240 lines
8.2 KiB
Python
240 lines
8.2 KiB
Python
|
"""Numpy pickle compatibility functions."""
|
||
|
|
|
||
|
|
import pickle
|
||
|
|
import os
|
||
|
|
import zlib
|
||
|
|
from io import BytesIO
|
||
|
|
|
||
|
|
from ._compat import PY3_OR_LATER
|
||
|
|
from .numpy_pickle_utils import _ZFILE_PREFIX
|
||
|
|
from .numpy_pickle_utils import Unpickler
|
||
|
|
|
||
|
|
|
||
|
|
def hex_str(an_int):
|
||
|
|
"""Convert an int to an hexadecimal string."""
|
||
|
|
return '{:#x}'.format(an_int)
|
||
|
|
|
||
|
|
if PY3_OR_LATER:
|
||
|
|
def asbytes(s):
|
||
|
|
if isinstance(s, bytes):
|
||
|
|
return s
|
||
|
|
return s.encode('latin1')
|
||
|
|
else:
|
||
|
|
asbytes = str
|
||
|
|
|
||
|
|
_MAX_LEN = len(hex_str(2 ** 64))
|
||
|
|
_CHUNK_SIZE = 64 * 1024
|
||
|
|
|
||
|
|
|
||
|
|
def read_zfile(file_handle):
|
||
|
|
"""Read the z-file and return the content as a string.
|
||
|
|
|
||
|
|
Z-files are raw data compressed with zlib used internally by joblib
|
||
|
|
for persistence. Backward compatibility is not guaranteed. Do not
|
||
|
|
use for external purposes.
|
||
|
|
"""
|
||
|
|
file_handle.seek(0)
|
||
|
|
header_length = len(_ZFILE_PREFIX) + _MAX_LEN
|
||
|
|
length = file_handle.read(header_length)
|
||
|
|
length = length[len(_ZFILE_PREFIX):]
|
||
|
|
length = int(length, 16)
|
||
|
|
|
||
|
|
# With python2 and joblib version <= 0.8.4 compressed pickle header is one
|
||
|
|
# character wider so we need to ignore an additional space if present.
|
||
|
|
# Note: the first byte of the zlib data is guaranteed not to be a
|
||
|
|
# space according to
|
||
|
|
# https://tools.ietf.org/html/rfc6713#section-2.1
|
||
|
|
next_byte = file_handle.read(1)
|
||
|
|
if next_byte != b' ':
|
||
|
|
# The zlib compressed data has started and we need to go back
|
||
|
|
# one byte
|
||
|
|
file_handle.seek(header_length)
|
||
|
|
|
||
|
|
# We use the known length of the data to tell Zlib the size of the
|
||
|
|
# buffer to allocate.
|
||
|
|
data = zlib.decompress(file_handle.read(), 15, length)
|
||
|
|
assert len(data) == length, (
|
||
|
|
"Incorrect data length while decompressing %s."
|
||
|
|
"The file could be corrupted." % file_handle)
|
||
|
|
return data
|
||
|
|
|
||
|
|
|
||
|
|
def write_zfile(file_handle, data, compress=1):
|
||
|
|
"""Write the data in the given file as a Z-file.
|
||
|
|
|
||
|
|
Z-files are raw data compressed with zlib used internally by joblib
|
||
|
|
for persistence. Backward compatibility is not guarantied. Do not
|
||
|
|
use for external purposes.
|
||
|
|
"""
|
||
|
|
file_handle.write(_ZFILE_PREFIX)
|
||
|
|
length = hex_str(len(data))
|
||
|
|
# Store the length of the data
|
||
|
|
file_handle.write(asbytes(length.ljust(_MAX_LEN)))
|
||
|
|
file_handle.write(zlib.compress(asbytes(data), compress))
|
||
|
|
|
||
|
|
###############################################################################
|
||
|
|
# Utility objects for persistence.
|
||
|
|
|
||
|
|
|
||
|
|
class NDArrayWrapper(object):
|
||
|
|
"""An object to be persisted instead of numpy arrays.
|
||
|
|
|
||
|
|
The only thing this object does, is to carry the filename in which
|
||
|
|
the array has been persisted, and the array subclass.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, filename, subclass, allow_mmap=True):
|
||
|
|
"""Constructor. Store the useful information for later."""
|
||
|
|
self.filename = filename
|
||
|
|
self.subclass = subclass
|
||
|
|
self.allow_mmap = allow_mmap
|
||
|
|
|
||
|
|
def read(self, unpickler):
|
||
|
|
"""Reconstruct the array."""
|
||
|
|
filename = os.path.join(unpickler._dirname, self.filename)
|
||
|
|
# Load the array from the disk
|
||
|
|
# use getattr instead of self.allow_mmap to ensure backward compat
|
||
|
|
# with NDArrayWrapper instances pickled with joblib < 0.9.0
|
||
|
|
allow_mmap = getattr(self, 'allow_mmap', True)
|
||
|
|
memmap_kwargs = ({} if not allow_mmap
|
||
|
|
else {'mmap_mode': unpickler.mmap_mode})
|
||
|
|
array = unpickler.np.load(filename, **memmap_kwargs)
|
||
|
|
# Reconstruct subclasses. This does not work with old
|
||
|
|
# versions of numpy
|
||
|
|
if (hasattr(array, '__array_prepare__') and
|
||
|
|
self.subclass not in (unpickler.np.ndarray,
|
||
|
|
unpickler.np.memmap)):
|
||
|
|
# We need to reconstruct another subclass
|
||
|
|
new_array = unpickler.np.core.multiarray._reconstruct(
|
||
|
|
self.subclass, (0,), 'b')
|
||
|
|
return new_array.__array_prepare__(array)
|
||
|
|
else:
|
||
|
|
return array
|
||
|
|
|
||
|
|
|
||
|
|
class ZNDArrayWrapper(NDArrayWrapper):
|
||
|
|
"""An object to be persisted instead of numpy arrays.
