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Revert "[dreambooth] fixing the incompatibity in requirements.txt (#3190) (#3378)" (#3481)

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NatalieC323
2023-04-06 20:22:52 +08:00
committed by GitHub
parent 891b8e7fac
commit fb8fae6f29
14 changed files with 98 additions and 124 deletions
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94
examples/images/diffusion/main.py
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@@ -23,21 +23,19 @@ from packaging import version
from PIL import Image
from prefetch_generator import BackgroundGenerator
from torch.utils.data import DataLoader, Dataset, Subset, random_split
from ldm.models.diffusion.ddpm import LatentDiffusion
#try:
from lightning.pytorch import seed_everything
from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
from lightning.pytorch.trainer import Trainer
from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
from lightning.pytorch.loggers import WandbLogger, TensorBoardLogger
from lightning.pytorch.strategies import ColossalAIStrategy,DDPStrategy
LIGHTNING_PACK_NAME = "lightning.pytorch."
# #except:
# from pytorch_lightning import seed_everything
# from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
# from pytorch_lightning.trainer import Trainer
# from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
# LIGHTNING_PACK_NAME = "pytorch_lightning."
try:
from lightning.pytorch import seed_everything
from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
from lightning.pytorch.trainer import Trainer
from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
LIGHTNING_PACK_NAME = "lightning.pytorch."
except:
from pytorch_lightning import seed_everything
from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
LIGHTNING_PACK_NAME = "pytorch_lightning."
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
@@ -577,7 +575,7 @@ if __name__ == "__main__":
# target: path to test dataset
# params:
# key: value
# lightning: (optional, has same defaults and can be specified on cmdline)
# lightning: (optional, has sane defaults and can be specified on cmdline)
# trainer:
# additional arguments to trainer
# logger:
@@ -655,7 +653,7 @@ if __name__ == "__main__":
# Sets the seed for the random number generator to ensure reproducibility
seed_everything(opt.seed)
# Intinalize and save configuration using the OmegaConf library.
# Intinalize and save configuratioon using teh OmegaConf library.
try:
# init and save configs
configs = [OmegaConf.load(cfg) for cfg in opt.base]
@@ -689,7 +687,7 @@ if __name__ == "__main__":
config.model["params"].update({"ckpt": ckpt})
rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"]))
model = LatentDiffusion(**config.model.get("params", dict()))
model = instantiate_from_config(config.model)
# trainer and callbacks
trainer_kwargs = dict()
@@ -698,7 +696,7 @@ if __name__ == "__main__":
# These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger.
default_logger_cfgs = {
"wandb": {
#"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
"target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
"params": {
"name": nowname,
"save_dir": logdir,
@@ -707,7 +705,7 @@ if __name__ == "__main__":
}
},
"tensorboard": {
#"target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger",
"target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger",
"params": {
"save_dir": logdir,
"name": "diff_tb",
@@ -720,32 +718,30 @@ if __name__ == "__main__":
default_logger_cfg = default_logger_cfgs["tensorboard"]
if "logger" in lightning_config:
logger_cfg = lightning_config.logger
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = WandbLogger(**logger_cfg.get("params", dict()))
else:
logger_cfg = default_logger_cfg
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = TensorBoardLogger(**logger_cfg.get("params", dict()))
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# config the strategy, defualt is ddp
if "strategy" in trainer_config:
strategy_cfg = trainer_config["strategy"]
trainer_kwargs["strategy"] = ColossalAIStrategy(**strategy_cfg.get("params", dict()))
strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"]
else:
strategy_cfg = {
#"target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy",
"target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy",
"params": {
"find_unused_parameters": False
}
}
trainer_kwargs["strategy"] = DDPStrategy(**strategy_cfg.get("params", dict()))
trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
# Set up ModelCheckpoint callback to save best models
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
#"target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint",
"target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{epoch:06}",
@@ -763,13 +759,13 @@ if __name__ == "__main__":
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
if version.parse(pl.__version__) < version.parse('1.4.0'):
trainer_kwargs["checkpoint_callback"] = ModelCheckpoint(**modelckpt_cfg.get("params", dict()))
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
# Set up various callbacks, including logging, learning rate monitoring, and CUDA management
# add callback which sets up log directory
default_callbacks_cfg = {
"setup_callback": { # callback to set up the training
#"target": "main.SetupCallback",
"target": "main.SetupCallback",
"params": {
"resume": opt.resume, # resume training if applicable
"now": now,
@@ -781,7 +777,7 @@ if __name__ == "__main__":
}
},
"image_logger": { # callback to log image data
#"target": "main.ImageLogger",
"target": "main.ImageLogger",
"params": {
"batch_frequency": 750, # how frequently to log images
"max_images": 4, # maximum number of images to log
@@ -789,14 +785,14 @@ if __name__ == "__main__":
}
},
"learning_rate_logger": { # callback to log learning rate
#"target": "main.LearningRateMonitor",
"target": "main.LearningRateMonitor",
"params": {
"logging_interval": "step", # logging frequency (either 'step' or 'epoch')
# "log_momentum": True # whether to log momentum (currently commented out)
}
},
"cuda_callback": { # callback to handle CUDA-related operations
#"target": "main.CUDACallback"
"target": "main.CUDACallback"
},
}
@@ -814,7 +810,7 @@ if __name__ == "__main__":
'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
default_metrics_over_trainsteps_ckpt_dict = {
'metrics_over_trainsteps_checkpoint': {
#"target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint',
"target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint',
'params': {
"dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
"filename": "{epoch:06}-{step:09}",
@@ -829,35 +825,15 @@ if __name__ == "__main__":
# Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
#Instantiate items according to the configs
trainer_kwargs.setdefault("callbacks", [])
if "setup_callback" in callbacks_cfg:
setup_callback_config = callbacks_cfg["setup_callback"]
trainer_kwargs["callbacks"].append(SetupCallback(**setup_callback_config.get("params", dict())))
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
if "image_logger" in callbacks_cfg:
image_logger_config = callbacks_cfg["image_logger"]
trainer_kwargs["callbacks"].append(ImageLogger(**image_logger_config.get("params", dict())))
if "learning_rate_logger" in callbacks_cfg:
learning_rate_logger_config = callbacks_cfg["learning_rate_logger"]
trainer_kwargs["callbacks"].append(LearningRateMonitor(**learning_rate_logger_config.get("params", dict())))
if "cuda_callback" in callbacks_cfg:
cuda_callback_config = callbacks_cfg["cuda_callback"]
trainer_kwargs["callbacks"].append(CUDACallback(**cuda_callback_config.get("params", dict())))
if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
metrics_over_config = callbacks_cfg['metrics_over_trainsteps_checkpoint']
trainer_kwargs["callbacks"].append(ModelCheckpoint(**metrics_over_config.get("params", dict())))
#trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
# Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir
# Create a data module based on the configuration file
data = DataModuleFromConfig(**config.data.get("params", dict()))
data = instantiate_from_config(config.data)
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
# calling these ourselves should not be necessary but it is.
# lightning still takes care of proper multiprocessing though