add 1.6

examples/pytorch/image_classification/finetune_image_classification.py

@@ -1,13 +1,12 @@
 import os
 from dataclasses import dataclass, field
 
+from modelscope import MsDataset, TrainingArgs
 from modelscope.metainfo import Trainers
-from modelscope.msdatasets.ms_dataset import MsDataset
 from modelscope.trainers.builder import build_trainer
-from modelscope.trainers.training_args import TrainingArgs
 
 
-@dataclass
+@dataclass(init=False)
 class ImageClassificationTrainingArgs(TrainingArgs):
     num_classes: int = field(
         default=None,
@@ -46,26 +45,35 @@ def create_dataset(name, split):
         dataset_name, namespace=namespace, subset_name='default', split=split)
 
 
-def train():
-    args = ImageClassificationTrainingArgs.from_cli(
-        model='damo/cv_vit-base_image-classification_ImageNet-labels',
-        max_epochs=1,
-        lr=1e-4,
-        optimizer='AdamW',
-        warmup_iters=1,
-        topk=(1, ))
-    if args.dataset_name is not None:
-        train_dataset = create_dataset(args.dataset_name, split='train')
-        val_dataset = create_dataset(args.dataset_name, split='validation')
+training_args = ImageClassificationTrainingArgs(
+    model='damo/cv_vit-base_image-classification_ImageNet-labels',
+    max_epochs=1,
+    lr=1e-4,
+    optimizer='AdamW',
+    warmup_iters=1,
+    topk=(1, )).parse_cli()
+config, args = training_args.to_config()
+
+
+def cfg_modify_fn(cfg):
+    if args.use_model_config:
+        cfg.merge_from_dict(config)
     else:
-        train_dataset = create_dataset(args.train_dataset_name, split='train')
-        val_dataset = create_dataset(args.val_dataset_name, split='validation')
+        cfg = config
+    return cfg
+
+
+def train():
+    train_dataset = create_dataset(
+        training_args.train_dataset_name, split=training_args.train_split)
+    val_dataset = create_dataset(
+        training_args.val_dataset_name, split=training_args.val_split)
 
     kwargs = dict(
         model=args.model,  # model id
         train_dataset=train_dataset,  # training dataset
         eval_dataset=val_dataset,  # validation dataset
-        cfg_modify_fn=args  # callback to modify configuration
+        cfg_modify_fn=cfg_modify_fn  # callback to modify configuration
     )
 
     # in distributed training, specify pytorch launcher