pickle

问题 I'm trying to use multiprocessing.Pool in my code but I got this exception: PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup __builtin__.instancemethod failed I found this and it's preferred solution recipe my problem is that I don't know how to implement this solution in my code. my code is something like that: class G(class): def submit(self,data): cmd = self.createCommand(data) subprocess.call(cmd, shell=True) # call for a short command def main(self): self.pool =

问题 I am writing scripts to process (very large) files by repeatedly unpickling objects until EOF. I would like to partition the file and have separate processes (in the cloud) unpickle and process separate parts. However my partitioner is not intelligent, it does not know about the boundaries between pickled objects in the file (since those boundaries depend on the object types being pickled, etc.). Is there a way to scan a file for a "start pickled object" sentinel? The naive way would be to

问题 I have been read pickle source code recently. The following code in copy_reg make me confused: _HEAPTYPE = 1<<9 def _reduce_ex(self, proto): assert proto < 2 for base in self.__class__.__mro__: if hasattr(base, '__flags__') and not base.__flags__ & _HEAPTYPE: break else: base = object # not really reachable if base is object: state = None So what does __flags__ using for? I found it is defined in type object: type.__flags__ = 2148423147 I was tried to search it in the official doc, but

问题 I am trying to establish really basic string-message IPC (no need for objects or anything fancy like that) between two Python processes on the same machine, the listener in Py2 and the client in Py3. This doesn't work out of the box because Py3 MP defaults to Pickle protocol 3, which doesn't exist for the Py2 listener, so the listener crashes on an unrecognized pickle protocol. I wish MP wouldn't pickle at all, I'm just sending strings over the wire and don't see why I should be forced to go

问题 My problem is: class A(object): def __init__(self): #init def __setstate__(self,state): #A __setstate__ code here def __getstate__(self): #A __getstate__ code here return state class B(A): def __init__(self): #creates many object variables here A is from an external library. Hard solution This I would like to avoid When pickling B, pickle of course uses class A's __setstate__ , __getstate__ methods, so in order for pickle to work I should do something like this: class B(A): def __init__(self)

问题 Firstly I'm aware that there have been multiple questions already asked regarding this particular error but I can't find any that address the precise context in which it's occurring for me. I've also tried the solutions provided for other similar errors and it hasn't made any difference. I'm using the python module pickle to save an object to file and the reload it using the following code: with open('test_file.pkl', 'wb') as a: pickle.dump(object1, a, pickle.HIGHEST_PROTOCOL) This doesn't

问题 I am trying to use the mnist_data for hand written digit recognition.Now i tried this code to load the data. import cPickle import numpy as np def load_data(): f = open('G:/thesis paper/data sets/mnist.pkl.gz', 'rb') training_data, validation_data, test_data = cPickle.load(f) f.close() return (training_data, validation_data, test_data) def load_data_nn(): training_data, validation_data, test_data = load_data() inputs = [np.reshape(x, (784, 1)) for x in training_data[0]] results = [vectorized

问题 I'm trying to make a dataset of images in the same format as mnist.pkl I have used https://github.com/dmitriy-serdyuk/cats_vs_dogs/blob/master/cats_vs_dogs/make_dataset.py as reference. This is what i have so far path = '/home/dell/thesis/neon/Images' def PIL2array(img): return numpy.array(img.getdata(), numpy.uint8).reshape(img.size[1], img.size[0], 1) def main(): fileList = [os.path.join(dirpath, f) for dirpath, dirnames, files in os.walk(path) for f in files if f.endswith('.jpg')] print

问题 Why does the multiprocessing package for python pickle objects to pass them between processes, i.e. to return results from different processes to the main interpreter process? This may be an incredibly naive question, but why can't process A say to process B "object x is at point y in memory, it's yours now" without having to perform the operation necessary to represent the object as a string. 回答1: multiprocessing runs jobs in different processes. Processes have their own independent memory

问题 I have a large dataset: 20,000 x 40,000 as a numpy array. I have saved it as a pickle file. Instead of reading this huge dataset into memory, I'd like to only read a few (say 100) rows of it at a time, for use as a minibatch. How can I read only a few randomly-chosen (without replacement) lines from a pickle file? 回答1: You can write pickles incrementally to a file, which allows you to load them incrementally as well. Take the following example. Here, we iterate over the items of a list, and