File size: 5,612 Bytes
4a2a6e4 | 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 | """Custom Normalize transform with per-timestamp support.
"""
import dataclasses
import numpy as np
from openpi.transforms import DataTransformFn, DataDict, apply_tree, pad_to_dim
from openpi.shared import array_typing as at
from b1k.shared.normalize import NormStats
@dataclasses.dataclass(frozen=True)
class NormalizeWithPerTimestamp(DataTransformFn):
norm_stats: at.PyTree[NormStats] | None
use_quantiles: bool = False
strict: bool = False
use_per_timestamp: bool = False
def __post_init__(self):
if self.norm_stats is not None and self.use_quantiles:
for stats in at.tree_leaves(self.norm_stats):
if isinstance(stats, NormStats) and (stats.q01 is None or stats.q99 is None):
raise ValueError("Quantile normalization requires q01 and q99 in norm_stats")
def __call__(self, data: DataDict) -> DataDict:
if self.norm_stats is None:
return data
return apply_tree(
data,
self.norm_stats,
self._normalize_quantile if self.use_quantiles else self._normalize,
strict=self.strict,
)
def _normalize(self, x, stats: NormStats):
# Check if this is actions and we should use per-timestamp normalization
if self.use_per_timestamp and stats.per_timestamp_mean is not None and x.ndim >= 2:
# x has shape [..., action_horizon, action_dim] for actions
# stats.per_timestamp_mean has shape [action_horizon, action_dim]
mean = stats.per_timestamp_mean[..., : x.shape[-2], : x.shape[-1]]
std = stats.per_timestamp_std[..., : x.shape[-2], : x.shape[-1]]
return (x - mean) / (std + 1e-6)
else:
# Regular normalization
mean, std = stats.mean[..., : x.shape[-1]], stats.std[..., : x.shape[-1]]
return (x - mean) / (std + 1e-6)
def _normalize_quantile(self, x, stats: NormStats):
assert stats.q01 is not None
assert stats.q99 is not None
# Check if this is actions and we should use per-timestamp normalization
if self.use_per_timestamp and stats.per_timestamp_q01 is not None and x.ndim >= 2:
# x has shape [..., action_horizon, action_dim] for actions
# stats.per_timestamp_q01 has shape [action_horizon, action_dim]
q01 = stats.per_timestamp_q01[: x.shape[-2], : x.shape[-1]]
q99 = stats.per_timestamp_q99[: x.shape[-2], : x.shape[-1]]
return (x - q01) / (q99 - q01 + 1e-6) * 2.0 - 1.0
else:
# Regular quantile normalization
q01, q99 = stats.q01[..., : x.shape[-1]], stats.q99[..., : x.shape[-1]]
return (x - q01) / (q99 - q01 + 1e-6) * 2.0 - 1.0
@dataclasses.dataclass(frozen=True)
class UnnormalizeWithPerTimestamp(DataTransformFn):
norm_stats: at.PyTree[NormStats] | None
use_quantiles: bool = False
use_per_timestamp: bool = False
def __post_init__(self):
if self.norm_stats is not None and self.use_quantiles:
for stats in at.tree_leaves(self.norm_stats):
if isinstance(stats, NormStats) and (stats.q01 is None or stats.q99 is None):
raise ValueError("Quantile normalization requires q01 and q99 in norm_stats")
def __call__(self, data: DataDict) -> DataDict:
if self.norm_stats is None:
return data
return apply_tree(
data,
self.norm_stats,
self._unnormalize_quantile if self.use_quantiles else self._unnormalize,
strict=True,
)
def _unnormalize(self, x, stats: NormStats):
# Check if this is actions and we should use per-timestamp normalization
if self.use_per_timestamp and stats.per_timestamp_mean is not None and x.ndim >= 2:
# x has shape [..., action_horizon, action_dim] for actions
# stats.per_timestamp_mean has shape [action_horizon, action_dim]
mean = pad_to_dim(stats.per_timestamp_mean, x.shape[-1], axis=-1, value=0.0)
std = pad_to_dim(stats.per_timestamp_std, x.shape[-1], axis=-1, value=1.0)
# Ensure we only use the appropriate timesteps
mean = mean[: x.shape[-2], :]
std = std[: x.shape[-2], :]
return x * (std + 1e-6) + mean
else:
# Regular unnormalization
mean = pad_to_dim(stats.mean, x.shape[-1], axis=-1, value=0.0)
std = pad_to_dim(stats.std, x.shape[-1], axis=-1, value=1.0)
return x * (std + 1e-6) + mean
def _unnormalize_quantile(self, x, stats: NormStats):
assert stats.q01 is not None
assert stats.q99 is not None
# Check if this is actions and we should use per-timestamp normalization
if self.use_per_timestamp and stats.per_timestamp_q01 is not None and x.ndim >= 2:
# x has shape [..., action_horizon, action_dim] for actions
# stats.per_timestamp_q01 has shape [action_horizon, action_dim]
q01 = stats.per_timestamp_q01[: x.shape[-2], : x.shape[-1]]
q99 = stats.per_timestamp_q99[: x.shape[-2], : x.shape[-1]]
return (x + 1.0) / 2.0 * (q99 - q01 + 1e-6) + q01
else:
# Regular quantile unnormalization
q01, q99 = stats.q01, stats.q99
if (dim := q01.shape[-1]) < x.shape[-1]:
return np.concatenate([(x[..., :dim] + 1.0) / 2.0 * (q99 - q01 + 1e-6) + q01, x[..., dim:]], axis=-1)
return (x + 1.0) / 2.0 * (q99 - q01 + 1e-6) + q01
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