmpl = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_output_tmpl)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
py_utils.GetExtraInputs(), py_utils.GetExtraArgs(),
py_utils.GetExtraVars())
assert not py_utils.GetExtraArgs(), (
'fns {} is not pure: extra_args={}'.format(
processor, py_utils.GetExtraArgs()))
return flat_output_tmpl + [bucketing_key]
proc_fn = _FlatOutputProcessor.get_concrete_function(
tf.TensorSpec([], tf.int32), tf.TensorSpec([], tf.string))
out_types = [
tf.DType(a.type) for a in proc_fn.function_def.signature.output_arg
]
assert out_types[-1] == tf.int32, ('%s is not expected.' % out_types[-1])
flat_outputs, bucket_keys = ops.gen_x_ops.generic_input(
processor=proc_fn, out_types=out_types[:-1], **kwargs)
tf.logging.debug('x_ops.generic_input flat_outputs=%s', flat_outputs)
# Pack flat_outputs to outputs.
outputs = output_tmpl.Pack(flat_outputs).out_values
tf.logging.debug('x_ops.generic_input outputs=%s', outputs)
return outputs, bucket_keys
def dump_spellings():
words = []
with open(FLAGS.in_words_txt, "r") as words_fh:
words = words_fh.read().lower().splitlines()
# if "<unk>" not in words:
# words.append("<unk>")
# We add 2 to account for <s> and (optional) </s> tokens.
longest_word_length = max(len(word) for word in words) + 2
print("GALV:", longest_word_length)
with open(FLAGS.in_units_txt, "r") as units_fh:
vocab_tokens = [line.rstrip("\n") for line in units_fh.readlines()]
print("GALV:", vocab_tokens)
@tf.function(
input_signature=[tf.TensorSpec(shape=[len(words)], dtype=tf.string)])
def tokenize_words(words_t):
padded_tokenized_t, _, paddings_t = str_to_vocab_tokens(
labels=words_t,
maxlen=longest_word_length,
append_eos=True,
pad_to_maxlen=True,
vocab_filepath=FLAGS.in_units_txt,
load_token_ids_from_vocab=False,
delimiter="",
)
# Either lengths or paddings are incorrect.
lengths_t = py_utils.LengthsFromPaddings(paddings_t)
ragged_tokenized_t = tf.RaggedTensor.from_tensor(padded_tokenized_t,
lengths=lengths_t)
# Drop start-of-sentence-token
ragged_tokenized_t = ragged_tokenized_t[:, 1:]
lengths_t -= 1
letters_t = vocab_id_to_token(
id=ragged_tokenized_t.flat_values,
vocab=vocab_tokens,
load_token_ids_from_vocab=False,
)
ragged_letters_t = tf.RaggedTensor.from_row_lengths(
letters_t, lengths_t)
# Is capatilizationt he problem?
return ragged_tokenized_t, ragged_letters_t
with tf.Session() as session:
spelling_numbers, spelling_letters = session.run(tokenize_words(words))
spelling_numbers = spelling_numbers.to_list()
spelling_letters = spelling_letters.to_list()
with open(FLAGS.out_spelling_txt,
"w") as spelling_fh, open(FLAGS.out_spelling_numbers_txt,
"w") as spelling_numbers_fh:
for word, numbers, letters in zip(words, spelling_numbers,
spelling_letters):
if isinstance(letters, list):
letters_str = " ".join([str(letter) for letter in word])
else:
letters_str = letters
numbers_str = " ".join([str(number) for number in numbers])
spelling_fh.write(f"{word} {letters_str}\n")
spelling_numbers_fh.write(f"{word} {numbers_str}\n")
spelling_fh.write("<unk> <unk>\n")
spelling_numbers_fh.write(f"<unk> {UNK_NUMBER}\n")
