Python file_io.is_directory方法代码示例

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _recursive_copy(src_dir, dest_dir):
  """Copy the contents of src_dir into the folder dest_dir.
  Args:
    src_dir: gsc or local path.
    dest_dir: gcs or local path.
  When called, dest_dir should exist.
  """
  src_dir = python_portable_string(src_dir)
  dest_dir = python_portable_string(dest_dir)

  file_io.recursive_create_dir(dest_dir)
  for file_name in file_io.list_directory(src_dir):
    old_path = os.path.join(src_dir, file_name)
    new_path = os.path.join(dest_dir, file_name)

    if file_io.is_directory(old_path):
      _recursive_copy(old_path, new_path)
    else:
      file_io.copy(old_path, new_path, overwrite=True) 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def recursive_copy(src_dir, dest_dir):
  """Copy the contents of src_dir into the folder dest_dir.
  Args:
    src_dir: gsc or local path.
    dest_dir: gcs or local path.
  """

  file_io.recursive_create_dir(dest_dir)
  for file_name in file_io.list_directory(src_dir):
    old_path = os.path.join(src_dir, file_name)
    new_path = os.path.join(dest_dir, file_name)

    if file_io.is_directory(old_path):
      recursive_copy(old_path, new_path)
    else:
      file_io.copy(old_path, new_path, overwrite=True) 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_bookmarks(self, request):
    run = request.args.get('run')
    if not run:
      return Respond(request, 'query parameter "run" is required', 'text/plain',
                     400)

    name = request.args.get('name')
    if name is None:
      return Respond(request, 'query parameter "name" is required',
                     'text/plain', 400)

    if run not in self.configs:
      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)

    config = self.configs[run]
    fpath = self._get_bookmarks_file_for_tensor(name, config)
    if not fpath:
      return Respond(
          request,
          'No bookmarks file found for tensor "%s" in the config file "%s"' %
          (name, self.config_fpaths[run]), 'text/plain', 400)
    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      return Respond(request, '"%s" not found, or is not a file' % fpath,
                     'text/plain', 400)

    bookmarks_json = None
    with file_io.FileIO(fpath, 'rb') as f:
      bookmarks_json = f.read()
    return Respond(request, bookmarks_json, 'application/json') 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_sprite_image(self, request):
    run = request.args.get('run')
    if not run:
      return Respond(request, 'query parameter "run" is required', 'text/plain',
                     400)

    name = request.args.get('name')
    if name is None:
      return Respond(request, 'query parameter "name" is required',
                     'text/plain', 400)

    if run not in self.configs:
      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)

    config = self.configs[run]
    embedding_info = self._get_embedding(name, config)

    if not embedding_info or not embedding_info.sprite.image_path:
      return Respond(
          request,
          'No sprite image file found for tensor "%s" in the config file "%s"' %
          (name, self.config_fpaths[run]), 'text/plain', 400)

    fpath = os.path.expanduser(embedding_info.sprite.image_path)
    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      return Respond(request, '"%s" does not exist or is directory' % fpath,
                     'text/plain', 400)
    f = file_io.FileIO(fpath, 'rb')
    encoded_image_string = f.read()
    f.close()
    image_type = imghdr.what(None, encoded_image_string)
    mime_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
    return Respond(request, encoded_image_string, mime_type) 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_bookmarks(self, request, query_params):
    run = query_params.get('run')
    if not run:
      request.respond('query parameter "run" is required', 'text/plain', 400)
      return

    name = query_params.get('name')
    if name is None:
      request.respond('query parameter "name" is required', 'text/plain', 400)
      return

    if run not in self.configs:
      request.respond('Unknown run: %s' % run, 'text/plain', 400)
      return

    config = self.configs[run]
    fpath = self._get_bookmarks_file_for_tensor(name, config)
    if not fpath:
      request.respond(
          'No bookmarks file found for tensor %s in the config file %s' %
          (name, self.config_fpaths[run]), 'text/plain', 400)
      return
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      request.respond('%s is not a file' % fpath, 'text/plain', 400)
      return

    bookmarks_json = None
    with file_io.FileIO(fpath, 'r') as f:
      bookmarks_json = f.read()
    request.respond(bookmarks_json, 'application/json') 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_sprite_image(self, request, query_params):
    run = query_params.get('run')
    if not run:
      request.respond('query parameter "run" is required', 'text/plain', 400)
      return

    name = query_params.get('name')
    if name is None:
      request.respond('query parameter "name" is required', 'text/plain', 400)
      return

    if run not in self.configs:
      request.respond('Unknown run: %s' % run, 'text/plain', 400)
      return

    config = self.configs[run]
    embedding_info = self._get_embedding(name, config)

