examples/pytorch/image_classification/run_train.sh

PYTHONPATH=. python -m torch.distributed.launch --nproc_per_node=2 \
    examples/pytorch/image_classification/finetune_image_classification.py \
    --num_classes 2 \
    --train_dataset_name 'tany0699/cats_and_dogs' \
    --val_dataset_name 'tany0699/cats_and_dogs' \
    --train_split train \
    --val_split validation \
    --use_model_config true \
add training args support and image classification fintune example design doc: https://yuque.antfin.com/pai/rwqgvl/khy4uw5dgi39s6ke usage: ```python from modelscope.trainers.training_args import (ArgAttr, MSArgumentParser, training_args) training_args.topk = ArgAttr(cfg_node_name=['train.evaluation.metric_options.topk', 'evaluation.metric_options.topk'], default=(1,), help='evaluation using topk, tuple format, eg (1,), (1,5)') training_args.train_data = ArgAttr(type=str, default='tany0699/cats_and_dogs', help='train dataset') training_args.validation_data = ArgAttr(type=str, default='tany0699/cats_and_dogs', help='validation dataset') training_args.model_id = ArgAttr(type=str, default='damo/cv_vit-base_image-classification_ImageNet-labels', help='model name') parser = MSArgumentParser(training_args) cfg_dict = parser.get_cfg_dict() args = parser.args train_dataset = create_dataset(args.train_data, split='train') val_dataset = create_dataset(args.validation_data, split='validation') def cfg_modify_fn(cfg): cfg.merge_from_dict(cfg_dict) return cfg kwargs = dict( model=args.model_id, # model id train_dataset=train_dataset, # training dataset eval_dataset=val_dataset, # validation dataset cfg_modify_fn=cfg_modify_fn # callback to modify configuration ) trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs) # start to train trainer.train() ``` Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11225071 2022-12-30 07:35:15 +08:00			`PYTHONPATH=. python -m torch.distributed.launch --nproc_per_node=2 \`
push to hub Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12235855 2023-04-10 18:17:52 +08:00			`examples/pytorch/image_classification/finetune_image_classification.py \`
add training args support and image classification fintune example design doc: https://yuque.antfin.com/pai/rwqgvl/khy4uw5dgi39s6ke usage: ```python from modelscope.trainers.training_args import (ArgAttr, MSArgumentParser, training_args) training_args.topk = ArgAttr(cfg_node_name=['train.evaluation.metric_options.topk', 'evaluation.metric_options.topk'], default=(1,), help='evaluation using topk, tuple format, eg (1,), (1,5)') training_args.train_data = ArgAttr(type=str, default='tany0699/cats_and_dogs', help='train dataset') training_args.validation_data = ArgAttr(type=str, default='tany0699/cats_and_dogs', help='validation dataset') training_args.model_id = ArgAttr(type=str, default='damo/cv_vit-base_image-classification_ImageNet-labels', help='model name') parser = MSArgumentParser(training_args) cfg_dict = parser.get_cfg_dict() args = parser.args train_dataset = create_dataset(args.train_data, split='train') val_dataset = create_dataset(args.validation_data, split='validation') def cfg_modify_fn(cfg): cfg.merge_from_dict(cfg_dict) return cfg kwargs = dict( model=args.model_id, # model id train_dataset=train_dataset, # training dataset eval_dataset=val_dataset, # validation dataset cfg_modify_fn=cfg_modify_fn # callback to modify configuration ) trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs) # start to train trainer.train() ``` Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11225071 2022-12-30 07:35:15 +08:00			`--num_classes 2 \`
update training args 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. 2023-02-10 05:32:21 +00:00			`--train_dataset_name 'tany0699/cats_and_dogs' \`
add 1.6 2023-05-22 10:53:18 +08:00			`--val_dataset_name 'tany0699/cats_and_dogs' \`
			`--train_split train \`
			`--val_split validation \`
			`--use_model_config true \`