python - 我如何使用 python 中的 Joblib 并行化调用其他 function 的 function

def Gmm_scan_Gen(list_dir, merger_idx, target_fm, lut_map, revtarget_fm, ROT_bounds_x=15, ROT_bounds_y=15, ROT_bounds_z=15, b_nonlin = 3.0, Mu_GMM_low = 10, Mu_GMM_high = 240, SIG_GMM_low =1, SIG_GMM_high = 25, sig_BiasField=0.5, sig_gamma = 0.4, dtype = None, vol_only=True, RAS=True, Rotation=True, Scaling=True, ElasticTransformation=True): volume,aff_vol,header_vol= load_volume(list_dir, dtype = dtype, vol_only=vol_only, RAS = RAS) label_map_init=volume.copy() print('label_map loaded') b_nonlin = np.random.uniform(0,b_nonlin) label_map = augmentor(label_map_init, b_nonlin, ROT_bounds_x, ROT_bounds_y, ROT_bounds_z, Rotation=Rotation, Scaling=Scaling, ElasticTransformation=ElasticTransformation) print('deformation done ') print(type(label_map[0,0,0])) img = np.zeros_like(label_map,dtype=np.float32) print("initial labels", (np.unique(label_map))) for label in np.unique(label_map): mu = (np.random.uniform(low = Mu_GMM_low ,high = Mu_GMM_high,size=None)) sig =(np.random.uniform(low = SIG_GMM_low, high= SIG_GMM_high, size=None)) structure = label_map==label img[structure]=np.random.normal(mu,sig,len(img[structure])) print('out of Gmm') ### Biais Field if sig_BiasField > 0: sigB = np.random.uniform(0,sig_BiasField) bf = np.random.normal(loc = 0, scale=sigB,size = (4,4,4,1)) print(bf.shape) print(img.shape) bf = upsample(bf,img.shape) bf = cropImage(bf,img.shape) bf = np.exp(bf) img_bf = np.multiply(bf, img) else : img_bf = img #img = img*bf print('bf') ## Gamma augmentation if sig_gamma > 0 : gamma = (np.random.normal(0,sig_gamma)) imgG=img_bf-img_bf.min() imgG=imgG/imgG.max() imgA = imgG**(np.exp(gamma)) else: imgA = img_bf label_map= Convert_labels(label_map,merger_idx,target_fm) label_map_rgb = fm2rgb(label_map,revtarget_fm,lut_map) plotVol(label_map_rgb,sliceIdx=100) plotVol(imgA,sliceIdx=100) print('imshow') plt.show() return imgA, label_map_rgb

from joblib import Parallel, delayed label_dir = r"C:\Users\abr40\Documents\training_label_maps" list_dir = sorted(os.path.join(label_dir,fname) for fname in os.listdir(label_dir)) if __name__ == '__main__': imgA, label_map_rgb = Parallel(n_jobs=8)(delayed(Gmm_scan_Gen)(list_dir[i],merger_idx,target_fm,lut_map,revtarget_fm) for i in range(len(list_dir)))

PS C:\Users\abr40\Documents> python -i .\GMM_bis.py 2022-08-04 17:01:33.350339: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2022-08-04 17:01:33.350568: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. <class 'list'> joblib.externals.loky.process_executor._RemoteTraceback: """ Traceback (most recent call last): File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\externals\loky\process_executor.py", line 436, in _process_worker r = call_item() File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\externals\loky\process_executor.py", line 288, in __call__ return self.fn(*self.args, **self.kwargs) File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\_parallel_backends.py", line 595, in __call__ return self.func(*args, **kwargs) File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py", line 262, in __call__ return [func(*args, **kwargs) File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py", line 262, in <listcomp> return [func(*args, **kwargs) File ".\GMM_bis.py", line 101, in Gmm_scan_Gen volume,aff_vol,header_vol= load_volume(list_dir, dtype = dtype, vol_only=vol_only, RAS = RAS) ValueError: too many values to unpack (expected 3) """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File ".\GMM_bis.py", line 230, in <module> imgA, label_map_rgb = Parallel(n_jobs=8)(delayed(Gmm_scan_Gen)(list_dir[i], [i], File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblibib\site-packages\joblib\parallel.py", line 1056, in __call__ self.retrieve() File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblibib\site-packages\joblib\parallel.py", line 935, in retrieve self._output.extend(job.get(timeout=self.timeout)) File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\site-packages\joblibib\site-packages\joblib\_parallel_backends.py", line 542, in wrap_future_result return future.result(timeout=timeout) ib\concurrent\futures\_ File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\concurrent\futures\_base.py", line 444, in result return self.__get_result() ib\concurrent\futures\_ File "C:\Users\abr40\AppData\Local\Programs\Python\Python38\lib\concurrent\futures\_base.py", line 389, in __get_result raise self._exception ValueError: too many values to unpack (expected 3)