包装C++ FileSystem API的文件IO方法
2018-10-18 17:15 更新
＃版权所有2015 TensorFlow作者.版权所有.
＃根据Apache许可证版本2.0(“许可证”)许可;
＃除非符合许可证,否则您不得使用此文件.
＃您可以获得许可证的副本
＃http://www.apache.org/licenses/LICENSE-2.0
＃除非适用法律要求或书面同意软件
根据许可证分发的＃分发在“按原样”基础上,
＃无明示或暗示的任何种类的保证或条件.
＃查看有关权限的特定语言的许可证
许可证下的＃限制.
＃ ============================================================================
""包装 C ++ FileSystem API的文件IO方法.""
""C ++ FileSystem API是 SWIG 包装在 file_io.i 中.这些功能称之为
完成基本的文件 IO 操作.""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import uuid
import six
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.framework import errors
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
class FileIO(object):
"""FileIO class that exposes methods to read / write to / from files.
The constructor takes the following arguments:
name: name of the file
mode: one of 'r', 'w', 'a', 'r+', 'w+', 'a+'. Append 'b' for bytes mode.
Can be used as an iterator to iterate over lines in the file.
The default buffer size used for the BufferedInputStream used for reading
the file line by line is 1024 * 512 bytes.
"""
def __init__(self, name, mode):
self.__name = name
self.__mode = mode
self._read_buf = None
self._writable_file = None
self._binary_mode = "b" in mode
mode = mode.replace("b", "")
if mode not in ("r", "w", "a", "r+", "w+", "a+"):
raise errors.InvalidArgumentError(
None, None, "mode is not 'r' or 'w' or 'a' or 'r+' or 'w+' or 'a+'")
self._read_check_passed = mode in ("r", "r+", "a+", "w+")
self._write_check_passed = mode in ("a", "w", "r+", "a+", "w+")
@property
def name(self):
"""Returns the file name."""
return self.__name
@property
def mode(self):
"""Returns the mode in which the file was opened."""
return self.__mode
def _preread_check(self):
if not self._read_buf:
if not self._read_check_passed:
raise errors.PermissionDeniedError(None, None,
"File isn't open for reading")
with errors.raise_exception_on_not_ok_status() as status:
self._read_buf = pywrap_tensorflow.CreateBufferedInputStream(
compat.as_bytes(self.__name), 1024 * 512, status)
def _prewrite_check(self):
if not self._writable_file:
if not self._write_check_passed:
raise errors.PermissionDeniedError(None, None,
"File isn't open for writing")
with errors.raise_exception_on_not_ok_status() as status:
self._writable_file = pywrap_tensorflow.CreateWritableFile(
compat.as_bytes(self.__name), compat.as_bytes(self.__mode), status)
def _prepare_value(self, val):
if self._binary_mode:
return compat.as_bytes(val)
else:
return compat.as_str_any(val)
def size(self):
"""Returns the size of the file."""
return stat(self.__name).length
def write(self, file_content):
"""Writes file_content to the file. Appends to the end of the file."""
self._prewrite_check()
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.AppendToFile(
compat.as_bytes(file_content), self._writable_file, status)
def read(self, n=-1):
"""Returns the contents of a file as a string.
Starts reading from current position in file.
Args:
n: Read 'n' bytes if n != -1. If n = -1, reads to end of file.
Returns:
'n' bytes of the file (or whole file) in bytes mode or 'n' bytes of the
string if in string (regular) mode.
"""
self._preread_check()
with errors.raise_exception_on_not_ok_status() as status:
if n == -1:
length = self.size() - self.tell()
else:
length = n
return self._prepare_value(
pywrap_tensorflow.ReadFromStream(self._read_buf, length, status))
@deprecation.deprecated_args(
None,
"position is deprecated in favor of the offset argument.",
"position")
def seek(self, offset=None, whence=0, position=None):
# TODO(jhseu): Delete later. Used to omit `position` from docs.
# pylint: disable=g-doc-args
"""Seeks to the offset in the file.
Args:
offset: The byte count relative to the whence argument.
whence: Valid values for whence are:
0: start of the file (default)
1: relative to the current position of the file
2: relative to the end of file. offset is usually negative.
