Instructions to use hchen657Naza/PWM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hchen657Naza/PWM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hchen657Naza/PWM", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 21,421 Bytes
23e1f91 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 | #!/usr/bin/env python
# -- coding: UTF-8 --
import os, re
import time
import numpy as np
import h5py
import argparse
import dm_env
import collections
from collections import deque
import rospy
from sensor_msgs.msg import JointState, Image
from nav_msgs.msg import Odometry
from cv_bridge import CvBridge
import cv2
import threading
import av
import logging
from pathlib import Path
def encode_video_frames(images: np.ndarray, dst: Path, fps: int, vcodec: str = "libx264",
pix_fmt: str = "yuv420p", g: int = 2, crf: int = 23, fast_decode: int = 0,
log_level: int = av.logging.ERROR, overwrite: bool = False) -> bytes:
if vcodec not in {"h264", "hevc", "libx264", "libx265", "libsvtav1"}:
raise ValueError(f"Unsupported codec {vcodec}")
video_path = Path(dst)
video_path.parent.mkdir(parents=True, exist_ok=overwrite)
if (vcodec in {"libsvtav1", "hevc", "libx265"}) and pix_fmt == "yuv444p":
pix_fmt = "yuv420p"
h, w, _ = images[0].shape
options = {}
for k, v in {"g": g, "crf": crf}.items():
if v is not None:
options[k] = str(v)
if fast_decode:
key = "svtav1-params" if vcodec == "libsvtav1" else "tune"
options[key] = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode"
if log_level is not None:
logging.getLogger("libav").setLevel(log_level)
with av.open(str(video_path), "w") as out:
stream = out.add_stream(vcodec, fps, options=options)
stream.pix_fmt, stream.width, stream.height = pix_fmt, w, h
for i, img in enumerate(images):
frame = av.VideoFrame.from_ndarray(img, format="rgb24")
for pkt in stream.encode(frame):
out.mux(pkt)
if (i + 1) % 100 == 0 or i == len(images) - 1:
print(f"Encoded frame {i+1}")
for pkt in stream.encode():
out.mux(pkt)
if log_level is not None:
av.logging.restore_default_callback()
if not video_path.exists():
raise OSError(f"Video encoding failed: {video_path}")
def create_video_from_images(images, output_path, fps=30, codec="libx264", quality=23):
if not images:
raise ValueError("No image data")
print(f"Start encoding video, codec: {codec} CRF: {quality}")
encode_video_frames(np.asarray(images), Path(output_path), fps=fps, vcodec=codec, crf=quality, overwrite=True)
print(f"Video saved to: {output_path}")
# ββ save_data βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def save_data(args, timesteps, actions, dataset_path):
data_size = len(actions)
data_dict = {k: [] for k in [
'/observations/qpos', '/observations/qvel', '/observations/effort',
'/action', '/base_action']}
video_images = {cam: [] for cam in args.camera_names}
assert args.export_video, "Please set --export_video to enable video export"
while actions:
action = actions.pop(0)
ts = timesteps.pop(0)
for k in ['qpos', 'qvel', 'effort']:
data_dict[f'/observations/{k}'].append(ts.observation[k])
data_dict['/action'].append(action)
data_dict['/base_action'].append(ts.observation['base_vel'])
for cam in args.camera_names:
img = ts.observation['images'][cam]
if args.export_video:
video_img = img if img.shape[2] == 3 else cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if video_img.dtype != np.uint8:
video_img = (video_img * 255).astype(np.uint8) if video_img.max() <= 1.0 else video_img.astype(np.uint8)
video_images[cam].append(video_img)
t0 = time.time()
with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024**2*2) as root:
root.attrs['sim'], root.attrs['compress'] = False, False
obs = root.create_group('observations')
for k in ['qpos', 'qvel', 'effort']:
obs.create_dataset(k, (data_size, 14))
root.create_dataset('action', (data_size, 14))
root.create_dataset('base_action', (data_size, 2))
for name, arr in data_dict.items():
root[name][...] = arr
print(f'\033[32m\nSaving: {time.time() - t0:.1f} secs. %s \033[0m\n' % dataset_path)
if args.export_video and data_size:
print('\033[33m\nExporting videos...\033[0m')
video_dir = os.path.join(os.path.dirname(dataset_path), "video")
os.makedirs(video_dir, exist_ok=True)
for cam in args.camera_names:
if video_images[cam]:
try:
cam_dir = os.path.join(video_dir, cam)
os.makedirs(cam_dir, exist_ok=True)
episode_idx = os.path.basename(dataset_path).split('_')[-1]
video_path = os.path.join(cam_dir, f"episode_{episode_idx}.mp4")
