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1703114 | 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 | # This file supplies action normalization utilities for DeepThinkVLA.
# Author: Cheng Yin
# Date: 2025-09
# Copyright (c) Cheng Yin. All rights reserved.
# See LICENSE file in the project root for license information.
import numpy as np
import torch
from torch import Tensor, nn
def create_stats_buffers(
normalization_type: str,
stats = None,
):
assert isinstance(normalization_type, str)
shape = tuple(stats["mean"].shape)
if normalization_type == "MEAN_STD":
mean = torch.ones(shape, dtype=torch.float32) * torch.inf
std = torch.ones(shape, dtype=torch.float32) * torch.inf
buffer = nn.ParameterDict(
{
"mean": nn.Parameter(mean, requires_grad=False),
"std": nn.Parameter(std, requires_grad=False),
}
)
elif normalization_type == "MIN_MAX":
min = torch.ones(shape, dtype=torch.float32) * torch.inf
max = torch.ones(shape, dtype=torch.float32) * torch.inf
buffer = nn.ParameterDict(
{
"min": nn.Parameter(min, requires_grad=False),
"max": nn.Parameter(max, requires_grad=False),
}
)
elif normalization_type == "QUANTILE":
q01 = torch.ones(shape, dtype=torch.float32) * torch.inf
q99 = torch.ones(shape, dtype=torch.float32) * torch.inf
buffer = nn.ParameterDict(
{
"q01": nn.Parameter(q01, requires_grad=False),
"q99": nn.Parameter(q99, requires_grad=False),
}
)
if stats:
if isinstance(stats["mean"], np.ndarray):
if normalization_type == "MEAN_STD":
buffer["mean"].data = torch.from_numpy(stats["mean"]).to(dtype=torch.float32)
buffer["std"].data = torch.from_numpy(stats["std"]).to(dtype=torch.float32)
elif normalization_type == "MIN_MAX":
buffer["min"].data = torch.from_numpy(stats["min"]).to(dtype=torch.float32)
buffer["max"].data = torch.from_numpy(stats["max"]).to(dtype=torch.float32)
elif normalization_type == "QUANTILE":
buffer["q01"].data = torch.from_numpy(stats["q01"]).to(dtype=torch.float32)
buffer["q99"].data = torch.from_numpy(stats["q99"]).to(dtype=torch.float32)
elif isinstance(stats["mean"], torch.Tensor):
if normalization_type == "MEAN_STD":
buffer["mean"].data = stats["mean"].clone().to(dtype=torch.float32)
buffer["std"].data = stats["std"].clone().to(dtype=torch.float32)
elif normalization_type == "MIN_MAX":
buffer["min"].data = stats["min"].clone().to(dtype=torch.float32)
buffer["max"].data = stats["max"].clone().to(dtype=torch.float32)
elif normalization_type == "QUANTILE":
buffer["q01"].data = stats["q01"].clone().to(dtype=torch.float32)
buffer["q99"].data = stats["q99"].clone().to(dtype=torch.float32)
else:
type_ = type(stats["mean"])
raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.")
return buffer
def _no_stats_error_str(name: str) -> str:
return (
f"`{name}` is infinity. You should either initialize with `stats` as an argument, or use a "
"pretrained model."
)
class Normalize_Action(nn.Module):
"""Normalizes data (e.g. "observation.image") for more stable and faster convergence during training."""
def __init__(
self,
normalization_type: str,
stats = None,
action_mask = None,
):
super().__init__()
self.normalization_type = normalization_type
self.stats = stats
self.action_mask = torch.tensor(action_mask, dtype=torch.bool) if action_mask is not None else None
stats_buffers = create_stats_buffers(normalization_type, stats)
setattr(self, "buffer_value", stats_buffers)
# TODO(rcadene): should we remove torch.no_grad?
@torch.no_grad
def forward(self, action):
self.action_mask = self.action_mask.to(action.device)
buffer = getattr(self, "buffer_value")
if self.normalization_type == "MEAN_STD":
mean = buffer["mean"].to(action.device, dtype=action.dtype)
std = buffer["std"].to(action.device, dtype=action.dtype)
assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
assert not torch.isinf(std).any(), _no_stats_error_str("std")
normalized_actions = torch.where(
self.action_mask,
(action - mean) / (std + 1e-8),
action,
)
elif self.normalization_type == "MIN_MAX":
min = buffer["min"].to(action.device, dtype=action.dtype)
max = buffer["max"].to(action.device, dtype=action.dtype)
assert not torch.isinf(min).any(), _no_stats_error_str("min")
assert not torch.isinf(max).any(), _no_stats_error_str("max")
normalized_actions = torch.where(
self.action_mask,
torch.clamp(2 * (action - min) / (max - min + 1e-8) - 1, -1, 1),
action,
)
elif self.normalization_type == "QUANTILE":
q01 = buffer["q01"].to(action.device, dtype=action.dtype)
q99 = buffer["q99"].to(action.device, dtype=action.dtype)
assert not torch.isinf(q01).any(), _no_stats_error_str("q01")
assert not torch.isinf(q99).any(), _no_stats_error_str("q99")
normalized_actions = torch.where(
self.action_mask,
torch.clamp(2 * (action - q01) / (q99 - q01 + 1e-8) - 1, -1, 1),
action,
)
else:
raise ValueError(self.normalization_type)
return normalized_actions
class Unnormalize_Action(nn.Module):
def __init__(
self,
normalization_type: str,
stats = None,
action_mask = None,
):
super().__init__()
self.normalization_type = normalization_type
self.stats = stats
self.action_mask = torch.tensor(action_mask, dtype=torch.bool) if action_mask is not None else None
stats_buffers = create_stats_buffers(normalization_type, stats)
setattr(self, "buffer_value", stats_buffers)
# TODO(rcadene): should we remove torch.no_grad?
@torch.no_grad
def forward(self, normalized_actions):
self.action_mask = self.action_mask.to(normalized_actions.device)
buffer = getattr(self, "buffer_value")
if self.normalization_type == "MEAN_STD":
mean = buffer["mean"].to(normalized_actions.device, dtype=normalized_actions.dtype)
std = buffer["std"].to(normalized_actions.device, dtype=normalized_actions.dtype)
assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
assert not torch.isinf(std).any(), _no_stats_error_str("std")
action = torch.where(
self.action_mask,
normalized_actions * std + mean,
normalized_actions,
)
elif self.normalization_type == "MIN_MAX":
min = buffer["min"].to(normalized_actions.device, dtype=normalized_actions.dtype)
max = buffer["max"].to(normalized_actions.device, dtype=normalized_actions.dtype)
assert not torch.isinf(min).any(), _no_stats_error_str("min")
assert not torch.isinf(max).any(), _no_stats_error_str("max")
action = torch.where(
self.action_mask,
0.5 * (normalized_actions + 1) * (max - min + 1e-8) + min,
normalized_actions,
)
elif self.normalization_type == "QUANTILE":
q01 = buffer["q01"].to(normalized_actions.device, dtype=normalized_actions.dtype)
q99 = buffer["q99"].to(normalized_actions.device, dtype=normalized_actions.dtype)
assert not torch.isinf(q01).any(), _no_stats_error_str("q01")
assert not torch.isinf(q99).any(), _no_stats_error_str("q99")
action = torch.where(
self.action_mask,
0.5 * (normalized_actions + 1) * (q99 - q01 + 1e-8) + q01,
normalized_actions,
)
else:
raise ValueError(self.normalization_type)
return action
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