parser.add_argument("--conda_env", type=str, default="some_name") parser.add_argument("--notification_email", type=str, default="will@email.com") # 添加特定于模型的参数 parser = LitModel.add_model_specific_args(parser) # 将所有可用的trainer选项添加到argparse parser = Trainer.add_argparse...
importargparseimportpytorch_lightningasplparser=argparse.ArgumentParser("")sub_parsers=parser.add_subparsers()train_parser=sub_parsers.add_parser("train")train_parser.add_argument("--seed")train_parser=pl.Trainer.add_argparse_args(train_parser)args=parser.parse_args() Runpython cli.py train --hel...
🚀 Feature Current Trainer.add_argparse_args(parser_param) creates a new ArgumentParser object, passing the parser_param object as a parent to the newly created parser. Trainer.add_argparse_args() returns the new ArgumentParser object to ...
利用argparse库,将所有参数添加到argparse.ArgumentParser中,detr、moco就是这么干的。 importargparseparser=argparse.ArgumentParser()parser.add_argument('--batch-size',type=int,default=1)parser.add_argument('--learning-rate',type=float,default=0.001)print(parser.parse_args()) 2. 利用yacs库创建默认配置,...
支持argparse命令行指定参数,也支持config.yaml配置文件 支持最优模型保存ModelCheckpoint 支持自定义回调函数Callback 支持NNI模型剪枝(L1/L2-Pruner,FPGM-Pruner Slim-Pruner)nni_pruning 非常轻便,安装简单 ...
from .library.custom_train_functions import apply_masked_loss, add_custom_train_arguments class FluxTrainer: def __init__(self): self.sample_prompts_te_outputs = None def init_train(self, args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) #...
python type error是什么意思_Python 报错 TypeError：’DoesNotExist’对象不可调用
parser = argparse.ArgumentParser() _, unknown = parser.parse_known_args() for item in unknown: source = item.split('=') if len(source) != 2: raise ValueError("You should add = to the passed arguments. " "For example --seed=123, the store_true action is not su...
🐛 Bug When generating a parser for Trainer arguments with Trainer.add_argparse_args, the type of fast_dev_run is no longer interpreted correctly (was working correctly on PL 1.08, but no longer works with PL 1.1). When interpreting an ar...
import ArgumentParser def main(args): model = MyModule() data = MyData() trainer = Trainer.from_argparse_args(args) trainer.fit(model, data) if __name__ == "__main__": parser = ArgumentParser() parser = Trainer.add_argparse_args(parser) args = parser.parse_args() main(args) ...