62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
import inspect
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from omegaconf import OmegaConf
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from lerobot.common.utils import get_safe_torch_device
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def make_policy(cfg):
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if cfg.policy.name == "tdmpc":
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from lerobot.common.policies.tdmpc.policy import TDMPCPolicy
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policy = TDMPCPolicy(
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cfg.policy, n_obs_steps=cfg.n_obs_steps, n_action_steps=cfg.n_action_steps, device=cfg.device
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)
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elif cfg.policy.name == "diffusion":
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from lerobot.common.policies.diffusion.policy import DiffusionPolicy
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policy = DiffusionPolicy(
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cfg=cfg.policy,
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cfg_device=cfg.device,
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cfg_noise_scheduler=cfg.noise_scheduler,
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cfg_rgb_model=cfg.rgb_model,
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cfg_obs_encoder=cfg.obs_encoder,
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cfg_optimizer=cfg.optimizer,
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cfg_ema=cfg.ema,
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# n_obs_steps=cfg.n_obs_steps,
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# n_action_steps=cfg.n_action_steps,
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**cfg.policy,
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)
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elif cfg.policy.name == "act":
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from lerobot.common.policies.act.configuration_act import ActionChunkingTransformerConfig
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from lerobot.common.policies.act.modeling_act import ActionChunkingTransformerPolicy
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expected_kwargs = set(inspect.signature(ActionChunkingTransformerConfig).parameters)
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assert set(cfg.policy).issuperset(
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expected_kwargs
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), f"Hydra config is missing arguments: {set(cfg.policy).difference(expected_kwargs)}"
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policy_cfg = ActionChunkingTransformerConfig(
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**{
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k: v
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for k, v in OmegaConf.to_container(cfg.policy, resolve=True).items()
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if k in expected_kwargs
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}
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)
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policy = ActionChunkingTransformerPolicy(policy_cfg)
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policy.to(get_safe_torch_device(cfg.device))
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else:
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raise ValueError(cfg.policy.name)
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if cfg.policy.pretrained_model_path:
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# TODO(rcadene): hack for old pretrained models from fowm
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if cfg.policy.name == "tdmpc" and "fowm" in cfg.policy.pretrained_model_path:
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if "offline" in cfg.policy.pretrained_model_path:
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policy.step[0] = 25000
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elif "final" in cfg.policy.pretrained_model_path:
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policy.step[0] = 100000
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else:
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raise NotImplementedError()
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policy.load(cfg.policy.pretrained_model_path)
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return policy
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