File size: 11,274 Bytes
7896def | 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 | from typing import Literal, Dict, Annotated, Union, Any, List, Tuple, Optional
import torch
import json
from collections import defaultdict
import numpy as np
from omegaconf import DictConfig, OmegaConf
import hashlib
from pathlib import Path
from git import Repo
from fastwam.utils.logging_config import get_logger
from fastwam.utils.pytorch_utils import dict_apply
logger = get_logger(__name__)
ConstConstStr = Annotated[str, "format: 'const_min/const_max', where const_min and const_max give the constant range"]
NormMode = Union[Literal["min/max", "q01/q99", "z-score"], ConstConstStr]
class LinearNormalizer:
def __init__(
self,
shape_meta,
use_stepwise_action_norm,
default_mode: NormMode,
exception_mode: Dict[str, Dict[str, NormMode]],
stats: Dict[str, Dict[str, Dict[str, torch.Tensor]]]
):
super().__init__()
self.normalizers = {"action": {}, "state": {}}
self.stats = stats
for meta in shape_meta["action"]:
key = meta["key"]
if use_stepwise_action_norm:
cur_stats = {k.removeprefix("stepwise_"): v for k, v in stats["action"][key].items() if k.startswith("stepwise_")}
else:
cur_stats = {k.removeprefix("global_"): v for k, v in stats["action"][key].items() if k.startswith("global_")}
if exception_mode is not None and "action" in exception_mode and key in exception_mode["action"]:
cur_mode = exception_mode["action"][key]
else:
cur_mode = default_mode
self.normalizers["action"][key] = SingleFieldLinearNormalizer(
stats=cur_stats,
mode=cur_mode,
)
for meta in shape_meta["state"]:
key = meta["key"]
cur_stats = {k.removeprefix("global_"): v for k, v in stats["state"][key].items() if k.startswith("global_")}
if exception_mode is not None and "state" in exception_mode and key in exception_mode["state"]:
cur_mode = exception_mode["state"][key]
else:
cur_mode = default_mode
self.normalizers["state"][key] = SingleFieldLinearNormalizer(
stats=cur_stats,
mode=cur_mode,
)
def get_stats(self):
stats = {
"action": {key: norm.get_stats() for key, norm in self.normalizers["action"].items()},
"state": {key: norm.get_stats() for key, norm in self.normalizers["state"].items()}
}
return stats
def forward(self, batch: Dict[str, Dict[str, torch.Tensor]]) -> torch.Tensor:
if "action" in batch:
for key, norm in self.normalizers["action"].items():
batch["action"][key] = norm.forward(batch["action"][key])
for key, norm in self.normalizers["state"].items():
batch["state"][key] = norm.forward(batch["state"][key])
return batch
def backward(self, batch: Dict[str, Dict[str, torch.Tensor]]) -> torch.Tensor:
for key, norm in self.normalizers["action"].items():
batch["action"][key] = norm.backward(batch["action"][key])
for key, norm in self.normalizers["state"].items():
batch["state"][key] = norm.backward(batch["state"][key])
return batch
class SingleFieldLinearNormalizer:
std_reg = 1e-8
range_tol = 1e-4
output_max = 1.0
output_min = -1.0
def __init__(self, stats, mode: NormMode="min/max"):
self.stats = stats
self.mode = mode
if mode == "z-score":
input_mean, input_std = stats["mean"], stats["std"]
scale = 1.0 / (input_std + self.std_reg)
offset = - input_mean / (input_std + self.std_reg)
else:
if mode == "min/max":
input_min, input_max = stats["min"], stats["max"]
elif mode == "q01/q99":
input_min, input_max = stats["q01"], stats["q99"]
else:
# parse const_min/const_max
input_min, input_max = map(float, mode.split("/"))
input_min = torch.full_like(stats["min"], input_min)
input_max = torch.full_like(stats["max"], input_max)
input_range = input_max - input_min
ignore_dim = input_range < self.range_tol
input_range[ignore_dim] = self.output_max - self.output_min
scale = (self.output_max - self.output_min) / input_range
offset = self.output_min - scale * input_min
offset[ignore_dim] = (self.output_max + self.output_min) / 2 - input_min[ignore_dim]
self.scale = scale
self.offset = offset
def get_stats(self):
return self.stats
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x * self.scale + self.offset
x = torch.clamp(x, -5.0, 5.0)
return x
def backward(self, x: torch.Tensor) -> torch.Tensor:
x = (x - self.offset) / self.scale
return x
def save_dataset_stats_to_json(dataset_stats: dict, file_path: str):
def convert_tensor(obj):
if isinstance(obj, torch.Tensor):
return obj.detach().cpu().numpy().tolist()
elif isinstance(obj, (defaultdict, dict)):
return {k: convert_tensor(v) for k, v in dict(obj).items()}
elif isinstance(obj, (list, tuple)):
return [convert_tensor(item) for item in obj]
