如何将多处理与继承结合使用

Question

我正试图通过多处理来加快脚本的运行时间。当我用更简单的定义(如调整不同目录上图像的大小(尝试相同的多处理代码时，多处理效果很好，但当我用下面的代码尝试时，它运行了，但没有给出任何输出或引发任何错误，我想知道这可能是什么原因。

我还想知道如何对这些代码使用多处理，也许继承是问题所在？

class Skeleton:
def __init__(self, path, **kwargs):
if type(path) is str:
self.path = path
self.inputStack = loadStack(self.path).astype(bool)
if kwargs != {}:
aspectRatio = kwargs["aspectRatio"]
self.inputStack = ndimage.interpolation.zoom(self.inputStack, zoom=aspectRatio, order=2, 
prefilter=False)
def setThinningOutput(self, mode="reflect"):
# Thinning output
self.skeletonStack = get_thinned(self.inputStack, mode)
def setNetworkGraph(self, findSkeleton=False):
# Network graph of the crowded region removed output
self.skeletonStack = self.inputStack
self.graph = get_networkx_graph_from_array(self.skeletonStack)
def setPrunedSkeletonOutput(self):
# Prune unnecessary segments in crowded regions removed skeleton
self.setNetworkGraph(findSkeleton=True)
self.outputStack = pr.getPrunedSkeleton(self.skeletonStack, self.graph)
saveStack(self.outputStack, self.path + "pruned/")

class Trabeculae (Skeleton):
pass
def TrabeculaeY (path):
path_mrb01_square = Trabeculae(path)
path_mrb01_square.setPrunedSkeletonOutput()
if __name__=='__main__':
path1 = (r' ')
path2 = (r' ')
path3 = (r' ')
the_list =[]
the_list.append(path1)
the_list.append(path2)
the_list.append(path3)
for i in range (0,len(the_list)):
p1 = multiprocessing.Process(target=TrabeculaeY, args=(the_list[i],))
p1.start()
p1.join()

Answer 1

继承对于多处理来说不是问题。

您不能join()循环内的进程。这意味着循环等待p1完成它的工作，然后再继续下一个循环。

相反，在一个循环中启动所有进程，然后等待第二个循环中的所有进程，如下所示：

if __name__=='__main__':
path1 = (r' ')
path2 = (r' ')
path3 = (r' ')
the_list =[]
the_list.append(path1)
the_list.append(path2)
the_list.append(path3)
started_processes = []
for i in range (0,len(the_list)):
p1 = multiprocessing.Process(target=TrabeculaeY, args=(the_list[i],))
p1.start()
started_processes.append(p1)
for p in started_processes:
p.join()  

我用于测试的完整代码：

import multiprocessing

class Skeleton:
def __init__(self, path, **kwargs):
self.path = path
pass
def setThinningOutput(self, mode="reflect"):
pass
def setNetworkGraph(self, findSkeleton=False):
pass
def setPrunedSkeletonOutput(self):
print(self.path)

class Trabeculae(Skeleton):
pass

def TrabeculaeY(path:str):
path_mrb01_square = Trabeculae(path)
path_mrb01_square.setPrunedSkeletonOutput()

if __name__ == '__main__':
the_list = [r'1', r'2', r'3']
started_processes = []
for path in the_list:
process = multiprocessing.Process(target=TrabeculaeY, args=path)
process.start()
started_processes.append(process)
for process in started_processes:
process.join()