| import numpy as np |
| import torch |
| import hydra |
| import dill |
| import sys, os |
|
|
| current_file_path = os.path.abspath(__file__) |
| parent_dir = os.path.dirname(current_file_path) |
| sys.path.append(parent_dir) |
|
|
| from diffusion_policy.workspace.robotworkspace import RobotWorkspace |
| from diffusion_policy.env_runner.dp_runner import DPRunner |
|
|
| class DP: |
|
|
| def __init__(self, ckpt_file: str, n_obs_steps, n_action_steps): |
| self.policy = self.get_policy(ckpt_file, None, "cuda:0") |
| self.runner = DPRunner(n_obs_steps=n_obs_steps, n_action_steps=n_action_steps) |
|
|
| def update_obs(self, observation): |
| self.runner.update_obs(observation) |
| |
| def reset_obs(self): |
| self.runner.reset_obs() |
|
|
| def get_action(self, observation=None): |
| action = self.runner.get_action(self.policy, observation) |
| return action |
|
|
| def get_last_obs(self): |
| return self.runner.obs[-1] |
|
|
| def get_policy(self, checkpoint, output_dir, device): |
| |
| payload = torch.load(open(checkpoint, "rb"), pickle_module=dill) |
| cfg = payload["cfg"] |
| cls = hydra.utils.get_class(cfg._target_) |
| workspace = cls(cfg, output_dir=output_dir) |
| workspace: RobotWorkspace |
| workspace.load_payload(payload, exclude_keys=None, include_keys=None) |
|
|
| |
| policy = workspace.model |
| if cfg.training.use_ema: |
| policy = workspace.ema_model |
|
|
| device = torch.device(device) |
| policy.to(device) |
| policy.eval() |
|
|
| return policy |
|
|
