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import numpy as np
import torch
import math
import einops
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
IMAGE_KEYS = (
"base_0_rgb",
"left_wrist_0_rgb",
"right_wrist_0_rgb",
)
def dict_apply(func, d):
"""
Apply a function to all values in a dictionary recursively.
If the value is a dictionary, it will apply the function to its values.
"""
for key, value in d.items():
if isinstance(value, dict):
dict_apply(func, value)
else:
d[key] = func(value)
return d
class Normalizer:
def __init__(
self,
norm_stats: Dict[str, Dict[str, np.ndarray]],
from_file: bool=False,
data_type: str=None,
norm_type: Dict[str, str] | None = None,
):
if from_file:
if data_type == 'libero':
norm_stats['state']['mean'] = np.array(norm_stats['state']['mean'][:8])
norm_stats['state']['std'] = np.array(norm_stats['state']['std'][:8])
norm_stats['actions']['mean'] = np.array(norm_stats['actions']['mean'][:7])
norm_stats['actions']['std'] = np.array(norm_stats['actions']['std'][:7])
elif data_type == 'robotwin':
norm_stats['observation.state'], norm_stats['action'] = {}, {}
norm_stats['observation.state']['q01'] = np.array(norm_stats['observation.state.arm.position']['q01'][:6] + norm_stats['observation.state.effector.position']['q01'][:1] + norm_stats['observation.state.arm.position']['q01'][6:] + norm_stats['observation.state.effector.position']['q01'][1:])
norm_stats['observation.state']['q99'] = np.array(norm_stats['observation.state.arm.position']['q99'][:6] + norm_stats['observation.state.effector.position']['q99'][:1] + norm_stats['observation.state.arm.position']['q99'][6:] + norm_stats['observation.state.effector.position']['q99'][1:])
norm_stats['action']['q01'] = np.array(norm_stats['action.arm.position']['q01'][:6] + norm_stats['action.effector.position']['q01'][:1] + norm_stats['action.arm.position']['q01'][6:] + norm_stats['action.effector.position']['q01'][1:])
norm_stats['action']['q99'] = np.array(norm_stats['action.arm.position']['q99'][:6] + norm_stats['action.effector.position']['q99'][:1] + norm_stats['action.arm.position']['q99'][6:] + norm_stats['action.effector.position']['q99'][1:])
elif data_type == 'robotwin_rep':
norm_stats['observation.state'], norm_stats['action'] = {}, {}
norm_stats['observation.state']['q01'] = np.array(norm_stats['observation.state.arm.position']['q01'] + norm_stats['observation.state.effector.position']['q01'])
norm_stats['observation.state']['q99'] = np.array(norm_stats['observation.state.arm.position']['q99'] + norm_stats['observation.state.effector.position']['q99'])
norm_stats['action']['q01'] = np.array(norm_stats['action.arm.position']['q01'][:6] + norm_stats['action.effector.position']['q01'][:1] + norm_stats['action.arm.position']['q01'][6:] + norm_stats['action.effector.position']['q01'][1:])
norm_stats['action']['q99'] = np.array(norm_stats['action.arm.position']['q99'][:6] + norm_stats['action.effector.position']['q99'][:1] + norm_stats['action.arm.position']['q99'][6:] + norm_stats['action.effector.position']['q99'][1:])
elif data_type == 'customized':
for key in norm_stats:
if isinstance(norm_stats[key], dict):
for sub_key in norm_stats[key]:
norm_stats[key][sub_key] = np.array(norm_stats[key][sub_key])
self.norm_stats = norm_stats
else:
self.norm_stats = dict_apply(lambda x: x.astype(np.float32), norm_stats)
self.norm_type = norm_type or {}
self.from_file = from_file
def normalize(self, data: Dict[str, np.ndarray]) -> Dict[str, torch.Tensor]:
normalized_data = {}
for key, value in data.items():
if key in self.norm_stats:
norm_type = self.norm_type.get(key, "identity")
if norm_type == "meanstd":
mean = self.norm_stats[key]["mean"]
std = self.norm_stats[key]["std"]
normalized_value = (value - mean) / (std + 1e-6)
elif norm_type == "bounds_99_woclip":
low = self.norm_stats[key]["q01"]
high = self.norm_stats[key]["q99"]
normalized_value = (value - low) / (high - low + 1e-6) * 2.0 - 1.0
elif norm_type == "std":
std = self.norm_stats[key]["std"]
normalized_value = value / (std + 1e-6)
elif norm_type == "minmax":
min_val = self.norm_stats[key]["min"]
max_val = self.norm_stats[key]["max"]
normalized_value = (value - min_val) / (
max_val - min_val + 1e-6
) * 2 - 1
elif norm_type == "identity":
normalized_value = value
else:
raise ValueError(
f"Unknown normalization type: {norm_type}. Supported types are 'meanstd', 'minmax', and 'identity'."
