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5261696 | 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 | """Raw-IMU preprocessing and prompt construction for AnyMo."""
from __future__ import annotations
from typing import Any, Sequence
import numpy as np
import torch
from scipy.signal import resample
from transformers import ProcessorMixin
from transformers.feature_extraction_utils import BatchFeature
from .modeling_components import SEGMENT_NAMES
IMU_BOS_TOKEN = "<imu_bos>"
IMU_EOS_TOKEN = "<imu_eos>"
IMU_CONTRASTIVE_TEMPLATE = (
"Represent the human motion from the wearable IMU motion tokens.\n\n"
"The IMU tokens are from IMU sensors attached to the user's {sensor_context}.\n\n"
"Input IMU token:\n{imu_token}\n\nReturn a compact embedding of the motion."
)
TEXT_CONTRASTIVE_PREFIX = "Represent the human motion described by the text.\n\nMotion description:\n"
TEXT_CONTRASTIVE_SUFFIX = "\n\nReturn a compact embedding of the motion."
CAPTION_TEMPLATE = (
"Describe the human motion represented by the wearable IMU motion tokens.\n\n"
"The IMU tokens are from IMU sensors attached to the user's {sensor_context}.\n\n"
"Input IMU token:\n{imu_token}"
)
HAR_TEMPLATE = (
"Recognize the activity represented by the wearable IMU motion tokens.\n\n"
"The IMU tokens are from IMU sensors attached to the user's {sensor_context}.\n\n"
"Input IMU token:\n{imu_token}\n\nCHOICES:\n{choices}\n\n"
"Choose the best matching option. Output the option key followed by the selected activity label."
)
DEFAULT_LOCATION_ALIASES = {
"head": "Head",
"neck": "Neck",
"pelvis": "Pelvis",
"waist": "Pelvis",
"lower back": "L5",
"chest": "T8",
"left shoulder": "L_Shoulder",
"right shoulder": "R_Shoulder",
"left upper arm": "L_UpperArm",
"right upper arm": "R_UpperArm",
"left forearm": "L_Forearm",
"right forearm": "R_Forearm",
"left wrist": "L_Forearm",
"right wrist": "R_Forearm",
"left hand": "L_Hand",
"right hand": "R_Hand",
"left thigh": "L_UpperLeg",
"right thigh": "R_UpperLeg",
"left lower leg": "L_LowerLeg",
"right lower leg": "R_LowerLeg",
"left ankle": "L_LowerLeg",
"right ankle": "R_LowerLeg",
"left foot": "L_Foot",
"right foot": "R_Foot",
}
class AnyMoProcessor(ProcessorMixin):
"""Maps raw wearable IMU streams to the AnyMo graph and prompt formats."""
tokenizer_class = "AutoTokenizer"
valid_kwargs = ["target_sample_rate_hz", "location_aliases", "channel_order"]
def __init__(
self,
tokenizer,
target_sample_rate_hz: int = 60,
location_aliases: dict[str, str] | None = None,
channel_order: Sequence[str] = ("acc_x", "acc_y", "acc_z", "gyro_x", "gyro_y", "gyro_z"),
):
super().__init__(tokenizer=tokenizer)
self.target_sample_rate_hz = int(target_sample_rate_hz)
self.location_aliases = dict(DEFAULT_LOCATION_ALIASES)
if location_aliases:
self.location_aliases.update(
{str(key).strip().lower(): str(value) for key, value in location_aliases.items()}
)
self.channel_order = list(channel_order)
def _canonical_segment(self, location: str) -> str:
raw = str(location).strip()
if raw in SEGMENT_NAMES:
return raw
normalized = raw.replace("_", " ").strip().lower()
if normalized in self.location_aliases:
return self.location_aliases[normalized]
matches = [name for name in SEGMENT_NAMES if name.replace("_", " ").lower() == normalized]
if matches:
return matches[0]
raise ValueError(
f"Unknown sensor location {location!r}. Use one of the canonical 23 segments "
f"or provide an explicit location alias."
