added pali inference

74e8f2f over 2 years ago

2.8 kB

# Copyright 2024 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Preprocessing builder."""

from absl import logging
from big_vision.pp import registry
import tensorflow as tf


def get_preprocess_fn(pp_pipeline, log_data=True):
  """Transform an input string into the preprocessing function.
  The minilanguage is as follows:
    fn1|fn2(arg, arg2,...)|...
  And describes the successive application of the various `fn`s to the input,
  where each function can optionally have one or more arguments, which are
  either positional or key/value, as dictated by the `fn`.
  The output preprocessing function expects a dictionary as input. This
  dictionary should have a key "image" that corresponds to a 3D tensor
  (height x width x channel).
  Args:
    pp_pipeline: A string describing the pre-processing pipeline. If empty or
      None, no preprocessing will be executed.
    log_data: Whether to log the data before and after preprocessing. Can also
      be a string to show in the log for debugging, for example dataset name.
  Returns:
    preprocessing function.
  Raises:
    ValueError: if preprocessing function name is unknown
  """

  names, ops = [], []
  if pp_pipeline:
    for op_spec in pp_pipeline.split("|"):
      if not op_spec: continue  # Skip empty section instead of error.
      try:
        ops.append(registry.Registry.lookup(f"preprocess_ops.{op_spec}")())
        names.append(registry.parse_name(op_spec)[0])
      except SyntaxError as err:
        raise ValueError(f"Syntax error on: {op_spec}") from err

  def _preprocess_fn(data):
    """The preprocessing function that is returned."""
    nonlocal log_data

    # Apply all the individual steps in sequence.
    if log_data:
      logging.info("Data before pre-processing (%s):\n%s", log_data, data)
    for name, op in zip(names, ops):
      with tf.name_scope(name):
        data = op(data)

    # Validate input
    if not isinstance(data, dict):
      raise ValueError("Argument `data` must be a dictionary, "
                       "not %s" % str(type(data)))

    if log_data:
      logging.info("Data after pre-processing (%s):\n%s", log_data, data)
    log_data = False  # For eager&pygrain: only log first one of each pipeline.
    return data

  return _preprocess_fn

	# Copyright 2024 Big Vision Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	"""Preprocessing builder."""

	from absl import logging
	from big_vision.pp import registry
	import tensorflow as tf


	def get_preprocess_fn(pp_pipeline, log_data=True):
	"""Transform an input string into the preprocessing function.

	The minilanguage is as follows:

	fn1\|fn2(arg, arg2,...)\|...

	And describes the successive application of the various `fn`s to the input,
	where each function can optionally have one or more arguments, which are
	either positional or key/value, as dictated by the `fn`.

	The output preprocessing function expects a dictionary as input. This
	dictionary should have a key "image" that corresponds to a 3D tensor
	(height x width x channel).

	Args:
	pp_pipeline: A string describing the pre-processing pipeline. If empty or
	None, no preprocessing will be executed.
	log_data: Whether to log the data before and after preprocessing. Can also
	be a string to show in the log for debugging, for example dataset name.

	Returns:
	preprocessing function.

	Raises:
	ValueError: if preprocessing function name is unknown
	"""

	names, ops = [], []
	if pp_pipeline:
	for op_spec in pp_pipeline.split("\|"):
	if not op_spec: continue # Skip empty section instead of error.
	try:
	ops.append(registry.Registry.lookup(f"preprocess_ops.{op_spec}")())
	names.append(registry.parse_name(op_spec)[0])
	except SyntaxError as err:
	raise ValueError(f"Syntax error on: {op_spec}") from err

	def _preprocess_fn(data):
	"""The preprocessing function that is returned."""
	nonlocal log_data

	# Apply all the individual steps in sequence.
	if log_data:
	logging.info("Data before pre-processing (%s):\n%s", log_data, data)
	for name, op in zip(names, ops):
	with tf.name_scope(name):
	data = op(data)

	# Validate input
	if not isinstance(data, dict):
	raise ValueError("Argument `data` must be a dictionary, "
	"not %s" % str(type(data)))

	if log_data:
	logging.info("Data after pre-processing (%s):\n%s", log_data, data)
	log_data = False # For eager&pygrain: only log first one of each pipeline.
	return data

	return _preprocess_fn