本文整理汇总了Python中args.get_args方法的典型用法代码示例。如果您正苦于以下问题：Python args.get_args方法的具体用法？Python args.get_args怎么用？Python args.get_args使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类args的用法示例。
在下文中一共展示了args.get_args方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "morph")
morph(args) 示例2: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
# command line args
args = get_args()
save_dir = os.path.join("checkpoints", args.log_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
os.makedirs(os.path.join(save_dir, 'images'))
with open(os.path.join(save_dir, 'command.sh'), 'w') as f:
f.write('python -X faulthandler ' + ' '.join(sys.argv))
f.write('\n')
if args.seed is None:
args.seed = random.randint(0, 1000000)
set_random_seed(args.seed)
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
if args.sync_bn:
assert args.distributed
print("Arguments:")
print(args)
ngpus_per_node = torch.cuda.device_count()
if args.distributed:
args.world_size = ngpus_per_node * args.world_size
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(save_dir, ngpus_per_node, args))
else:
main_worker(args.gpu, save_dir, ngpus_per_node, args) 示例3: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
ctx = get_extension_context(
args.context, device_id=args.device_id, type_config=args.type_config)
nn.set_default_context(ctx)
train(args) 示例4: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "generate")
generate(args) 示例5: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "match")
match(args) 示例6: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "train")
train(args) 示例7: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args)
train(args) 示例8: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "generate")
generate(args) 示例9: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
train = True
if args.data_type == "train":
train = True
elif args.data_type == "val":
train = False
prepare_pix2pix_dataset(args.dataset, train) 示例10: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
save_args(args, "generate")
interpolate(args) 示例11: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
# Context
extension_module = args.context
ctx = get_extension_context(
extension_module, device_id=args.device_id, type_config=args.type_config)
nn.set_default_context(ctx)
train(args) 示例12: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
rng = np.random.RandomState(1223)
# Get context
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(
args.context, device_id=args.device_id, type_config=args.type_config)
nn.set_default_context(ctx)
iterations = []
mean_iou = []
model_dir = args.model_load_path
for filename in os.listdir(model_dir):
args.model_load_path = model_dir+filename
miou = eval.validate(args)
iterations.append(filename.split('.')[0])
mean_iou.append(miou)
for i in range(len(iterations)):
iterations[i] = iterations[i].replace('param_', '')
itr = list(map(int, iterations))
# Plot Iterations Vs mIOU
plt.axes([0, max(itr), 0.0, 1.0])
plt.xlabel('Iterations')
plt.ylabel('Accuracy - mIOU')
plt.scatter(itr, mean_iou)
plt.show()
print(iterations)
print(mean_iou)
with open('iterations.txt', 'w') as f:
for item in iterations:
f.write('%s\n' % item)
with open('miou.txt', 'w') as f2:
for item in mean_iou:
f2.write('%s\n' % item)
#plt.plot(iterations, mean_iou) 示例13: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
'''
Arguments:
train-file = txt file containing randomly selected image filenames to be taken as training set.
val-file = txt file containing randomly selected image filenames to be taken as validation set.
data-dir = dataset directory
Usage: python dataset_utils.py --train-file="" --val-file="" --data_dir=""
'''
args = get_args()
data_dir = args.data_dir
generate_path_files(data_dir, args.train_file, args.val_file) 示例14: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
args = get_args()
rng = np.random.RandomState(1223)
# Get context
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(
args.context, device_id=args.device_id, type_config=args.type_config)
nn.set_default_context(ctx)
miou = validate(args) 示例15: main
# 需要导入模块: import args [as 别名]
# 或者: from args import get_args [as 别名]
def main():
'''
Main
Usage: python convert_tf_nnabla.py --input-ckpt-file=/path to ckpt file --output-nnabla-file=/output .h5 file
'''
# Parse the arguments
args = get_args()
# convert the input file(.ckpt) to the output file(.h5)
convert(args.input_ckpt_file, args.output_nnabla_file)