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train.py
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train.py
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import inspect
import hydra
import torch
from omegaconf import DictConfig, OmegaConf
from torch.utils.data import DataLoader
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.strategies.ddp import DDPStrategy
from src.bigearthnet_dataset.BEN_DataModule_LMDB_Encoder import BENDataSet
from src.args import parse_cfg
from src.augmentations import (
FullTransformPipeline,
NCropAugmentation,
build_transform_pipeline,
)
from src.csmae import CSMAE
from src.utils import Checkpointer
@hydra.main(version_base="1.2")
def main(cfg: DictConfig):
OmegaConf.set_struct(cfg, False)
cfg = parse_cfg(cfg)
seed_everything(cfg.seed)
# Instantiate CSMAE model
model = CSMAE(cfg)
model = model.to(memory_format=torch.channels_last)
# Build data augmentation pipeline
pipelines = []
for aug_cfg in cfg.augmentations:
pipelines.append(
NCropAugmentation(
build_transform_pipeline(cfg.data.dataset, aug_cfg, cfg), aug_cfg.num_crops
)
)
transform = FullTransformPipeline(pipelines)
# Prepare dataloaders
train_dataset = BENDataSet(
transform=transform,
root_dir=cfg.data.root_dir,
split_dir=cfg.data.split_dir,
split="train",
max_img_idx=cfg.data.get('max_img_idx', None),
img_size=(cfg.data.num_bands, cfg.data.img_size, cfg.data.img_size),
)
train_loader = DataLoader(
train_dataset,
batch_size=cfg.optimizer.batch_size,
shuffle=True,
num_workers=cfg.data.num_workers,
pin_memory=True,
drop_last=False,
)
# Build callbacks considering the train configuration
callbacks = []
ckpt = Checkpointer(
cfg,
logdir=cfg.checkpoint.dir,
)
callbacks.append(ckpt)
if cfg.wandb.enabled:
wandb_logger = WandbLogger(
name=cfg.name,
project=cfg.wandb.project,
entity=cfg.wandb.entity,
offline=cfg.wandb.offline,
)
wandb_logger.watch(model, log="gradients", log_freq=100)
wandb_logger.log_hyperparams(OmegaConf.to_container(cfg))
lr_monitor = LearningRateMonitor(logging_interval="step")
callbacks.append(lr_monitor)
# Run training
trainer_kwargs = OmegaConf.to_container(cfg)
valid_kwargs = inspect.signature(Trainer.__init__).parameters
trainer_kwargs = {name: trainer_kwargs[name] for name in valid_kwargs if name in trainer_kwargs}
trainer_kwargs.update(
{
"logger": wandb_logger if cfg.wandb.enabled else None,
"callbacks": callbacks,
"enable_checkpointing": False,
"strategy": DDPStrategy(find_unused_parameters=cfg.find_unused_parameters)
}
)
trainer = Trainer(
log_every_n_steps=10,
**trainer_kwargs,
num_sanity_val_steps=2,
)
trainer.fit(model, train_loader)
if __name__ == "__main__":
main()