本文整理汇总了Python中utils.config.process_config方法的典型用法代码示例。如果您正苦于以下问题：Python config.process_config方法的具体用法？Python config.process_config怎么用？Python config.process_config使用的例子？那么, 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utils.config的用法示例。
在下文中一共展示了config.process_config方法的5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: from utils import config [as 别名]
# 或者: from utils.config import process_config [as 别名]
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([config.callbacks.tensorboard_log_dir, config.callbacks.checkpoint_dir])
print('Create the data generator.')
data_loader = SimpleMnistDataLoader(config)
print('Create the model.')
model = SimpleMnistModel(config)
print('Create the trainer')
trainer = SimpleMnistModelTrainer(model.model, data_loader.get_train_data(), config)
print('Start training the model.')
trainer.train() 示例2: main
# 需要导入模块: from utils import config [as 别名]
# 或者: from utils.config import process_config [as 别名]
def main():
# capture the config path from the run arguments
# then process the json configuration fill
try:
args = get_args()
config = process_config(args.config)
# create the experiments dirs
create_dirs([config.callbacks.tensorboard_log_dir, config.callbacks.checkpoint_dir])
print('Create the data generator.')
data_loader = factory.create("data_loader."+config.data_loader.name)(config)
print('Create the model.')
model = factory.create("models."+config.model.name)(config)
print('Create the trainer')
trainer = factory.create("trainers."+config.trainer.name)(model.model, data_loader.get_train_data(), config)
print('Start training the model.')
trainer.train()
except Exception as e:
print(e)
sys.exit(1) 示例3: main
# 需要导入模块: from utils import config [as 别名]
# 或者: from utils.config import process_config [as 别名]
def main():
# capture the config path from the run arguments
# then process the json configration file
try:
args = get_args()
config = process_config(args.config)
except Exception as e:
print("missing or invalid arguments", e)
exit(0)
tf.logging.set_verbosity(tf.logging.INFO)
if args.stylize:
evaluate(config, args.content, args.style)
else:
train(config) 示例4: main
# 需要导入模块: from utils import config [as 别名]
# 或者: from utils.config import process_config [as 别名]
def main():
args = get_args()
config = process_config(args.config)
# create the experiments dirs
create_dirs([config.cache_dir, config.model_dir,
config.log_dir, config.img_dir])
# logging to the file and stdout
logger = get_logger(config.log_dir, config.exp_name)
# fix random seed to reproduce results
random.seed(config.random_seed)
logger.info('Random seed: {:d}'.format(config.random_seed))
if config.method in ['src', 'jigsaw', 'rotate']:
model = AuxModel(config, logger)
else:
raise ValueError("Unknown method: %s" % config.method)
src_loader, val_loader = get_train_val_dataloader(config.datasets.src)
test_loader = get_test_dataloader(config.datasets.test)
tar_loader = None
if config.datasets.get('tar', None):
tar_loader = get_target_dataloader(config.datasets.tar)
if config.mode == 'train':
model.train(src_loader, tar_loader, val_loader, test_loader)
elif config.mode == 'test':
model.test(test_loader) 示例5: main
# 需要导入模块: from utils import config [as 别名]
# 或者: from utils.config import process_config [as 别名]
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
sess = tf.Session()
# create your data generator
data = DataGenerator(config)
# create an instance of the model you want
model = ExampleModel(config)
# create tensorboard logger
logger = Logger(sess, config)
# create trainer and pass all the previous components to it
trainer = ExampleTrainer(sess, model, data, config, logger)
#load model if exists
model.load(sess)
# here you train your model
trainer.train()