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

The following are 16 code examples of tensorflow.python.lib.io.file_io.is_directory(). 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: util.py    From pydatalab with Apache License 2.0 6 votes vote down vote up 
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)
Example #2
Source File: task.py    From pydatalab with Apache License 2.0 6 votes vote down vote up 
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)
Example #3
Source File: task.py    From pydatalab with Apache License 2.0 6 votes vote down vote up 
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)
Example #4
Source File: projector_plugin.py    From lambda-packs with MIT License 5 votes vote down vote up 
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')
Example #5
Source File: projector_plugin.py    From lambda-packs with MIT License 5 votes vote down vote up 
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)
Example #6
Source File: plugin.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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')
Example #7
Source File: plugin.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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)
Example #8
Source File: plugin.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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')
Example #9
Source File: plugin.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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)
Example #10
Source File: gcs_smoke.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
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))
Example #11
Source File: plugin.py    From keras-lambda with MIT License 5 votes vote down vote up 
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')
Example #12
Source File: plugin.py    From keras-lambda with MIT License 5 votes vote down vote up 
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)
Example #13
Source File: projector_plugin.py    From lambda-packs with MIT License 4 votes vote down vote up 
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')
Example #14
Source File: plugin.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
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')
Example #15
Source File: plugin.py    From deep_image_model with Apache License 2.0 4 votes vote down vote up 
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')
Example #16
Source File: plugin.py    From keras-lambda with MIT License 4 votes vote down vote up 
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')