File size: 1,783 Bytes
a220803 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import sys
sys.path.append(".")
from opensora.models.ae.videobase.dataset_videobase import VideoDataset
from opensora.models.ae.videobase import (
VQVAEModel,
VQVAEConfiguration,
VQVAETrainer,
)
import argparse
from typing import Optional
from accelerate.utils import set_seed
from transformers import HfArgumentParser, TrainingArguments
from dataclasses import dataclass, field, asdict
@dataclass
class VQVAEArgument:
embedding_dim: int = field(default=256),
n_codes: int = field(default=2048),
n_hiddens: int = field(default=240),
n_res_layers: int = field(default=4),
resolution: int = field(default=128),
sequence_length: int = field(default=16),
downsample: str = field(default="4,4,4"),
no_pos_embd: bool = True,
data_path: str = field(default=None, metadata={"help": "data path"})
@dataclass
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, vqvae_args, training_args):
# Load Config
config = VQVAEConfiguration(**asdict(vqvae_args))
# Load Model
model = VQVAEModel(config)
# Load Dataset
dataset = VideoDataset(args.data_path, sequence_length=args.sequence_length, resolution=config.resolution)
# Load Trainer
trainer = VQVAETrainer(model, training_args, train_dataset=dataset)
trainer.train()
if __name__ == "__main__":
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, vqvae_args, training_args)
|