print(f"Exporting video for camera {cam}: {video_path}")
create_video_from_images(video_images[cam], video_path,
fps=args.video_fps, codec=args.video_codec, quality=args.video_quality)
print(f'\033[32mβ
Camera {cam} video exported: {video_path}\033[0m')
except Exception as e:
print(f'\033[31mβ Camera {cam} video export failed: {e}\033[0m')
else:
print(f'\033[33mβ οΈ Camera {cam} has no image data, skipping video export\033[0m')
print(f'\033[32m\nVideo export done: {time.time() - t0:.1f} secs\033[0m')
# ββ RosOperator βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
from pynput import keyboard
class RosOperator:
def __init__(self, args):
self.args = args
self.stop_flag = False
self.bridge = CvBridge()
self.init_deques()
self.init_ros()
def init_deques(self):
self.img_left_deque = deque()
self.img_right_deque = deque()
self.img_front_deque = deque()
self.img_left_depth_deque = deque()
self.img_right_depth_deque = deque()
self.img_front_depth_deque = deque()
self.master_arm_left_deque = deque()
self.master_arm_right_deque = deque()
self.puppet_arm_left_deque = deque()
self.puppet_arm_right_deque = deque()
self.robot_base_deque = deque()
def keyboard_listener(self):
def on_press(key):
if key == keyboard.Key.space:
self.stop_flag = True
print("\033[35m>>> Space key pressed, stopping collection and saving...\033[0m")
return False
with keyboard.Listener(on_press=on_press) as listener:
listener.join()
def img_left_callback(self, msg):
if len(self.img_left_deque) >= 2000: self.img_left_deque.popleft()
self.img_left_deque.append(msg)
def img_right_callback(self, msg):
if len(self.img_right_deque) >= 2000: self.img_right_deque.popleft()
self.img_right_deque.append(msg)
def img_front_callback(self, msg):
if len(self.img_front_deque) >= 2000: self.img_front_deque.popleft()
self.img_front_deque.append(msg)
def img_left_depth_callback(self, msg):
if len(self.img_left_depth_deque) >= 2000: self.img_left_depth_deque.popleft()
self.img_left_depth_deque.append(msg)
def img_right_depth_callback(self, msg):
if len(self.img_right_depth_deque) >= 2000: self.img_right_depth_deque.popleft()
self.img_right_depth_deque.append(msg)
def img_front_depth_callback(self, msg):
if len(self.img_front_depth_deque) >= 2000: self.img_front_depth_deque.popleft()
self.img_front_depth_deque.append(msg)
def master_arm_left_callback(self, msg):
if len(self.master_arm_left_deque) >= 2000: self.master_arm_left_deque.popleft()
self.master_arm_left_deque.append(msg)
def master_arm_right_callback(self, msg):
if len(self.master_arm_right_deque) >= 2000: self.master_arm_right_deque.popleft()
self.master_arm_right_deque.append(msg)
def puppet_arm_left_callback(self, msg):
if len(self.puppet_arm_left_deque) >= 2000: self.puppet_arm_left_deque.popleft()
self.puppet_arm_left_deque.append(msg)
def puppet_arm_right_callback(self, msg):
if len(self.puppet_arm_right_deque) >= 2000: self.puppet_arm_right_deque.popleft()
self.puppet_arm_right_deque.append(msg)
def robot_base_callback(self, msg):
if len(self.robot_base_deque) >= 2000: self.robot_base_deque.popleft()
self.robot_base_deque.append(msg)
def init_ros(self):
rospy.init_node('record_episodes', anonymous=True)
rospy.Subscriber(self.args.img_left_topic, Image, self.img_left_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.img_right_topic, Image, self.img_right_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.img_front_topic, Image, self.img_front_callback, queue_size=1000, tcp_nodelay=True)
if self.args.use_depth_image:
rospy.Subscriber(self.args.img_left_depth_topic, Image, self.img_left_depth_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.img_right_depth_topic, Image, self.img_right_depth_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.img_front_depth_topic, Image, self.img_front_depth_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.master_arm_left_topic, JointState, self.master_arm_left_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.master_arm_right_topic, JointState, self.master_arm_right_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.puppet_arm_left_topic, JointState, self.puppet_arm_left_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.puppet_arm_right_topic, JointState, self.puppet_arm_right_callback, queue_size=1000, tcp_nodelay=True)
rospy.Subscriber(self.args.robot_base_topic, Odometry, self.robot_base_callback, queue_size=1000, tcp_nodelay=True)
def process(self):
timesteps, actions = [], []
count = 0