)
normalized_data[key] = normalized_value
else:
# If the key is not in norm_stats, we assume no normalization is needed
normalized_data[key] = value
return normalized_data
def unnormalize(self, data: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
"""
Unnormalize the given data using stored normalization statistics.
Args:
data (Dict[str, np.ndarray]): Dictionary of normalized arrays to unnormalize.
Returns:
Dict[str, np.ndarray]: Dictionary of unnormalized arrays.
"""
unnormalized_data = {}
for key, value in data.items():
if key in self.norm_stats:
norm_type = self.norm_type.get(key, "identity")
stats = self.norm_stats[key]
if norm_type == "meanstd":
mean = stats["mean"]
std = stats["std"]
unnormalized_value = value * (std + 1e-6) + mean
elif norm_type == "bounds_98" or norm_type == 'bounds_98_woclip':
low = self.norm_stats[key]["q02"]
high = self.norm_stats[key]["q98"]
unnormalized_value = ((value + 1.0) / 2.0) * (high - low + 1e-6) + low
elif norm_type == "bounds_99" or norm_type == "bounds_99_woclip":
low = self.norm_stats[key]["q01"]
high = self.norm_stats[key]["q99"]
unnormalized_value = ((value + 1.0) / 2.0) * (high - low + 1e-6) + low
elif norm_type == "std":
std = stats["std"]
unnormalized_value = value * (std + 1e-6)
elif norm_type == "minmax":
min_val = stats["min"]
max_val = stats["max"]
# Reverse: (x + 1)/2 * (max-min+eps) + min
unnormalized_value = (value + 1) / 2.0 * (max_val - min_val + 1e-6) + min_val
elif norm_type == "identity":
unnormalized_value = value
else:
raise ValueError(
f"Unknown normalization type: {norm_type}. Supported types are 'meanstd', 'minmax', and 'identity'."
)
unnormalized_data[key] = unnormalized_value
else:
# If no normalization was applied, return as-is
unnormalized_data[key] = value
return unnormalized_data
def resize_with_pad_item(img, width, height, pad_value=-1):
# assume no-op when width height fits already
if img.ndim != 3:
raise ValueError(f"(c,h,w) expected, but {img.shape}")
cur_height, cur_width = img.shape[1:]
ratio = max(cur_width / width, cur_height / height)
resized_height = int(cur_height / ratio)
resized_width = int(cur_width / ratio)
resized_img = F.interpolate(
img.unsqueeze(0), size=(resized_height, resized_width), mode="bilinear", align_corners=False
).squeeze(0)
pad_height = max(0, int(height - resized_height))
pad_width = max(0, int(width - resized_width))
# pad on left and top of image
padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value)
return padded_img
def prepare_images(config, image_processor, observation: dict[str, Tensor], use_depth_align=False):
"""Normalize, resize, and pad images and stack them into a tensor.