    if not embedding_info or not embedding_info.sprite.image_path:
      request.respond(
          'No sprite image file found for tensor %s in the config file %s' %
          (name, self.config_fpaths[run]), 'text/plain', 400)
      return

    fpath = embedding_info.sprite.image_path
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      request.respond(
          '%s does not exist or is directory' % fpath, 'text/plain', 400)
      return
    f = file_io.FileIO(fpath, 'r')
    encoded_image_string = f.read()
    f.close()
    image_type = imghdr.what(None, encoded_image_string)
    mime_type = _IMGHDR_TO_MIMETYPE.get(image_type, _DEFAULT_IMAGE_MIMETYPE)
    request.respond(encoded_image_string, mime_type) 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def create_dir_test():
  """Verifies file_io directory handling methods ."""

  starttime = int(round(time.time() * 1000))
  dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime)
  print("Creating dir %s" % dir_name)
  file_io.create_dir(dir_name)
  elapsed = int(round(time.time() * 1000)) - starttime
  print("Created directory in: %d milliseconds" % elapsed)
  # Check that the directory exists.
  dir_exists = file_io.is_directory(dir_name)
  print("%s directory exists: %s" % (dir_name, dir_exists))

  # List contents of just created directory.
  print("Listing directory %s." % dir_name)
  starttime = int(round(time.time() * 1000))
  print(file_io.list_directory(dir_name))
  elapsed = int(round(time.time() * 1000)) - starttime
  print("Listed directory %s in %s milliseconds" % (dir_name, elapsed))

  # Delete directory.
  print("Deleting directory %s." % dir_name)
  starttime = int(round(time.time() * 1000))
  file_io.delete_recursively(dir_name)
  elapsed = int(round(time.time() * 1000)) - starttime
  print("Deleted directory %s in %s milliseconds" % (dir_name, elapsed)) 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_metadata(self, request):
    run = request.args.get('run')
    if run is None:
      return Respond(request, 'query parameter "run" is required', 'text/plain',
                     400)

    name = request.args.get('name')
    if name is None:
      return Respond(request, 'query parameter "name" is required',
                     'text/plain', 400)

    num_rows = _parse_positive_int_param(request, 'num_rows')
    if num_rows == -1:
      return Respond(request, 'query parameter num_rows must be integer > 0',
                     'text/plain', 400)

    if run not in self.configs:
      return Respond(request, 'Unknown run: "%s"' % run, 'text/plain', 400)

    config = self.configs[run]
    fpath = self._get_metadata_file_for_tensor(name, config)
    if not fpath:
      return Respond(
          request,
          'No metadata file found for tensor "%s" in the config file "%s"' %
          (name, self.config_fpaths[run]), 'text/plain', 400)
    fpath = _rel_to_abs_asset_path(fpath, self.config_fpaths[run])
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      return Respond(request, '"%s" not found, or is not a file' % fpath,
                     'text/plain', 400)

    num_header_rows = 0
    with file_io.FileIO(fpath, 'r') as f:
      lines = []
      # Stream reading the file with early break in case the file doesn't fit in
      # memory.
      for line in f:
        lines.append(line)
        if len(lines) == 1 and '\t' in lines[0]:
          num_header_rows = 1
        if num_rows and len(lines) >= num_rows + num_header_rows:
          break
    return Respond(request, ''.join(lines), 'text/plain') 

# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import is_directory [as 别名]
def _serve_metadata(self, request, query_params):
    run = query_params.get('run')
    if run is None:
      request.respond('query parameter "run" is required', 'text/plain', 400)
      return

    name = query_params.get('name')
    if name is None:
      request.respond('query parameter "name" is required', 'text/plain', 400)
      return

    num_rows = _parse_positive_int_param(request, query_params, 'num_rows')
    if num_rows == -1:
      return

    if run not in self.configs:
      request.respond('Unknown run: %s' % run, 'text/plain', 400)
      return

    config = self.configs[run]
    fpath = self._get_metadata_file_for_tensor(name, config)
    if not fpath:
      request.respond(
          'No metadata file found for tensor %s in the config file %s' %
          (name, self.config_fpaths[run]), 'text/plain', 400)
      return
    if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
      request.respond('%s is not a file' % fpath, 'text/plain', 400)
      return

    num_header_rows = 0
    with file_io.FileIO(fpath, 'r') as f:
      lines = []
      # Stream reading the file with early break in case the file doesn't fit in
      # memory.
      for line in f:
        lines.append(line)
        if len(lines) == 1 and '\t' in lines[0]:
          num_header_rows = 1
        if num_rows and len(lines) >= num_rows + num_header_rows:
          break
    request.respond(''.join(lines), 'text/plain')