)
def prepare_imu(
self,
imu: np.ndarray | torch.Tensor,
sensor_locations: Sequence[str] | Sequence[Sequence[str]],
sampling_rate: float | Sequence[float],
*,
return_tensors: str = "pt",
) -> BatchFeature:
values = imu.detach().cpu().numpy() if isinstance(imu, torch.Tensor) else np.asarray(imu)
if values.ndim == 3:
values = values[None]
if values.ndim != 4 or values.shape[-1] != 6:
raise ValueError(f"Expected IMU shape [T, S, 6] or [B, T, S, 6], got {values.shape}")
batch_size, _, sensor_count, _ = values.shape
if sensor_locations and isinstance(sensor_locations[0], str):
locations = [list(sensor_locations)] * batch_size
else:
locations = [list(row) for row in sensor_locations]
if len(locations) != batch_size or any(len(row) != sensor_count for row in locations):
raise ValueError("sensor_locations must contain one location for every sensor in every sample")
rates = [float(sampling_rate)] * batch_size if np.isscalar(sampling_rate) else [float(x) for x in sampling_rate]
if len(rates) != batch_size:
raise ValueError("sampling_rate must be a scalar or contain one value per sample")
graph_rows, mask_rows, context_rows = [], [], []
for sample, sample_locations, rate in zip(values, locations, rates):
if rate <= 0:
raise ValueError(f"sampling_rate must be positive, got {rate}")
target_length = max(1, int(round(sample.shape[0] * self.target_sample_rate_hz / rate)))
if target_length != sample.shape[0]:
sample = np.asarray(resample(sample, target_length, axis=0), dtype=np.float32)
else:
sample = np.asarray(sample, dtype=np.float32)
graph = np.zeros((6, sample.shape[0], len(SEGMENT_NAMES), 1), dtype=np.float32)
mask = np.zeros(len(SEGMENT_NAMES), dtype=bool)
canonical = [self._canonical_segment(name) for name in sample_locations]
if len(set(canonical)) != len(canonical):
raise ValueError("Multiple sensors map to the same graph segment; provide distinct canonical segments")
for sensor_index, segment in enumerate(canonical):
node_index = SEGMENT_NAMES.index(segment)
graph[:, :, node_index, 0] = sample[:, sensor_index, :].T
mask[node_index] = True
graph_rows.append(graph)
mask_rows.append(mask)
context_rows.append(self.format_sensor_context(canonical))
if len({row.shape[1] for row in graph_rows}) != 1:
raise ValueError("A batch must have equal resampled sequence lengths")
data = {
"imu_values": torch.from_numpy(np.stack(graph_rows)),
"visible_node_mask": torch.from_numpy(np.stack(mask_rows)),
"sensor_context": context_rows,
}
if return_tensors != "pt":
data["imu_values"] = data["imu_values"].numpy()
data["visible_node_mask"] = data["visible_node_mask"].numpy()
return BatchFeature(data=data, tensor_type=None)
@staticmethod
def format_sensor_context(segments: Sequence[str]) -> str:
names = []
for segment in segments:
if segment.startswith("L_"):
segment = "Left " + segment[2:]
elif segment.startswith("R_"):
segment = "Right " + segment[2:]
names.append(segment.replace("_", " "))
return ", ".join(names) if names else "visible body segments"
@staticmethod
def local_ids_to_text(local_ids: Sequence[int]) -> str:
tokens = "".join(f"<imu_{int(index):04d}>" for index in local_ids)
return f"{IMU_BOS_TOKEN}{tokens}{IMU_EOS_TOKEN}"
def build_motion_prompt(self, local_ids: Sequence[int], sensor_context: str) -> str:
return IMU_CONTRASTIVE_TEMPLATE.format(
imu_token=self.local_ids_to_text(local_ids), sensor_context=sensor_context
)
@staticmethod
def build_text_prompt(text: str) -> str:
return f"{TEXT_CONTRASTIVE_PREFIX}{text}{TEXT_CONTRASTIVE_SUFFIX}"
def build_caption_prompt(self, local_ids: Sequence[int], sensor_context: str) -> str:
content = CAPTION_TEMPLATE.format(
imu_token=self.local_ids_to_text(local_ids), sensor_context=sensor_context
)
return f"<|im_start|>user\n{content}<|im_end|>\n<|im_start|>assistant\n"
def build_har_prompt(
self, local_ids: Sequence[int], sensor_context: str, candidate_labels: Sequence[str]
) -> str:
choices = "\n".join(f"{chr(65 + index)}: {label}" for index, label in enumerate(candidate_labels))
content = HAR_TEMPLATE.format(
imu_token=self.local_ids_to_text(local_ids),
sensor_context=sensor_context,
choices=choices,
)
return f"<|im_start|>user\n{content}<|im_end|>\n<|im_start|>assistant\n"
def __call__(self, *args, **kwargs) -> BatchFeature:
return self.prepare_imu(*args, **kwargs)
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