Python tensorflow.python.lib.io.file_io.stat() Examples

The following are 5 code examples of tensorflow.python.lib.io.file_io.stat(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.lib.io.file_io , or try the search function .
Example #1
Source File: saver.py    From lambda-packs with MIT License 5 votes vote down vote up 
def get_checkpoint_mtimes(checkpoint_prefixes):
  """Returns the mtimes (modification timestamps) of the checkpoints.

  Globs for the checkpoints pointed to by `checkpoint_prefixes`.  If the files
  exist, collect their mtime.  Both V2 and V1 checkpoints are considered, in
  that priority.

  This is the recommended way to get the mtimes, since it takes into account
  the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefixes: a list of checkpoint paths, typically the results of
      `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
      sharded/non-sharded or V1/V2.
  Returns:
    A list of mtimes (in microseconds) of the found checkpoints.
  """
  mtimes = []

  def match_maybe_append(pathname):
    fnames = file_io.get_matching_files(pathname)
    if fnames:
      mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
      return True
    return False

  for checkpoint_prefix in checkpoint_prefixes:
    # Tries V2's metadata file first.
    pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                          saver_pb2.SaverDef.V2)
    if match_maybe_append(pathname):
      continue
    # Otherwise, tries V1, where the prefix is the complete pathname.
    match_maybe_append(checkpoint_prefix)

  return mtimes
Example #2
Source File: saver.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def get_checkpoint_mtimes(checkpoint_prefixes):
  """Returns the mtimes (modification timestamps) of the checkpoints.

  Globs for the checkpoints pointed to by `checkpoint_prefixes`.  If the files
  exist, collect their mtime.  Both V2 and V1 checkpoints are considered, in
  that priority.

  This is the recommended way to get the mtimes, since it takes into account
  the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefixes: a list of checkpoint paths, typically the results of
      `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
      sharded/non-sharded or V1/V2.
  Returns:
    A list of mtimes (in microseconds) of the found checkpoints.
  """
  mtimes = []

  def match_maybe_append(pathname):
    fnames = file_io.get_matching_files(pathname)
    if fnames:
      mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
      return True
    return False

  for checkpoint_prefix in checkpoint_prefixes:
    # Tries V2's metadata file first.
    pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                          saver_pb2.SaverDef.V2)
    if match_maybe_append(pathname):
      continue
    # Otherwise, tries V1, where the prefix is the complete pathname.
    match_maybe_append(checkpoint_prefix)

  return mtimes
Example #3
Source File: saver.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def get_checkpoint_mtimes(checkpoint_prefixes):
  """Returns the mtimes (modification timestamps) of the checkpoints.

  Globs for the checkpoints pointed to by `checkpoint_prefixes`.  If the files
  exist, collect their mtime.  Both V2 and V1 checkpoints are considered, in
  that priority.

  This is the recommended way to get the mtimes, since it takes into account
  the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefixes: a list of checkpoint paths, typically the results of
      `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
      sharded/non-sharded or V1/V2.
  Returns:
    A list of mtimes (in microseconds) of the found checkpoints.
  """
  mtimes = []

  def match_maybe_append(pathname):
    fnames = file_io.get_matching_files(pathname)
    if fnames:
      mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
      return True
    return False

  for checkpoint_prefix in checkpoint_prefixes:
    # Tries V2's metadata file first.
    pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                          saver_pb2.SaverDef.V2)
    if match_maybe_append(pathname):
      continue
    # Otherwise, tries V1, where the prefix is the complete pathname.
    match_maybe_append(checkpoint_prefix)

  return mtimes
Example #4
Source File: saver.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def get_checkpoint_mtimes(checkpoint_prefixes):
  """Returns the mtimes (modification timestamps) of the checkpoints.

  Globs for the checkpoints pointed to by `checkpoint_prefixes`.  If the files
  exist, collect their mtime.  Both V2 and V1 checkpoints are considered, in
  that priority.

  This is the recommended way to get the mtimes, since it takes into account
  the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefixes: a list of checkpoint paths, typically the results of
      `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
      sharded/non-sharded or V1/V2.
  Returns:
    A list of mtimes (in microseconds) of the found checkpoints.
  """
  mtimes = []

  def match_maybe_append(pathname):
    fnames = file_io.get_matching_files(pathname)
    if fnames:
      mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
      return True
    return False

  for checkpoint_prefix in checkpoint_prefixes:
    # Tries V2's metadata file first.
    pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                          saver_pb2.SaverDef.V2)
    if match_maybe_append(pathname):
      continue
    # Otherwise, tries V1, where the prefix is the complete pathname.
    match_maybe_append(checkpoint_prefix)

  return mtimes
Example #5
Source File: saver.py    From keras-lambda with MIT License 5 votes vote down vote up 
def get_checkpoint_mtimes(checkpoint_prefixes):
  """Returns the mtimes (modification timestamps) of the checkpoints.

  Globs for the checkpoints pointed to by `checkpoint_prefixes`.  If the files
  exist, collect their mtime.  Both V2 and V1 checkpoints are considered, in
  that priority.

  This is the recommended way to get the mtimes, since it takes into account
  the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefixes: a list of checkpoint paths, typically the results of
      `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of
      sharded/non-sharded or V1/V2.
  Returns:
    A list of mtimes (in microseconds) of the found checkpoints.
  """
  mtimes = []

  def match_maybe_append(pathname):
    fnames = file_io.get_matching_files(pathname)
    if fnames:
      mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9)
      return True
    return False

  for checkpoint_prefix in checkpoint_prefixes:
    # Tries V2's metadata file first.
    pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                          saver_pb2.SaverDef.V2)
    if match_maybe_append(pathname):
      continue
    # Otherwise, tries V1, where the prefix is the complete pathname.
    match_maybe_append(checkpoint_prefix)

  return mtimes