[refactor] rename

Former-commit-id: ec317605d24223527b8c2d4b4d2ee876f86abbaf

train_scripts/train_videogpt.py

@@ -1,13 +1,12 @@
 import sys
 sys.path.append(".")
 from opensora.models.ae.videobase import (
-    VideoGPTVQVAE,
-    VideoGPTConfiguration,
-    VideoGPTDataset,
-    VideoGPTTrainer,
+    VQVAEModel,
+    VQVAEConfiguration,
+    VQVAEDataset,
+    VQVAETrainer,
 )
 import argparse
-import accelerate
 from typing import Optional
 from accelerate.utils import set_seed
 from transformers import HfArgumentParser, TrainingArguments
@@ -15,18 +14,18 @@ from dataclasses import dataclass, field
 
 
 @dataclass
-class VideoGPTArgument(VideoGPTConfiguration):
+class VQVAEArgument(VQVAEConfiguration):
     data_path: str = field(default=None, metadata={"help": "data path"})
 
 @dataclass
-class VideoGPTTrainingArgument(TrainingArguments):
+class VQVAETrainingArgument(TrainingArguments):
     remove_unused_columns: Optional[bool] = field(
         default=False, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
     )
 
-def train(args, videogpt_args, training_args):
+def train(args, VQVAE_args, training_args):
     # Load Model
-    model_config = VideoGPTConfiguration(
+    model_config = VQVAEConfiguration(
         embedding_dim=args.embedding_dim,
         n_codes=args.n_codes,
         n_hiddens=args.n_hiddens,
@@ -35,19 +34,19 @@ def train(args, videogpt_args, training_args):
         downsample=args.downsample,
         resolution=args.resolution
     )
-    model = VideoGPTVQVAE(model_config)
+    model = VQVAEModel(model_config)
     # Load Dataset
-    dataset = VideoGPTDataset(args.data_path, sequence_length=args.sequence_length)
+    dataset = VQVAEDataset(args.data_path, sequence_length=args.sequence_length)
 
     # Load Trainer
-    trainer = VideoGPTTrainer(model, training_args, train_dataset=dataset)
+    trainer = VQVAETrainer(model, training_args, train_dataset=dataset)
     trainer.train()
 
 
 if __name__ == "__main__":
-    parser = HfArgumentParser((VideoGPTArgument, VideoGPTTrainingArgument))
-    videogpt_args, training_args = parser.parse_args_into_dataclasses()
-    args = argparse.Namespace(**vars(videogpt_args), **vars(training_args))
+    parser = HfArgumentParser((VQVAEArgument, VQVAETrainingArgument))
+    VQVAE_args, training_args = parser.parse_args_into_dataclasses()
+    args = argparse.Namespace(**vars(VQVAE_args), **vars(training_args))
     set_seed(args.seed)
 
-    train(args, videogpt_args, training_args)
+    train(args, VQVAE_args, training_args)