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import time
import torch
import hydra
import pytorch_lightning as pl
from typing import Any
from hydra.core.config_store import ConfigStore
from omegaconf import OmegaConf
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks import ModelCheckpoint
from pathlib import Path
from dataclasses import dataclass
from .module import GenericModule
from .data.module import GenericDataModule
from .callbacks import EvalSaveCallback, ImageLoggerCallback
from .models.schema import ModelConfiguration, DINOConfiguration, ResNetConfiguration
from .data.schema import MIADataConfiguration, KITTIDataConfiguration, NuScenesDataConfiguration
@dataclass
class ExperimentConfiguration:
name: str
@dataclass
class Configuration:
model: ModelConfiguration
experiment: ExperimentConfiguration
data: Any
training: Any
cs = ConfigStore.instance()
# Store root configuration schema
cs.store(name="pretrain", node=Configuration)
cs.store(name="mapper_nuscenes", node=Configuration)
cs.store(name="mapper_kitti", node=Configuration)
# Store data configuration schema
cs.store(group="schema/data", name="mia",
node=MIADataConfiguration, package="data")
cs.store(group="schema/data", name="kitti", node=KITTIDataConfiguration, package="data")
cs.store(group="schema/data", name="nuscenes", node=NuScenesDataConfiguration, package="data")
cs.store(group="model/schema/backbone", name="dino", node=DINOConfiguration, package="model.image_encoder.backbone")
cs.store(group="model/schema/backbone", name="resnet", node=ResNetConfiguration, package="model.image_encoder.backbone")
@hydra.main(version_base=None, config_path="conf", config_name="pretrain")
def train(cfg: Configuration):
OmegaConf.resolve(cfg)
dm = GenericDataModule(cfg.data)
model = GenericModule(cfg)
exp_name_with_time = cfg.experiment.name + \
"_" + time.strftime("%Y-%m-%d_%H-%M-%S")
callbacks: list[pl.Callback]
if cfg.training.eval:
save_dir = Path(cfg.training.save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
callbacks = [
EvalSaveCallback(save_dir=save_dir)
]
logger = None
else:
callbacks = [
ImageLoggerCallback(num_classes=cfg.training.num_classes),
ModelCheckpoint(
monitor=cfg.training.checkpointing.monitor,
save_last=cfg.training.checkpointing.save_last,
save_top_k=cfg.training.checkpointing.save_top_k,
)
]
logger = WandbLogger(
name=exp_name_with_time,
id=exp_name_with_time,
entity="mappred-large",
project="map-pred-full-v3",
)
logger.watch(model, log="all", log_freq=500)
if cfg.training.checkpoint is not None:
state_dict = torch.load(cfg.training.checkpoint)['state_dict']
model.load_state_dict(state_dict, strict=False)
trainer_args = OmegaConf.to_container(cfg.training.trainer)
trainer_args['callbacks'] = callbacks
trainer_args['logger'] = logger
trainer = pl.Trainer(**trainer_args)
if cfg.training.eval:
trainer.test(model, datamodule=dm)
else:
trainer.fit(model, datamodule=dm)
if __name__ == "__main__":
pl.seed_everything(42)
torch.set_float32_matmul_precision("high")
train()
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