Question

有没有办法告诉numpy在写入python pickle文件时保留非标准跨步？

>>> # Create an array with non-standard striding
>>> x = numpy.arange(2*3*4, dtype='uint8').reshape((2,3,4)).transpose(0,2,1)

>>> x.strides
(12, 1, 4)

>>> # The pickling process converts it to a c-contiguous array.
>>> # Often, this is a good thing, but for some applications, the
>>> # non-standard striding is intentional and important to preserve.
>>> pickled = cPickle.dumps(x, protocol=cPickle.HIGHEST_PROTOCOL)
>>> cPickle.loads(pickled).strides
(12, 3, 1)

>>> # This is indeed happening during serialization, not deserialization
>>> pickletools.dis(pickled)
...
151: S        STRING     '\x00\x04\x08\x01\x05\t\x02\x06\n\x03\x07\x0b\x0c\x10\x14\r\x11\x15\x0e\x12\x16\x0f\x13\x17'
...

注意： numpy足够聪明，可以保留c-contiguous或fortran-contiguous，但它不会保留所有非标准步幅模式，包括酸洗和去除斑点。

Answer 1

我能想到的唯一方法就是亲自去做：

# ---------------------------------------------
import numpy
from numpy.lib.stride_tricks import as_strided
import cPickle

def dumps(x, protocol=cPickle.HIGHEST_PROTOCOL):
    # flatten that keep real data order
    y = as_strided(x, shape=(x.size,), strides=(min(x.strides),))
    return cPickle.dumps([y,x.shape,x.strides],protocol=protocol)

def loads(pickled):
    y,shape,strides = cPickle.loads(pickled)
    return as_strided(y,shape=shape,strides=strides)

if __name__=='__main__':
    x = numpy.arange(2*3*4, dtype='uint8').reshape((2,3,4)).transpose(0,2,1)

    pickled = dumps(x)
    y = loads(pickled)

    print 'x strides =', x.strides
    print 'y strides =', y.strides
    print 'x==y:', (x==y).all()
# ---------------------------------------------

输出：

x strides = (12, 1, 4)
y strides = (12, 1, 4)
x==y: True

保留python pickle文件中的numpy步幅

1 个答案: