File size: 2,283 Bytes
912c7e2 | 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 | import hydra
import subprocess
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from composer.trainer import Trainer
from composer.loggers import WandBLogger
from composer.models import ComposerModel
from pfp import DEVICE, REPO_DIRS, DATA_DIRS, set_seeds
from pfp.data.dataset_pcd import RobotDatasetPcd
from pfp.data.dataset_images import RobotDatasetImages
@hydra.main(version_base=None, config_path="../conf", config_name="trainer_eval")
def main(cfg: OmegaConf):
# Download checkpoint if not present
ckpt_path = REPO_DIRS.CKPT / cfg.run_name
if not ckpt_path.exists():
subprocess.run(
[
"rsync",
"-hPrl",
f"chisari@rlgpu2:{ckpt_path}",
f"{REPO_DIRS.CKPT}/",
]
)
train_cfg = OmegaConf.load(ckpt_path / "config.yaml")
cfg = OmegaConf.merge(train_cfg, cfg)
print(OmegaConf.to_yaml(cfg))
set_seeds(cfg.seed)
data_path_valid = DATA_DIRS.PFP / cfg.task_name / "valid"
if cfg.obs_mode == "pcd":
dataset_valid = RobotDatasetPcd(data_path_valid, **cfg.dataset)
elif cfg.obs_mode == "rgb":
dataset_valid = RobotDatasetImages(data_path_valid, **cfg.dataset)
else:
raise ValueError(f"Unknown observation mode: {cfg.obs_mode}")
dataloader_valid = DataLoader(
dataset_valid,
shuffle=False,
**cfg.dataloader,
persistent_workers=True if cfg.dataloader.num_workers > 0 else False,
)
composer_model: ComposerModel = hydra.utils.instantiate(cfg.model)
wandb_logger = WandBLogger(
project="pfp-trainer-eval",
entity="rl-lab-chisari",
init_kwargs={
"config": OmegaConf.to_container(cfg),
"mode": "online" if cfg.log_wandb else "disabled",
},
)
trainer = Trainer(
model=composer_model,
eval_dataloader=dataloader_valid,
device="gpu" if DEVICE.type == "cuda" else "cpu",
loggers=[wandb_logger],
save_folder="ckpt/{run_name}",
run_name=cfg.run_name, # set this to continue training from previous ckpt
autoresume=True if cfg.run_name is not None else False,
)
trainer.eval()
return
if __name__ == "__main__":
main()
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