"""
# pylint: enable=g-doc-args
self._preread_check()
# We needed to make offset a keyword argument for backwards-compatibility.
# This check exists so that we can convert back to having offset be a
# positional argument.
# TODO(jhseu): Make `offset` a positional argument after `position` is
# deleted.
if offset is None and position is None:
raise TypeError("seek(): offset argument required")
if offset is not None and position is not None:
raise TypeError("seek(): offset and position may not be set "
"simultaneously.")
if position is not None:
offset = position
with errors.raise_exception_on_not_ok_status() as status:
if whence == 0:
pass
elif whence == 1:
offset += self.tell()
elif whence == 2:
offset += self.size()
else:
raise errors.InvalidArgumentError(
None, None,
"Invalid whence argument: {}. Valid values are 0, 1, or 2."
.format(whence))
ret_status = self._read_buf.Seek(offset)
pywrap_tensorflow.Set_TF_Status_from_Status(status, ret_status)
def readline(self):
r"""Reads the next line from the file. Leaves the '\n' at the end."""
self._preread_check()
return self._prepare_value(self._read_buf.ReadLineAsString())
def readlines(self):
"""Returns all lines from the file in a list."""
self._preread_check()
lines = []
while True:
s = self.readline()
if not s:
break
lines.append(s)
return lines
def tell(self):
"""Returns the current position in the file."""
self._preread_check()
return self._read_buf.Tell()
def __enter__(self):
"""Make usable with "with" statement."""
return self
def __exit__(self, unused_type, unused_value, unused_traceback):
"""Make usable with "with" statement."""
self.close()
def __iter__(self):
return self
def next(self):
retval = self.readline()
if not retval:
raise StopIteration()
return retval
def __next__(self):
return self.next()
def flush(self):
"""Flushes the Writable file.
This only ensures that the data has made its way out of the process without
any guarantees on whether it's written to disk. This means that the
data would survive an application crash but not necessarily an OS crash.
"""
if self._writable_file:
with errors.raise_exception_on_not_ok_status() as status:
ret_status = self._writable_file.Flush()
pywrap_tensorflow.Set_TF_Status_from_Status(status, ret_status)
def close(self):
"""Closes FileIO. Should be called for the WritableFile to be flushed."""
self._read_buf = None
if self._writable_file:
with errors.raise_exception_on_not_ok_status() as status:
ret_status = self._writable_file.Close()
pywrap_tensorflow.Set_TF_Status_from_Status(status, ret_status)
self._writable_file = None
def file_exists(filename):
"""Determines whether a path exists or not.
Args:
filename: string, a path
Returns:
True if the path exists, whether its a file or a directory.
False if the path does not exist and there are no filesystem errors.
Raises:
errors.OpError: Propagates any errors reported by the FileSystem API.
"""
try:
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.FileExists(compat.as_bytes(filename), status)
except errors.NotFoundError:
return False
return True
def delete_file(filename):
"""Deletes the file located at 'filename'.
Args:
filename: string, a filename
Raises:
errors.OpError: Propagates any errors reported by the FileSystem API. E.g.,
NotFoundError if the file does not exist.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.DeleteFile(compat.as_bytes(filename), status)
def read_file_to_string(filename, binary_mode=False):
"""Reads the entire contents of a file to a string.
Args:
filename: string, path to a file
binary_mode: whether to open the file in binary mode or not. This changes
the type of the object returned.
Returns:
contents of the file as a string or bytes.
Raises:
errors.OpError: Raises variety of errors that are subtypes e.g.
NotFoundError etc.
"""
if binary_mode:
f = FileIO(filename, mode="rb")
else:
f = FileIO(filename, mode="r")
return f.read()
def write_string_to_file(filename, file_content):
"""Writes a string to a given file.
Args:
filename: string, path to a file
file_content: string, contents that need to be written to the file
Raises:
errors.OpError: If there are errors during the operation.
"""
with FileIO(filename, mode="w") as f:
f.write(file_content)
def get_matching_files(filename):
"""Returns a list of files that match the given pattern(s).
Args:
filename: string or iterable of strings. The glob pattern(s).
Returns:
A list of strings containing filenames that match the given pattern(s).
Raises:
errors.OpError: If there are filesystem / directory listing errors.
"""
with errors.raise_exception_on_not_ok_status() as status:
if isinstance(filename, six.string_types):
return [
# Convert the filenames to string from bytes.
compat.as_str_any(matching_filename)
for matching_filename in pywrap_tensorflow.GetMatchingFiles(
compat.as_bytes(filename), status)
]
else:
return [
# Convert the filenames to string from bytes.
compat.as_str_any(matching_filename)
for single_filename in filename
for matching_filename in pywrap_tensorflow.GetMatchingFiles(
compat.as_bytes(single_filename), status)
]
def create_dir(dirname):
"""Creates a directory with the name 'dirname'.
