1.OMP_NUM_THREADS的设置方法 要设置OMP_NUM_THREADS环境变量,可以使用以下方法之一: - 在命令行中使用export命令: ``` export OMP_NUM_THREADS=4 ``` 这将设置OMP_NUM_THREADS变量的值为4,表示程序将使用4个线程进行并行计算。 - 在脚本文件中使用export命令: ``` #!/bin/bash export OMP_NUM_THREADS=...
math.degrees(math.atan(vector[2]/math.sqrt(vector[0]**2+vector[1]**2))), "Profile", 0)result = self.shaDEM.ShadowCalc(data, vector, scale, unitZ, maxVal) b
OMP_NUM_THREADS是控制OpenMP并行线程数的标准环境变量。Multiwfn的内置控制方法虽然便于理解,但是写成脚本...
# 需要导入模块: import torch [as 别名]# 或者: from torch importset_num_threads[as 别名]defrun(self):# TODO Fix this dependency. The policy itself sets the thread limit# to 1, but this configuration seems to be per-thread in pytorch# so need to set it here too :(importtorch torch....
world_size=1, rank=0, ) Example #8Source File: mono_3d_estimation.py From 3d-vehicle-tracking with BSD 3-Clause "New" or "Revised" License 6 votes def main(): torch.set_num_threads(multiprocessing.cpu_count()) args = parse_args() if args.set == 'gta': from model.model import...
base_dir, 'Labels', label_filename))) # Shape: [H x W x 3] label = label[..., 0] == 255 # Turn it into a {0,1} binary mask with shape: [H x W] label = label.astype(np.uint8) # find corresponding image file img_file = label_filename.split('_')[0] + '.jpg' ...
img = cv2.imread(sys.argv[1]) cv2.setUseOptimized(True) cv2.setNumThreads(8) gs = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation() gs.setBaseImage(img) if (sys.argv[2][0] == 's'): gs.switchToSingleStrategy() elif (sys.argv[2][0] == 'f'): ...
▲点赞 1▼ defconfigure(num_jobs=8, TEST=False, subtract=0, num_proc=None, num_thread_per_proc=None):''' num_jobs is typically the # of genes we are parallelizing over '''ifnum_procisNone: num_proc = multiprocessing.cpu_count() - subtractifnum_jobs > num_proc: ...