rate = rospy.Rate(self.args.frame_rate)
print("\033[36m>>> Collection started. Press [Space] to stop and save early...\033[0m")
# qpos change detection variables (left/right arm separately)
last_qpos_left, last_qpos_right = None, None
consecutive_unchanged_count_left, consecutive_unchanged_count_right = 0, 0
UNCHANGED_THRESHOLD = 100 # warn after 100 consecutive unchanged frames
# start background keyboard listener thread
threading.Thread(target=self.keyboard_listener, daemon=True).start()
while (count < self.args.max_timesteps + 1) and not rospy.is_shutdown() and not self.stop_flag:
result = self.get_frame()
if not result:
rate.sleep()
continue
count += 1
(img_front, img_left, img_right, img_front_depth, img_left_depth, img_right_depth,
puppet_arm_left, puppet_arm_right, master_arm_left, master_arm_right, robot_base) = result
image_dict = {self.args.camera_names[0]: img_front,
self.args.camera_names[1]: img_left,
self.args.camera_names[2]: img_right}
obs = collections.OrderedDict()
obs['images'] = image_dict
if self.args.use_depth_image:
obs['images_depth'] = {self.args.camera_names[0]: img_front_depth,
self.args.camera_names[1]: img_left_depth,
self.args.camera_names[2]: img_right_depth}
obs['qpos'] = np.concatenate((puppet_arm_left.position, puppet_arm_right.position))
obs['qvel'] = np.concatenate((puppet_arm_left.velocity, puppet_arm_right.velocity))
obs['effort'] = np.concatenate((puppet_arm_left.effort, puppet_arm_right.effort))
obs['base_vel'] = [robot_base.twist.twist.linear.x, robot_base.twist.twist.angular.z] if self.args.use_robot_base else [0.0, 0.0]
# left arm qpos change detection
current_qpos_left = puppet_arm_left.position
if last_qpos_left is not None and np.array_equal(current_qpos_left, last_qpos_left):
consecutive_unchanged_count_left += 1
else:
consecutive_unchanged_count_left = 0
if consecutive_unchanged_count_left >= UNCHANGED_THRESHOLD:
print(f"\033[33mβ οΈ Warning: left arm 'position' unchanged for {consecutive_unchanged_count_left} consecutive frames.\033[0m")
last_qpos_left = current_qpos_left
# right arm qpos change detection
current_qpos_right = puppet_arm_right.position
if last_qpos_right is not None and np.array_equal(current_qpos_right, last_qpos_right):
consecutive_unchanged_count_right += 1
else:
consecutive_unchanged_count_right = 0
if consecutive_unchanged_count_right >= UNCHANGED_THRESHOLD:
print(f"\033[33mβ οΈ Warning: right arm 'position' unchanged for {consecutive_unchanged_count_right} consecutive frames.\033[0m")
last_qpos_right = current_qpos_right
if count == 1:
timesteps.append(dm_env.TimeStep(dm_env.StepType.FIRST, None, None, obs))
continue
timesteps.append(dm_env.TimeStep(dm_env.StepType.MID, None, None, obs))
left_action = np.concatenate((puppet_arm_left.position[:6], [master_arm_left.position[6]]))
right_action = np.concatenate((puppet_arm_right.position[:6], [master_arm_right.position[6]]))
actions.append(np.concatenate((left_action, right_action)))
print("Frame data: ", count)
rate.sleep()
print(f"\n>>> Collection finished, {len(actions)} frames total. Saving...")
return timesteps, actions
def get_frame(self):
if len(self.img_left_deque) == 0 or len(self.img_right_deque) == 0 or len(self.img_front_deque) == 0:
return False
if self.args.use_depth_image and (len(self.img_left_depth_deque) == 0 or len(self.img_right_depth_deque) == 0 or len(self.img_front_depth_deque) == 0):
return False
frame_time = min([self.img_left_deque[-1].header.stamp.to_sec(),
self.img_right_deque[-1].header.stamp.to_sec(),
self.img_front_deque[-1].header.stamp.to_sec()])
if self.args.use_depth_image:
frame_time = min(frame_time,
self.img_left_depth_deque[-1].header.stamp.to_sec(),
self.img_right_depth_deque[-1].header.stamp.to_sec(),
self.img_front_depth_deque[-1].header.stamp.to_sec())
for dq in [self.img_left_deque, self.img_right_deque, self.img_front_deque,
self.master_arm_left_deque, self.master_arm_right_deque,
self.puppet_arm_left_deque, self.puppet_arm_right_deque]:
if not dq or dq[-1].header.stamp.to_sec() < frame_time:
return False
if self.args.use_depth_image:
for dq in [self.img_left_depth_deque, self.img_right_depth_deque, self.img_front_depth_deque]:
if not dq or dq[-1].header.stamp.to_sec() < frame_time:
return False
if self.args.use_robot_base and (not self.robot_base_deque or self.robot_base_deque[-1].header.stamp.to_sec() < frame_time):
return False
def pop(dq):
while dq[0].header.stamp.to_sec() < frame_time:
dq.popleft()