Args:
observation (dict[str, Tensor])
Returns:
images (torch.Tensor): (*b, n, c, h, w) images in range [-1.0, 1.0]
img_masks (torch.Tensor): (*b, n) masks for images, True if image is present, False if missing
"""
dtype = observation["state"].dtype
images, img_masks = [], []
if use_depth_align:
pil_images = []
for key in IMAGE_KEYS:
if key in observation["image"]:
# resize, pad, and normalize
img = observation["image"][key]
assert img.ndim == 3, f"Expected 3D image, got {img.shape}"
pil_img = img.cpu().numpy()
if image_processor is None:
img = img.to(dtype) / 127.5 - 1.0 # to [-1, 1]
img = resize_with_pad_item(
img, *config.resize_imgs_with_padding, pad_value=-1.0
)
else:
img = resize_with_pad_item(
img, *config.resize_imgs_with_padding, pad_value=0
)
img = image_processor(img)['pixel_values']
images.append(img)
img_masks.append(True)
if use_depth_align:
pil_images.append(pil_img)
else:
# zero padding
if image_processor is None:
img = torch.full_like(img, fill_value=-1.0)
if use_depth_align:
pil_img = torch.full_like(pil_img, fill_value=-1.0)
else:
img = np.zeros_like(img)
if use_depth_align:
pil_img = np.zeros_like(pil_img)
images.append(img)
if use_depth_align:
pil_images.append(pil_img)
img_masks.append(False)
if isinstance(images[0], torch.Tensor):
images = torch.stack(images, dim=0) # (n, c, h, w)
elif isinstance(images[0], np.ndarray):
images = torch.from_numpy(np.stack(images, axis=0)) # (n, c, h, w)
img_masks = torch.tensor(img_masks, dtype=torch.bool) # (*n)
if use_depth_align:
pil_images = torch.from_numpy(np.stack(pil_images, axis=0)) # (n, c, h, w)
else:
pil_images = []
return images, img_masks, pil_images
def prepare_state(config, observation: dict[str, Tensor]):
"""Pad the state to the maximum state dimension.
Args:
observation (dict[str, Tensor])
Returns:
state (torch.Tensor): (*b, max_state_dim) padded state tensor
"""
state = observation["state"]
state = F.pad(state, (0, config.max_state_dim - state.shape[-1]))
return state
def prepare_action(config, observation: dict[str, Tensor]):
"""Pad the action to the maximum action dimension.
Args:
observation (dict[str, Tensor])
Returns:
action (torch.Tensor): (*b, n, max_action_dim) padded action tensor
action_dim (int): the actual dimension of the action before padding
"""
# ipdb.set_trace()
action = observation["action"]
action = F.pad(action, (0, config.max_action_dim - action.shape[-1]))
return action
def prepare_language(config, language_tokenizer, observation: dict[str, Tensor]):
"""If `prompt` is provided, modify it to PaliGemma format and tokenize it.
If `lang_tokens` and `lang_masks` are provided, use them directly.
PaliGemma expects prefix prompts to be formatted as:
<images> .... <images> <bos> prompt <sep>, where <sep> uses `\\n`.
So here we format the prompt to start with `<bos>` and end with `\\n`.
Later, we will concatenate the images and language tokens into a single sequence.
Args:
observation (dict[str, Tensor])
Returns:
lang_tokens (torch.Tensor): (*b, l) language tokens
lang_masks (torch.Tensor): (*b, l) masks for language tokens, True if token is present, False if missing
"""
lang_tokens = observation.get("lang_tokens", None)
lang_masks = observation.get("lang_masks", None)
prompt = observation.get("prompt", None)
# either provide `prompt` or (`lang_tokens`, `lang_masks`)
if prompt is None and (lang_tokens is None or lang_masks is None):
raise ValueError(
"Either 'prompt' or ('lang_tokens', 'lang_masks') must be provided in the observation."
)
device = observation["state"].device
if prompt is not None and (lang_tokens is None or lang_masks is None):
prompt = [p if p.startswith("<bos>") else f"<bos>{p}" for p in prompt]
prompt = [p if p.endswith("\n") else f"{p}\n" for p in prompt]
tokenized_prompt = language_tokenizer.__call__(
prompt,
padding="max_length",
padding_side="right",
max_length=config.tokenizer_max_length,
truncation=True,
return_tensors="pt",
)
lang_tokens = tokenized_prompt["input_ids"].to(device=device)
lang_masks = tokenized_prompt["attention_mask"].to(
device=device, dtype=torch.bool
)
else:
lang_tokens = observation["lang_tokens"].to(device=device)
lang_masks = observation["lang_masks"].to(device=device, dtype=torch.bool)
return lang_tokens.squeeze(0), lang_masks.squeeze(0) |