Args:
dirname: string, name of the directory to be created
Notes:
The parent directories need to exist. Use recursive_create_dir instead if
there is the possibility that the parent dirs don't exist.
Raises:
errors.OpError: If the operation fails.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.CreateDir(compat.as_bytes(dirname), status)
def recursive_create_dir(dirname):
"""Creates a directory and all parent/intermediate directories.
It succeeds if dirname already exists and is writable.
Args:
dirname: string, name of the directory to be created
Raises:
errors.OpError: If the operation fails.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.RecursivelyCreateDir(compat.as_bytes(dirname), status)
def copy(oldpath, newpath, overwrite=False):
"""Copies data from oldpath to newpath.
Args:
oldpath: string, name of the file who's contents need to be copied
newpath: string, name of the file to which to copy to
overwrite: boolean, if false its an error for newpath to be occupied by an
existing file.
Raises:
errors.OpError: If the operation fails.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.CopyFile(
compat.as_bytes(oldpath), compat.as_bytes(newpath), overwrite, status)
def rename(oldname, newname, overwrite=False):
"""Rename or move a file / directory.
Args:
oldname: string, pathname for a file
newname: string, pathname to which the file needs to be moved
overwrite: boolean, if false its an error for newpath to be occupied by an
existing file.
Raises:
errors.OpError: If the operation fails.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.RenameFile(
compat.as_bytes(oldname), compat.as_bytes(newname), overwrite, status)
def atomic_write_string_to_file(filename, contents):
"""Writes to `filename` atomically.
This means that when `filename` appears in the filesystem, it will contain
all of `contents`. With write_string_to_file, it is possible for the file
to appear in the filesystem with `contents` only partially written.
Accomplished by writing to a temp file and then renaming it.
Args:
filename: string, pathname for a file
contents: string, contents that need to be written to the file
"""
temp_pathname = filename + ".tmp" + uuid.uuid4().hex
write_string_to_file(temp_pathname, contents)
rename(temp_pathname, filename, overwrite=True)
def delete_recursively(dirname):
"""Deletes everything under dirname recursively.
Args:
dirname: string, a path to a directory
Raises:
errors.OpError: If the operation fails.
"""
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.DeleteRecursively(compat.as_bytes(dirname), status)
def is_directory(dirname):
"""Returns whether the path is a directory or not.
Args:
dirname: string, path to a potential directory
Returns:
True, if the path is a directory; False otherwise
"""
try:
status = pywrap_tensorflow.TF_NewStatus()
return pywrap_tensorflow.IsDirectory(compat.as_bytes(dirname), status)
finally:
pywrap_tensorflow.TF_DeleteStatus(status)
def list_directory(dirname):
"""Returns a list of entries contained within a directory.
The list is in arbitrary order. It does not contain the special entries "."
and "..".
Args:
dirname: string, path to a directory
Returns:
[filename1, filename2, ... filenameN] as strings
Raises:
errors.NotFoundError if directory doesn't exist
"""
if not is_directory(dirname):
raise errors.NotFoundError(None, None, "Could not find directory")
with errors.raise_exception_on_not_ok_status() as status:
# Convert each element to string, since the return values of the
# vector of string should be interpreted as strings, not bytes.
return [
compat.as_str_any(filename)
for filename in pywrap_tensorflow.GetChildren(
compat.as_bytes(dirname), status)
]
def walk(top, in_order=True):
"""Recursive directory tree generator for directories.
Args:
top: string, a Directory name
in_order: bool, Traverse in order if True, post order if False.
Errors that happen while listing directories are ignored.
Yields:
Each yield is a 3-tuple: the pathname of a directory, followed by lists of
all its subdirectories and leaf files.
(dirname, [subdirname, subdirname, ...], [filename, filename, ...])
as strings
"""
top = compat.as_str_any(top)
try:
listing = list_directory(top)
except errors.NotFoundError:
return
files = []
subdirs = []
for item in listing:
full_path = os.path.join(top, item)
if is_directory(full_path):
subdirs.append(item)
else:
files.append(item)
here = (top, subdirs, files)
if in_order:
yield here
for subdir in subdirs:
for subitem in walk(os.path.join(top, subdir), in_order):
yield subitem
if not in_order:
yield here
def stat(filename):
"""Returns file statistics for a given path.
Args:
filename: string, path to a file
Returns:
FileStatistics struct that contains information about the path
Raises:
errors.OpError: If the operation fails.
"""
file_statistics = pywrap_tensorflow.FileStatistics()
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.Stat(compat.as_bytes(filename), file_statistics, status)
return file_statistics