return dq.popleft()
img_left = self.bridge.imgmsg_to_cv2(pop(self.img_left_deque), 'passthrough')
img_right = self.bridge.imgmsg_to_cv2(pop(self.img_right_deque), 'passthrough')
img_front = self.bridge.imgmsg_to_cv2(pop(self.img_front_deque), 'passthrough')
master_arm_left = pop(self.master_arm_left_deque)
master_arm_right = pop(self.master_arm_right_deque)
puppet_arm_left = pop(self.puppet_arm_left_deque)
puppet_arm_right = pop(self.puppet_arm_right_deque)
img_left_depth = img_right_depth = img_front_depth = None
if self.args.use_depth_image:
img_left_depth = cv2.copyMakeBorder(self.bridge.imgmsg_to_cv2(pop(self.img_left_depth_deque), 'passthrough'), 40, 40, 0, 0, cv2.BORDER_CONSTANT, value=0)
img_right_depth = cv2.copyMakeBorder(self.bridge.imgmsg_to_cv2(pop(self.img_right_depth_deque), 'passthrough'), 40, 40, 0, 0, cv2.BORDER_CONSTANT, value=0)
img_front_depth = cv2.copyMakeBorder(self.bridge.imgmsg_to_cv2(pop(self.img_front_depth_deque), 'passthrough'), 40, 40, 0, 0, cv2.BORDER_CONSTANT, value=0)
robot_base = None
if self.args.use_robot_base:
robot_base = pop(self.robot_base_deque)
return (img_front, img_left, img_right, img_front_depth, img_left_depth, img_right_depth,
puppet_arm_left, puppet_arm_right, master_arm_left, master_arm_right, robot_base)
# ββ Argument parsing ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_dir', type=str, default="./data")
parser.add_argument('--task_name', type=str, default="aloha_mobile_dummy")
parser.add_argument('-e', '--episode_idx', type=int, default=None)
parser.add_argument('--max_timesteps', type=int, default=500)
parser.add_argument('--camera_names', nargs='+', default=['cam_high', 'cam_left_wrist', 'cam_right_wrist'])
parser.add_argument('--img_front_topic', default='/camera_f/color/image_raw')
parser.add_argument('--img_left_topic', default='/camera_l/color/image_raw')
parser.add_argument('--img_right_topic', default='/camera_r/color/image_raw')
parser.add_argument('--img_front_depth_topic', default='/camera_f/depth/image_raw')
parser.add_argument('--img_left_depth_topic', default='/camera_l/depth/image_raw')
parser.add_argument('--img_right_depth_topic', default='/camera_r/depth/image_raw')
parser.add_argument('--master_arm_left_topic', default='/master/joint_left')
parser.add_argument('--master_arm_right_topic', default='/master/joint_right')
parser.add_argument('--puppet_arm_left_topic', default='/puppet/joint_left')
parser.add_argument('--puppet_arm_right_topic', default='/puppet/joint_right')
parser.add_argument('--robot_base_topic', default='/odom')
parser.add_argument('--use_robot_base', type=bool, default=False)
parser.add_argument('--use_depth_image', type=bool, default=False)
parser.add_argument('--frame_rate', type=int, default=30)
parser.add_argument('--export_video', action='store_true', help='Enable video export')
parser.add_argument('--video_fps', type=int, default=30)
parser.add_argument('--video_codec', choices=['libx264', 'libx265', 'libsvtav1'], default='libx264')
parser.add_argument('--video_quality', type=int, default=23, help='CRF value; lower = higher quality')
args = parser.parse_args()
# auto-compute episode_idx if not specified
if args.episode_idx is None:
dataset_dir = os.path.join(args.dataset_dir, args.task_name)
episode_indices = []
if os.path.exists(dataset_dir) and os.path.isdir(dataset_dir):
pattern = re.compile(r'^episode_(\d+)\.hdf5$')
for filename in os.listdir(dataset_dir):
match = pattern.match(filename)
if match:
episode_indices.append(int(match.group(1)))
if episode_indices:
args.episode_idx = max(episode_indices) + 1
print(f"\033[34m>>> Found {len(episode_indices)} existing episodes, next index: {args.episode_idx}\033[0m")
else:
args.episode_idx = 0
print(f"\033[34m>>> No existing episodes found, starting from index 0\033[0m")
return args
# ββ main ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def main():
args = get_arguments()
ros_operator = RosOperator(args)
timesteps, actions = ros_operator.process()
if len(actions) == 0:
print("\033[31m\nNo data collected, skipping save.\033[0m")
return
dataset_dir = os.path.join(args.dataset_dir, args.task_name)
os.makedirs(dataset_dir, exist_ok=True)
dataset_path = os.path.join(dataset_dir, f"episode_{args.episode_idx}")
save_data(args, timesteps, actions, dataset_path)
print("\033[32m>>> Saved to:", dataset_path + ".hdf5\033[0m")
if __name__ == '__main__':
main()
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