elif isinstance(obj, (int, float, str, bool, type(None))):
return obj
else:
return str(obj)
serializable_stats = convert_tensor(dataset_stats)
with open(file_path, 'w', encoding='utf-8') as f:
json.dump(serializable_stats, f, ensure_ascii=False, indent=2)
def load_dataset_stats_from_json(file_path: str,
try_convert_tensor: bool = True) -> Dict[str, Any]:
def is_numeric_list(obj):
if isinstance(obj, list):
if not obj:
return True
first = obj[0]
if isinstance(first, (int, float)):
return all(isinstance(x, (int, float)) for x in obj)
elif isinstance(first, list):
return all(is_numeric_list(item) for item in obj)
else:
return False
return False
def convert_back_to_tensor(obj):
if isinstance(obj, dict):
return {k: convert_back_to_tensor(v) for k, v in obj.items()}
elif isinstance(obj, list):
if is_numeric_list(obj):
try:
arr = np.array(obj)
return torch.from_numpy(arr)
except Exception:
return [convert_back_to_tensor(item) for item in obj]
else:
return [convert_back_to_tensor(item) for item in obj]
else:
return obj
with open(file_path, 'r', encoding='utf-8') as f:
data = json.load(f)
if try_convert_tensor:
data = convert_back_to_tensor(data)
data = dict_apply(
data,
lambda x: x.to(torch.float32) if isinstance(x, torch.Tensor) else x,
)
return data
def search_dataset_stats_cache_json(cache_dir: str | Path, data_config: DictConfig) -> Tuple[bool, str | None]:
if isinstance(cache_dir, str):
cache_dir = Path(cache_dir)
cache_dir.mkdir(parents=True, exist_ok=True)
def get_git_hash() -> Optional[str]:
repo = Repo(__file__, search_parent_directories=True)
return repo.head.commit.hexsha
def to_plain(value: Any) -> Any:
if OmegaConf.is_config(value):
return OmegaConf.to_container(value, resolve=True)
return value
def normalize_str_list(value: Any) -> List[str]:
value = to_plain(value)
if value is None:
return []
if isinstance(value, str):
value = [value]
return [str(item) for item in value if item is not None]
def normalize_transforms(value: Any) -> Any:
value = to_plain(value)
if isinstance(value, dict):
return [value]
return value
def normalize_dataset_dirs(cfg: DictConfig) -> Any:
dataset_cfg = cfg.get("dataset")
if dataset_cfg is None:
return None
embodiment_datasets = dataset_cfg.get("embodiment_datasets")
if embodiment_datasets is not None:
emb_dirs: Dict[str, List[str]] = {}
for emb, emb_cfg in embodiment_datasets.items():
dataset_groups = emb_cfg.get("dataset_groups")
if dataset_groups is None:
emb_dirs[emb] = []
continue
dirs: List[str] = []
for group in dataset_groups:
group_dirs = group.get("dataset_dirs")
if group_dirs is None:
continue
dirs.extend(normalize_str_list(group_dirs))
emb_dirs[emb] = sorted(dirs)
return emb_dirs
dataset_dirs = dataset_cfg.get("dataset_dirs")
return sorted(normalize_str_list(dataset_dirs))
def normalize_action_state_transforms(cfg: DictConfig) -> Any:
processor_cfg = cfg.get("processor")
if processor_cfg is None:
return None
embodiment_processors = processor_cfg.get("embodiment_processors")
if embodiment_processors is not None:
emb_transforms: Dict[str, Any] = {}
for emb, emb_cfg in embodiment_processors.items():
transforms = emb_cfg.get("action_state_transforms")
emb_transforms[emb] = normalize_transforms(transforms)
return emb_transforms
transforms = processor_cfg.get("action_state_transforms")
return normalize_transforms(transforms)
signature = {
"action_size": data_config.dataset.action_size,
"dataset_dirs": normalize_dataset_dirs(data_config),
"action_state_transforms": normalize_action_state_transforms(data_config),
}
signature_json = json.dumps(signature, sort_keys=True, separators=(",", ":"))
dataset_hash = hashlib.sha256(signature_json.encode("utf-8"), usedforsecurity=False).hexdigest()
git_hash = get_git_hash()
precise_name = f"dataset_stats_{dataset_hash}_{git_hash}.json"
precise = cache_dir / precise_name
if precise.exists():
logger.info(f"Found dataset stats cache with precisely matching dataset and git hash: {precise_name}.")
return True, str(precise)
candidates = sorted(cache_dir.glob(f"dataset_stats_{dataset_hash}_*.json"))
if not candidates:
logger.info(f"No dataset stats cache found for dataset hash {dataset_hash}")
return False, str(precise) # return precise cache path for saving cache
picked = candidates[0]
prefix = f"dataset_stats_{dataset_hash}_"
picked_git_hash = picked.name[len(prefix):-5]
assert picked_git_hash != git_hash
logger.warning(f"Found substitute dataset stats cache {picked.name} which mismatch current git hash {git_hash}.")
return True, str(picked)
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