import dataclasses import enum import logging import os import pickle import socket import sys import time import traceback import dill import hydra import numpy as np import omegaconf import torch import torch.nn.functional as F import tyro from omegaconf import open_dict from openpi.policies import policy as _policy from openpi.policies import policy_config as _policy_config from openpi.serving import websocket_policy_server from openpi.training import config as _config from openpi.training.config import get_data_config from unified_video_action.common.pytorch_util import dict_apply from unified_video_action.policy.base_image_policy import BaseImagePolicy from unified_video_action.workspace.base_workspace import BaseWorkspace from umi.real_world.real_inference_util import get_real_obs_resolution language_latents = pickle.load(open("prepared_data/language_latents.pkl", "rb")) def echo_exception(): exc_type, exc_value, exc_traceback = sys.exc_info() tb_lines = traceback.format_exception(exc_type, exc_value, exc_traceback) return "".join(tb_lines) def smooth_action(act_out, window_size=3, pad_size=1): kernel = torch.ones(1, 1, window_size) / window_size kernel = kernel.to(act_out.device) act_out_padded = F.pad(act_out, (0, 0, pad_size, pad_size), mode="replicate") batch_size, timesteps, action_dim = act_out_padded.shape act_out_padded = act_out_padded.permute(0, 2, 1) act_out_padded = act_out_padded.reshape(-1, 1, timesteps) smoothed_act_out = F.conv1d(act_out_padded, kernel, padding=0) smoothed_act_out = smoothed_act_out.reshape(batch_size, action_dim, timesteps - 2 * pad_size) smoothed_act_out = smoothed_act_out.permute(0, 2, 1) return smoothed_act_out class EvalRealPolicyAdapter: """Adapter to wrap eval_real.py PolicyInferenceNode as a Policy interface.""" def __init__(self, ckpt_path: str, device: str, output_dir: str): self.ckpt_path = ckpt_path if not self.ckpt_path.endswith(".ckpt"): self.ckpt_path = os.path.join(self.ckpt_path, "checkpoints", "latest.ckpt") payload = torch.load(open(self.ckpt_path, "rb"), map_location="cpu", pickle_module=dill) self.cfg = payload["cfg"] with open_dict(self.cfg): if "autoregressive_model_params" in self.cfg.model.policy: self.cfg.model.policy.autoregressive_model_params.num_sampling_steps = "100" print("-----------------------------------------------") print("num_sampling_steps", self.cfg.model.policy.autoregressive_model_params.num_sampling_steps) print("-----------------------------------------------") cfg_path = self.ckpt_path.replace(".ckpt", ".yaml") with open(cfg_path, "w") as f: f.write(omegaconf.OmegaConf.to_yaml(self.cfg)) print(f"Exported config to {cfg_path}") print(f"Loading configure: {self.cfg.task.name}, workspace: {self.cfg.model._target_}, policy: {self.cfg.model.policy._target_}") self.obs_res = get_real_obs_resolution(self.cfg.task.shape_meta) self.device = torch.device(device) cls = hydra.utils.get_class(self.cfg.model._target_) self.workspace = cls(self.cfg, output_dir=output_dir) self.workspace: BaseWorkspace self.workspace.load_payload(payload, exclude_keys=None, include_keys=None) self.policy: BaseImagePolicy = self.workspace.model if self.cfg.training.use_ema: self.policy = self.workspace.ema_model print("Using EMA model") self.policy.eval().to(self.device) self.policy.reset() # Note: past_action_list is shared across connections, but since WebSocket # processing is sequential per connection, this should work in practice self.past_action_list = [] self._metadata = {"obs_resolution": self.obs_res} @property def metadata(self): return self._metadata def infer(self, obs: dict) -> dict: """Infer action from observation. Returns dict with 'actions' key.""" obs_dict_np = obs.copy() task_name = None if "task_name" in obs_dict_np: task_name = obs_dict_np["task_name"] print("task_name", obs_dict_np["task_name"]) del obs_dict_np["task_name"] if self.cfg.task.dataset.language_emb_model is not None and task_name: if "cup" in task_name: language_goal = language_latents["cup"] elif "towel" in task_name: language_goal = language_latents["towel"] elif "mouse" in task_name: language_goal = language_latents["mouse"] else: language_goal = None if language_goal is not None: language_goal = torch.tensor(language_goal).to(self.device) language_goal = language_goal.unsqueeze(0) print("task_name", task_name) else: language_goal = None with torch.no_grad(): obs_dict = dict_apply( obs_dict_np, lambda x: