import argparse import os from pathlib import Path import h5py import numpy as np import json import robosuite import robosuite.utils.transform_utils as T import robosuite.macros as macros import init_path import libero.libero.utils.utils as libero_utils import cv2 from PIL import Image from robosuite.utils import camera_utils from libero.libero.envs import * from libero.libero import get_libero_path def main(): parser = argparse.ArgumentParser() parser.add_argument("--demo-file", default="demo.hdf5") parser.add_argument( "--use-actions", action="store_true", ) parser.add_argument("--use-camera-obs", action="store_true") parser.add_argument( "--dataset-path", type=str, default="datasets/", ) parser.add_argument( "--dataset-name", type=str, default="training_set", ) parser.add_argument("--no-proprio", action="store_true") parser.add_argument( "--use-depth", action="store_true", ) args = parser.parse_args() hdf5_path = args.demo_file f = h5py.File(hdf5_path, "r") env_name = f["data"].attrs["env"] env_args = f["data"].attrs["env_info"] env_kwargs = json.loads(f["data"].attrs["env_info"]) problem_info = json.loads(f["data"].attrs["problem_info"]) problem_info["domain_name"] problem_name = problem_info["problem_name"] language_instruction = problem_info["language_instruction"] # list of all demonstrations episodes demos = list(f["data"].keys()) bddl_file_name = f["data"].attrs["bddl_file_name"] bddl_file_dir = os.path.dirname(bddl_file_name) replace_bddl_prefix = "/".join(bddl_file_dir.split("bddl_files/")[:-1] + "bddl_files") hdf5_path = os.path.join(get_libero_path("datasets"), bddl_file_dir.split("bddl_files/")[-1].replace(".bddl", "_demo.hdf5")) output_parent_dir = Path(hdf5_path).parent output_parent_dir.mkdir(parents=True, exist_ok=True) h5py_f = h5py.File(hdf5_path, "w") grp = h5py_f.create_group("data") grp.attrs["env_name"] = env_name grp.attrs["problem_info"] = f["data"].attrs["problem_info"] grp.attrs["macros_image_convention"] = macros.IMAGE_CONVENTION libero_utils.update_env_kwargs( env_kwargs, bddl_file_name=bddl_file_name, has_renderer=not args.use_camera_obs, has_offscreen_renderer=args.use_camera_obs, ignore_done=True, use_camera_obs=args.use_camera_obs, camera_depths=args.use_depth, camera_names=[ "robot0_eye_in_hand", "agentview", ], reward_shaping=True, control_freq=20, camera_heights=128, camera_widths=128, camera_segmentations=None, ) grp.attrs["bddl_file_name"] = bddl_file_name grp.attrs["bddl_file_content"] = open(bddl_file_name, "r").read() print(grp.attrs["bddl_file_content"]) env = TASK_MAPPING[problem_name]( **env_kwargs, ) env_args = { "type": 1, "env_name": env_name, "problem_name": problem_name, "bddl_file": f["data"].attrs["bddl_file_name"], "env_kwargs": env_kwargs, } grp.attrs["env_args"] = json.dumps(env_args) print(grp.attrs["env_args"]) total_len = 0 demos = demos cap_index = 5 for (i, ep) in enumerate(demos): print("Playing back random episode... (press ESC to quit)") # # select an episode randomly # read the model xml, using the metadata stored in the attribute for this episode model_xml = f["data/{}".format(ep)].attrs["model_file"] reset_success = False while not reset_success: try: env.reset() reset_success = True except: continue model_xml = libero_utils.postprocess_model_xml(model_xml, {}) if not args.use_camera_obs: env.viewer.set_camera(0) # load the flattened mujoco states states = f["data/{}/states".format(ep)][()] actions = np.array(f["data/{}/actions".format(ep)][()]) num_actions = actions.shape[0] init_idx = 0 env.reset_from_xml_string(model_xml) env.sim.reset() env.sim.set_state_from_flattened(states[init_idx]) env.sim.forward() model_xml = env.sim.model.get_xml() ee_states = [] gripper_states = [] joint_states = [] robot_states = [] agentview_images = [] eye_in_hand_images = [] agentview_depths = [] eye_in_hand_depths = [] agentview_seg = {0: [], 1: [], 2: [], 3: [], 4: []} rewards = [] dones = [] valid_index = [] for j, action in enumerate(actions): obs, reward, done, info = env.step(action) if j < num_actions - 1: # ensure that the actions deterministically lead to the same recorded states state_playback = env.sim.get_state().flatten() # assert(np.all(np.equal(states[j + 1], state_playback))) err = np.linalg.norm(states[j + 1] - state_playback) if err > 0.01: print( f"[warning] playback diverged by {err:.2f} for ep {ep} at step {j}" ) # Skip recording because the force sensor is not stable in # the beginning if j < cap_index: continue valid_index.append(j) if not args.no_proprio: if "robot0_gripper_qpos" in obs: gripper_states.append(obs["robot0_gripper_qpos"]) joint_states.append(obs["robot0_joint_pos"]) ee_states.append( np.hstack( ( obs["robot0_eef_pos"], T.quat2axisangle(obs["robot0_eef_quat"]), ) ) ) robot_states.append(env.get_robot_state_vector(obs)) if args.use_camera_obs: if args.use_depth: agentview_depths.append(obs["agentview_depth"]) eye_in_hand_depths.append(obs["robot0_eye_in_hand_depth"]) agentview_images.append(obs["agentview_image"]) eye_in_hand_images.append(obs["robot0_eye_in_hand_image"]) else: env.render() # end of one trajectory states = states[valid_index] actions = actions[valid_index] dones = np.zeros(len(actions)).astype(np.uint8) dones[-1] = 1 rewards = np.zeros(len(actions)).astype(np.uint8) rewards[-1] = 1 print(len(actions), len(agentview_images)) assert len(actions) == len(agentview_images) print(len(actions)) ep_data_grp = grp.create_group(f"demo_{i}") obs_grp = ep_data_grp.create_group("obs") if not args.no_proprio: obs_grp.create_dataset( "gripper_states", data=np.stack(gripper_states, axis=0) ) obs_grp.create_dataset("joint_states", data=np.stack(joint_states, axis=0)) obs_grp.create_dataset("ee_states", data=np.stack(ee_states, axis=0)) obs_grp.create_dataset("ee_pos", data=np.stack(ee_states, axis=0)[:, :3]) obs_grp.create_dataset("ee_ori", data=np.stack(ee_states, axis=0)[:, 3:]) obs_grp.create_dataset("agentview_rgb", data=np.stack(agentview_images, axis=0)) obs_grp.create_dataset( "eye_in_hand_rgb", data=np.stack(eye_in_hand_images, axis=0) ) if args.use_depth: obs_grp.create_dataset( "agentview_depth", data=np.stack(agentview_depths, axis=0) ) obs_grp.create_dataset( "eye_in_hand_depth", data=np.stack(eye_in_hand_depths, axis=0) ) ep_data_grp.create_dataset("actions", data=actions) ep_data_grp.create_dataset("states", data=states) ep_data_grp.create_dataset("robot_states", data=np.stack(robot_states, axis=0)) ep_data_grp.create_dataset("rewards", data=rewards) ep_data_grp.create_dataset("dones", data=dones) ep_data_grp.attrs["num_samples"] = len(agentview_images) ep_data_grp.attrs["model_file"] = model_xml ep_data_grp.attrs["init_state"] = states[init_idx] total_len += len(agentview_images) grp.attrs["num_demos"] = len(demos) grp.attrs["total"] = total_len env.close() h5py_f.close() f.close() print("The created dataset is saved in the following path: ") print(hdf5_path) if __name__ == "__main__": main()