File size: 1,874 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 | import hydra
import wandb
import subprocess
from omegaconf import OmegaConf, open_dict
from pfp import set_seeds, REPO_DIRS
from pfp.envs.rlbench_runner import RLBenchRunner
from pfp.policy.base_policy import BasePolicy
from pfp.common.visualization import RerunViewer as RV
@hydra.main(version_base=None, config_path="../conf", config_name="eval")
def main(cfg: OmegaConf):
if not OmegaConf.has_resolver("eval"):
OmegaConf.register_new_resolver("eval", eval)
OmegaConf.resolve(cfg)
print(OmegaConf.to_yaml(cfg))
set_seeds(cfg.seed)
# Download checkpoint if not present
ckpt_path = REPO_DIRS.CKPT / cfg.policy.ckpt_name
if not ckpt_path.exists():
subprocess.run(
[
"rsync",
"-hPrl",
f"chisari@rlgpu2:{ckpt_path}",
f"{REPO_DIRS.CKPT}/",
]
)
with open_dict(cfg):
train_cfg = OmegaConf.load(ckpt_path / "config.yaml")
cfg.model = train_cfg.model
cfg.env_runner.env_config.task_name = train_cfg.task_name
cfg.env_runner.env_config.obs_mode = train_cfg.obs_mode
cfg.env_runner.env_config.use_pc_color = train_cfg.dataset.use_pc_color
cfg.env_runner.env_config.n_points = train_cfg.dataset.n_points
cfg.policy._target_ = train_cfg.model._target_ + ".load_from_checkpoint"
print(OmegaConf.to_yaml(cfg))
if cfg.env_runner.env_config.vis:
RV("pfp_evaluate")
wandb.init(
project="pfp-eval-rebuttal",
entity="rl-lab-chisari",
config=OmegaConf.to_container(cfg),
mode="online" if cfg.log_wandb else "disabled",
)
policy: BasePolicy = hydra.utils.instantiate(cfg.policy)
env_runner = RLBenchRunner(**cfg.env_runner)
_ = env_runner.run(policy)
wandb.finish()
return
if __name__ == "__main__":
main()
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