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7eb3f10 | 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 | import random
import itertools
from typing import Tuple, Dict, List
import pickle
from pathlib import Path
import json
import blosc
from tqdm import tqdm
import tap
import torch
import numpy as np
import einops
from rlbench.demo import Demo
from utils.utils_with_rlbench import (
RLBenchEnv,
keypoint_discovery,
obs_to_attn,
obs_to_attn_right,
obs_to_attn_left,
transform,
)
class Arguments(tap.Tap):
data_dir: Path = Path(__file__).parent / "c2farm"
seed: int = 2
tasks: Tuple[str, ...] = ("stack_wine",)
cameras: Tuple[str, ...] = ("over_shoulder_left", "over_shoulder_right", "overhead", "wrist_right", "wrist_left", "front")
image_size: str = "256,256"
output: Path = Path(__file__).parent / "datasets"
max_variations: int = 199
offset: int = 0
num_workers: int = 0
store_intermediate_actions: int = 1
def get_attn_indices_from_demo(
task_str: str, demo: Demo, cameras: Tuple[str, ...], Arm: str
) -> List[Dict[str, Tuple[int, int]]]:
frames = keypoint_discovery(demo)
frames.insert(0, 0)
if Arm == 'right':
right_cameras = ( "over_shoulder_right", "overhead", "wrist_right", "front")
return [{cam: obs_to_attn_right(demo[f], cam) for cam in right_cameras} for f in frames]
else:
left_cameras = ("over_shoulder_left", "overhead", "wrist_left", "front")
return [{cam: obs_to_attn_left(demo[f], cam) for cam in left_cameras} for f in frames]
def get_observation(task_str: str, variation: int,
episode: int, env: RLBenchEnv,
store_intermediate_actions: bool):
demos = env.get_demo(task_str, variation, episode)
demo = demos[0]
key_frame = keypoint_discovery(demo)
key_frame.insert(0, 0)
# keyframe_state_ls = []
right_keyframe_state_ls = []
left_keyframe_state_ls = []
right_keyframe_action_ls = []
left_keyframe_action_ls = []
right_intermediate_action_ls = []
left_intermediate_action_ls = []
for i in range(len(key_frame)):
right_state, right_action = env.get_obs_action_right(demo._observations[key_frame[i]])
left_state, left_action = env.get_obs_action_left(demo._observations[key_frame[i]])
# state = transform(state)
right_state = transform(right_state)
left_state = transform(left_state)
# keyframe_state_ls.append(state.unsqueeze(0))
right_keyframe_state_ls.append(right_state.unsqueeze(0))
left_keyframe_state_ls.append(left_state.unsqueeze(0))
right_keyframe_action_ls.append(right_action.unsqueeze(0))
left_keyframe_action_ls.append(left_action.unsqueeze(0))
if store_intermediate_actions and i < len(key_frame) - 1:
right_intermediate_actions = []
left_intermediate_actions = []
for j in range(key_frame[i], key_frame[i + 1] + 1):
_, right_action= env.get_obs_action_right(demo._observations[j])
_, left_action= env.get_obs_action_left(demo._observations[j])
right_intermediate_actions.append(right_action.unsqueeze(0))
left_intermediate_actions.append(left_action.unsqueeze(0))
right_intermediate_action_ls.append(torch.cat(right_intermediate_actions))
left_intermediate_action_ls.append(torch.cat(left_intermediate_actions))
return demo, right_keyframe_state_ls, left_keyframe_state_ls, right_keyframe_action_ls, left_keyframe_action_ls, right_intermediate_action_ls, left_intermediate_action_ls
class Dataset(torch.utils.data.Dataset):
def __init__(self, args: Arguments):
# load RLBench environment
self.env = RLBenchEnv(
data_path=args.data_dir,
image_size=[256,256],
apply_rgb=True,
apply_pc=True,
apply_cameras=args.cameras,
)
tasks = args.tasks
variations = range(args.offset, args.max_variations)
self.items = []
for task_str, variation in itertools.product(tasks, variations):
episodes_dir = args.data_dir / task_str / f"variation{variation}" / "episodes"
episodes = [
(task_str, variation, int(ep.stem[7:]))
for ep in episodes_dir.glob("episode*")
]
self.items += episodes
self.num_items = len(self.items)
def __len__(self) -> int:
return self.num_items
def __getitem__(self, index: int) -> None:
task, variation, episode = self.items[index]
taskvar_dir = args.output / f"{task}+{variation}"
taskvar_dir.mkdir(parents=True, exist_ok=True)
(demo,
right_keyframe_state_ls,
left_keyframe_state_ls,
right_keyframe_action_ls,
left_keyframe_action_ls,
right_intermediate_action_ls,
left_intermediate_action_ls) = get_observation(
task, variation, episode, self.env,
bool(args.store_intermediate_actions)
)
right_state_ls = einops.rearrange(
right_keyframe_state_ls,
"t 1 (m n ch) h w -> t n m ch h w",
ch=3,
n=4, # len(right_cameras)
m=2,
)
left_state_ls = einops.rearrange(
left_keyframe_state_ls,
"t 1 (m n ch) h w -> t n m ch h w",
ch=3,
n=4, # len(left_cameras)
m=2,
)
right_frame_ids = list(range(len(right_state_ls) - 1))
left_frame_ids = list(range(len(left_state_ls) - 1))
# num_frames = len(frame_ids)
right_attn_indices = get_attn_indices_from_demo(task, demo, args.cameras,'right')
left_attn_indices = get_attn_indices_from_demo(task, demo, args.cameras,'left')
# unimanual
# state_dict: List = [[] for _ in range(6)]
# print("Demo {}".format(episode))
# state_dict[0].extend(frame_ids)
# state_dict[1] = state_ls[:-1].numpy()
# state_dict[2].extend(keyframe_action_ls[1:])
# state_dict[3].extend(attn_indices)
# state_dict[4].extend(keyframe_action_ls[:-1]) # gripper pos
# state_dict[5].extend(intermediate_action_ls) # traj from gripper pos to keyframe action
# bimanual
state_dict: List = [[] for _ in range(12)]
print("Demo {}".format(episode))
state_dict[0].extend(right_frame_ids)
state_dict[1].extend(left_frame_ids)
state_dict[2] = right_state_ls[:-1].numpy()
state_dict[3] = left_state_ls[:-1].numpy()
state_dict[4].extend(right_keyframe_action_ls[1:]) # right action
state_dict[5].extend(left_keyframe_action_ls[1:]) # left action
state_dict[6].extend(right_attn_indices)
state_dict[7].extend(left_attn_indices)
state_dict[8].extend(right_keyframe_action_ls[:-1]) # right gripper pos
state_dict[9].extend(left_keyframe_action_ls[:-1]) # left gripper pos
state_dict[10].extend(right_intermediate_action_ls) # traj from gripper pos to keyframe action
state_dict[11].extend(left_intermediate_action_ls) # traj from gripper pos to keyframe action
with open(taskvar_dir / f"ep{episode}.dat", "wb") as f:
f.write(blosc.compress(pickle.dumps(state_dict)))
if __name__ == "__main__":
args = Arguments().parse_args()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
dataset = Dataset(args)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=1,
num_workers=args.num_workers,
collate_fn=lambda x: x,
)
for _ in tqdm(dataloader):
continue |