File size: 3,176 Bytes
f34af6f | 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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import os
import torch
import hydra
from omegaconf import DictConfig, OmegaConf
# Pytorch lightning imports
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from pytorch_lightning.loggers.wandb import WandbLogger
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
# from dataset.data import PdbDataModule
from dataset.classification_data import PdbDataModule
from models.classifier_wrapper_v2 import ClasfModule
from dataset.classification_data import get_dataloaders
from utils.experiments import get_pylogger, flatten_dict
import wandb
log = get_pylogger(__name__)
torch.set_float32_matmul_precision('high')
def _configure_wandb_env(exp_cfg: DictConfig):
"""Apply optional wandb environment overrides from YAML config."""
wandb_cfg = exp_cfg.wandb
if wandb_cfg.get("offline", False):
os.environ["WANDB_MODE"] = "offline"
wandb_dir = wandb_cfg.get("dir", None)
if wandb_dir:
os.environ["WANDB_DIR"] = wandb_dir
class ClassifierTrainer:
def __init__(self, *, cfg: DictConfig):
self._cfg = cfg
self._data_cfg = cfg.data
self._exp_cfg = cfg.experiment
self._datamodule: LightningDataModule = PdbDataModule(self._data_cfg)
# self.train_loader, self.val_loader = get_dataloaders(self._data_cfg)
self._model: LightningModule = ClasfModule(self._cfg)
def train(self):
callbacks = []
_configure_wandb_env(self._exp_cfg)
logger = WandbLogger(
**self._exp_cfg.wandb,
)
# Checkpoint directory
ckpt_dir = self._exp_cfg.checkpointer.dirpath
os.makedirs(ckpt_dir, exist_ok=True)
log.info(f"Checkpoints saved to {ckpt_dir}")
# Model Checkpoints
callbacks.append(ModelCheckpoint(**self._exp_cfg.checkpointer))
# Save config
cfg_path = os.path.join(ckpt_dir, 'config.yaml')
with open(cfg_path, 'w') as f:
OmegaConf.save(config=self._cfg, f=f.name)
cfg_dict = OmegaConf.to_container(self._cfg, resolve=True)
flat_cfg = dict(flatten_dict(cfg_dict))
if isinstance(logger.experiment.config, wandb.sdk.wandb_config.Config):
logger.experiment.config.update(flat_cfg)
num_devices = int(self._exp_cfg.get("num_devices", 1))
log.info(f"Using {num_devices} device(s)")
trainer = Trainer(
**self._exp_cfg.trainer,
callbacks=callbacks,
logger=logger,
use_distributed_sampler=False,
enable_progress_bar=True,
enable_model_summary=True,
devices=num_devices,
)
trainer.fit(
model=self._model,
# train_dataloaders=self.train_loader,
# val_dataloaders=self.val_loader,
datamodule=self._datamodule,
ckpt_path=self._exp_cfg.warm_start
)
@hydra.main(version_base=None, config_path="./configs", config_name="classifier.yaml")
def main(cfg: DictConfig):
exp = ClassifierTrainer(cfg=cfg)
exp.train()
if __name__ == "__main__":
main() |