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82482b3e96
Based on feat/0131/nlp_args branch, the original code review: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11408570 Support for running finetuning from the command line with training args, Compatible with the configuration optimization.
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
import os
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from dataclasses import dataclass, field
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from modelscope.metainfo import Trainers
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from modelscope.msdatasets.ms_dataset import MsDataset
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from modelscope.trainers.builder import build_trainer
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from modelscope.trainers.training_args import TrainingArgs
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@dataclass
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class ImageClassificationTrainingArgs(TrainingArgs):
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num_classes: int = field(
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default=None,
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metadata={
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'cfg_node': [
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'model.mm_model.head.num_classes',
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'model.mm_model.train_cfg.augments.0.num_classes',
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'model.mm_model.train_cfg.augments.1.num_classes'
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],
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'help':
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'number of classes',
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})
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topk: tuple = field(
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default=None,
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metadata={
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'cfg_node': [
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'train.evaluation.metric_options.topk',
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'evaluation.metric_options.topk'
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],
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'help':
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'evaluation using topk, tuple format, eg (1,), (1,5)',
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})
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warmup_iters: str = field(
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default=None,
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metadata={
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'cfg_node': 'train.lr_config.warmup_iters',
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'help': 'The warmup iters',
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})
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def create_dataset(name, split):
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namespace, dataset_name = name.split('/')
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return MsDataset.load(
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dataset_name, namespace=namespace, subset_name='default', split=split)
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def train():
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args = ImageClassificationTrainingArgs.from_cli(
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model='damo/cv_vit-base_image-classification_ImageNet-labels',
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max_epochs=1,
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lr=1e-4,
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optimizer='AdamW',
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warmup_iters=1,
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topk=(1, ))
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if args.dataset_name is not None:
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train_dataset = create_dataset(args.dataset_name, split='train')
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val_dataset = create_dataset(args.dataset_name, split='validation')
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else:
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train_dataset = create_dataset(args.train_dataset_name, split='train')
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val_dataset = create_dataset(args.val_dataset_name, split='validation')
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kwargs = dict(
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model=args.model, # model id
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train_dataset=train_dataset, # training dataset
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eval_dataset=val_dataset, # validation dataset
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cfg_modify_fn=args # callback to modify configuration
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)
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# in distributed training, specify pytorch launcher
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if 'MASTER_ADDR' in os.environ:
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kwargs['launcher'] = 'pytorch'
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trainer = build_trainer(
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name=Trainers.image_classification, default_args=kwargs)
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# start to train
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trainer.train()
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if __name__ == '__main__':
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train()
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