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06c11b0 | 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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | import torch
import numpy
import random
import numpy as np
import cv2
import imageio
from pathlib import Path
from robomme.env_record_wrapper import BenchmarkEnvBuilder
class VideoRecorder:
BORDER_COLOR = (255, 0, 0)
BORDER_THICKNESS = 10
def __init__(self, fps: int = 30):
self.fps = fps
self.frames: list[np.ndarray] = []
@staticmethod
def _to_numpy(t) -> np.ndarray:
return t.cpu().numpy() if isinstance(t, torch.Tensor) else np.asarray(t)
@classmethod
def _make_frame(
cls,
front: np.ndarray | torch.Tensor,
wrist: np.ndarray | torch.Tensor,
is_video_demo: bool = False,
) -> np.ndarray:
frame = np.hstack([cls._to_numpy(front), cls._to_numpy(wrist)]).astype(np.uint8)
if is_video_demo:
h, w = frame.shape[:2]
cv2.rectangle(frame, (0, 0), (w, h), cls.BORDER_COLOR, cls.BORDER_THICKNESS)
return frame
def add_initial_obs(self, obs: dict):
rgb_list = obs["front_rgb_list"]
for i, (front, wrist) in enumerate(zip(rgb_list, obs["wrist_rgb_list"])):
self.frames.append(self._make_frame(front, wrist, is_video_demo=i < len(rgb_list) - 1))
def add_step_obs(self, obs: dict):
self.frames.append(self._make_frame(
obs["front_rgb_list"][-1], obs["wrist_rgb_list"][-1],
))
def save(self, file_path: str):
dir_path = Path(file_path).parent
dir_path.mkdir(parents=True, exist_ok=True)
imageio.mimsave(file_path, self.frames, fps=self.fps)
self.frames = []
class DummyModel:
def __init__(self, seed: int):
self.base_action = np.array(
[0.0, 0.0, 0.0, -np.pi / 2, 0.0, np.pi / 2, np.pi / 4, 1.0],
dtype=np.float32,
)
self.set_model_seed(seed)
def set_model_seed(self, seed: int):
# set model seed will not affect the env seed
# env seed is fixed internally
torch.manual_seed(seed)
numpy.random.seed(seed)
random.seed(seed)
self.seed = seed
def predict(self, *args, **kwargs):
noise = np.random.normal(0, 0.01, self.base_action.shape)
noise[..., -1:] = 0.0 # Preserve gripper action
return self.base_action + noise
TASKS = BenchmarkEnvBuilder.get_task_list()
MODEL_SEED = 7 # 7, 42, 0
dummy_model = DummyModel(seed=MODEL_SEED)
total_success = []
for task in TASKS:
env_builder = BenchmarkEnvBuilder(
env_id=task,
dataset="test",
action_space="joint_angle", # change this to your model's action space
max_steps=1300, # we set 1300 in MME-VLA experiments. The longest length of training data is @daiyp
)
episode_count = env_builder.get_episode_num()
for episode in range(2):
env = env_builder.make_env_for_episode(
episode,
include_maniskill_obs=True,
include_front_depth=True,
include_wrist_depth=True,
include_front_camera_extrinsic=True,
include_wrist_camera_extrinsic=True,
include_available_multi_choices=True,
include_front_camera_intrinsic=True,
include_wrist_camera_intrinsic=True,
)
obs, info = env.reset()
task_goal = info["task_goal"]
if isinstance(task_goal, list):
task_goal = task_goal[0] # you can take alternative task goals if you want
print(f"\nTask goal: {task_goal}")
recorder = VideoRecorder()
recorder.add_initial_obs(obs)
current_front_rgb = obs["front_rgb_list"][-1]
current_wrist_rgb = obs["wrist_rgb_list"][-1]
while True:
dummy_action = dummy_model.predict(current_front_rgb, current_wrist_rgb, task_goal)
obs, reward, terminated, truncated, info = env.step(dummy_action)
if info is not None and info.get("status") == "error":
print(f"Error: {info.get('error_message')}")
total_success.append(False)
break
if terminated or truncated:
outcome = info.get("status", "unknown")
print(f"Outcome of episode {episode} of task {task}: {outcome}")
total_success.append(outcome == "success")
break
current_front_rgb = obs["front_rgb_list"][-1]
current_wrist_rgb = obs["wrist_rgb_list"][-1]
recorder.add_step_obs(obs)
env.close()
recorder.save(file_path=f"rollout_videos/{task}_ep_{episode}_{outcome}_{task_goal}.mp4")
print(f"Evaluation completed.")
print(f"Success rate: {sum(total_success) / len(total_success)}") |