|
||
|
|
|
||
|
|
This object store the Zfile filename in which
|
||
|
|
the data array has been persisted, and the meta information to
|
||
|
|
retrieve it.
|
||
|
|
The reason that we store the raw buffer data of the array and
|
||
|
|
the meta information, rather than array representation routine
|
||
|
|
(tostring) is that it enables us to use completely the strided
|
||
|
|
model to avoid memory copies (a and a.T store as fast). In
|
||
|
|
addition saving the heavy information separately can avoid
|
||
|
|
creating large temporary buffers when unpickling data with
|
||
|
|
large arrays.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, filename, init_args, state):
|
||
|
|
"""Constructor. Store the useful information for later."""
|
||
|
|
self.filename = filename
|
||
|
|
self.state = state
|
||
|
|
self.init_args = init_args
|
||
|
|
|
||
|
|
def read(self, unpickler):
|
||
|
|
"""Reconstruct the array from the meta-information and the z-file."""
|
||
|
|
# Here we a simply reproducing the unpickling mechanism for numpy
|
||
|
|
# arrays
|
||
|
|
filename = os.path.join(unpickler._dirname, self.filename)
|
||
|
|
array = unpickler.np.core.multiarray._reconstruct(*self.init_args)
|
||
|
|
with open(filename, 'rb') as f:
|
||
|
|
data = read_zfile(f)
|
||
|
|
state = self.state + (data,)
|
||
|
|
array.__setstate__(state)
|
||
|
|
return array
|
||
|
|
|
||
|
|
|
||
|
|
class ZipNumpyUnpickler(Unpickler):
|
||
|
|
"""A subclass of the Unpickler to unpickle our numpy pickles."""
|
||
|
|
|
||
|
|
dispatch = Unpickler.dispatch.copy()
|
||
|
|
|
||
|
|
def __init__(self, filename, file_handle, mmap_mode=None):
|
||
|
|
"""Constructor."""
|
||
|
|
self._filename = os.path.basename(filename)
|
||
|
|
self._dirname = os.path.dirname(filename)
|
||
|
|
self.mmap_mode = mmap_mode
|
||
|
|
self.file_handle = self._open_pickle(file_handle)
|
||
|
|
Unpickler.__init__(self, self.file_handle)
|
||
|
|
try:
|
||
|
|
import numpy as np
|
||
|
|
except ImportError:
|
||
|
|
np = None
|
||
|
|
self.np = np
|
||
|
|
|
||
|
|
def _open_pickle(self, file_handle):
|
||
|
|
return BytesIO(read_zfile(file_handle))
|
||
|
|
|
||
|
|
def load_build(self):
|
||
|
|
"""Set the state of a newly created object.
|
||
|
|
|
||
|
|
We capture it to replace our place-holder objects,
|
||
|
|
NDArrayWrapper, by the array we are interested in. We
|
||
|
|
replace them directly in the stack of pickler.
|
||
|
|
"""
|
||
|
|
Unpickler.load_build(self)
|
||
|
|
if isinstance(self.stack[-1], NDArrayWrapper):
|
||
|
|
if self.np is None:
|
||
|
|
raise ImportError("Trying to unpickle an ndarray, "
|
||
|
|
"but numpy didn't import correctly")
|
||
|
|
nd_array_wrapper = self.stack.pop()
|
||
|
|
array = nd_array_wrapper.read(self)
|
||
|
|
self.stack.append(array)
|
||
|
|
|
||
|
|
# Be careful to register our new method.
|
||
|
|
if PY3_OR_LATER:
|
||
|
|
dispatch[pickle.BUILD[0]] = load_build
|
||
|
|
else:
|
||
|
|
dispatch[pickle.BUILD] = load_build
|
||
|
|
|
||
|
|
|
||
|
|
def load_compatibility(filename):
|
||
|
|
"""Reconstruct a Python object from a file persisted with joblib.dump.
|
||
|
|
|
||
|
|
This function ensures the compatibility with joblib old persistence format
|
||
|
|
(<= 0.9.3).
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
-----------
|
||
|
|
filename: string
|
||
|
|
The name of the file from which to load the object
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
result: any Python object
|
||
|
|
The object stored in the file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
joblib.dump : function to save an object
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
This function can load numpy array files saved separately during the
|
||
|
|
dump.
|
||
|
|
"""
|
||
|
|
with open(filename, 'rb') as file_handle:
|
||
|
|
# We are careful to open the file handle early and keep it open to
|
||
|
|
# avoid race-conditions on renames. That said, if data is stored in
|
||
|
|
# companion files, moving the directory will create a race when
|
||
|
|
# joblib tries to access the companion files.
|
||
|
|
unpickler = ZipNumpyUnpickler(filename, file_handle=file_handle)
|
||
|
|
try:
|
||
|
|
obj = unpickler.load()
|
||
|
|
except UnicodeDecodeError as exc:
|
||
|
|
# More user-friendly error message
|
||
|
|
if PY3_OR_LATER:
|
||
|
|
new_exc = ValueError(
|
||
|
|
'You may be trying to read with '
|
||
|
|
'python 3 a joblib pickle generated with python 2. '
|
||
|
|
'This feature is not supported by joblib.')
|
||
|
|
new_exc.__cause__ = exc
|
||
|
|
raise new_exc
|
||
|
|
finally:
|
||
|
|
if hasattr(unpickler, 'file_handle'):
|
||
|
|
unpickler.file_handle.close()
|
||
|
|
return obj
|