torch.from_numpy(x).unsqueeze(0).to(self.device) ) if self.cfg.name == "uva": result = self.policy.predict_action( obs_dict=obs_dict, language_goal=language_goal ) self.past_action_list.append(np.array(result["action"][0].cpu())) if len(self.past_action_list) > 2: self.past_action_list.pop(0) action = smooth_action(result["action_pred"].detach().to("cpu")).numpy()[0] else: result = self.policy.predict_action( obs_dict, language_goal=language_goal ) action = result["action_pred"][0].detach().to("cpu").numpy() print("action") del result del obs_dict return {"actions": action} class EnvMode(enum.Enum): """Supported environments.""" ALOHA = "aloha" ALOHA_SIM = "aloha_sim" DROID = "droid" LIBERO = "libero" REAL = "real" @dataclasses.dataclass class Checkpoint: """Load a policy from a trained checkpoint.""" # Training config name (e.g., "pi0_aloha_sim"). data_config: str # Checkpoint directory (e.g., "checkpoints/pi0_aloha_sim/exp/10000"). dir: str | None = None @dataclasses.dataclass class EvalRealCheckpoint: """Load a policy from eval_real.py style checkpoint.""" # Checkpoint path (directory or .ckpt file). dir: str # Device to run on. device: str = "cuda" # Output directory. output_dir: str = "." @dataclasses.dataclass class Default: """Use the default policy for the given environment.""" @dataclasses.dataclass class Args: """Arguments for the serve_policy script.""" # Environment to serve the policy for. This is only used when serving default policies. env: EnvMode = EnvMode.ALOHA_SIM # If provided, will be used in case the "prompt" key is not present in the data, or if the model doesn't have a default # prompt. default_prompt: str | None = None # Port to serve the policy on. port: int = 8012 # Record the policy's behavior for debugging. record: bool = False # Specifies how to load the policy. If not provided, the default policy for the environment will be used. policy: Checkpoint | EvalRealCheckpoint | Default = dataclasses.field(default_factory=Default) # use_vllm: bool = False # Default checkpoints that should be used for each environment. # DEFAULT_CHECKPOINT: dict[EnvMode, Checkpoint] = { # EnvMode.ALOHA: Checkpoint( # config="pi0_aloha", # dir="s3://openpi-assets/checkpoints/pi0_base", # ), # EnvMode.ALOHA_SIM: Checkpoint( # config="pi0_aloha_sim", # dir="s3://openpi-assets/checkpoints/pi0_aloha_sim", # ), # EnvMode.DROID: Checkpoint( # config="pi0_fast_droid", # dir="s3://openpi-assets/checkpoints/pi0_fast_droid", # ), # EnvMode.LIBERO: Checkpoint( # config="pi0_fast_libero", # dir="s3://openpi-assets/checkpoints/pi0_fast_libero", # ), # } # def create_default_policy(env: EnvMode, *, default_prompt: str | None = None) -> _policy.Policy: # """Create a default policy for the given environment.""" # if checkpoint := DEFAULT_CHECKPOINT.get(env): # return _policy_config.create_trained_policy( # _config.get_config(checkpoint.config), checkpoint.dir, default_prompt=default_prompt # ) # raise ValueError(f"Unsupported environment mode: {env}") def create_policy(args: Args): """Create a policy from the given arguments.""" match args.policy: case EvalRealCheckpoint(): return EvalRealPolicyAdapter( ckpt_path=args.policy.dir, device=args.policy.device, output_dir=args.policy.output_dir, ) case Checkpoint(): import pathlib import openpi.shared.normalize as _normalize # _train_config = _config.get_config(args.policy.config) _data_config: _config.DataConfig = get_data_config(args.policy.data_config) norm_stats = _data_config.norm_stats return _policy_config.create_trained_policy( _data_config, args.policy.dir, default_prompt=args.default_prompt, norm_stats=norm_stats, use_vllm=_data_config.inference_use_vllm ) case Default(): raise NotImplementedError("Default policies are not yet supported.") # return create_default_policy(args.env, default_prompt=args.default_prompt) def main(args: Args) -> None: policy = create_policy(args) policy_metadata = policy.metadata # Record the policy's behavior. if args.record: policy = _policy.PolicyRecorder(policy, "policy_records") # hostname = socket.gethostname() # local_ip = socket.gethostbyname(hostname) # logging.info("Creating server (host: %s, ip: %s)", hostname, local_ip) server = websocket_policy_server.WebsocketPolicyServer( policy=policy, host="0.0.0.0", port=args.port, metadata=policy_metadata, ) server.serve_forever() if __name__ == "__main__": logging.basicConfig(level=logging.INFO, force=True) main(tyro.cli(Args))