File size: 5,629 Bytes
ae419ed | 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 | from __future__ import annotations
import pickle
import random
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import numpy as np
import torch
from torch.utils.data import Dataset
from .features import bone_features, dynamics_features, mask_keypoints
@dataclass
class ClipSample:
video_id: str
label: int
joint: np.ndarray
def load_pickle(path: str | Path) -> Any:
with open(path, "rb") as f:
return pickle.load(f)
def save_pickle(obj: Any, path: str | Path) -> None:
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "wb") as f:
pickle.dump(obj, f)
class FallClipDataset(Dataset):
def __init__(
self,
pkl_path: str | Path,
robustness: str = "clean",
missing_amount: float = 0.0,
train: bool = False,
confidence_dropout: bool = False,
random_dropout: bool = False,
random_dropout_prob: float = 0.1,
high_conf_prob: float = 0.1,
low_conf_prob: float = 0.5,
low_conf_threshold: float = 0.3,
seed: int = 7,
) -> None:
self.samples = load_pickle(pkl_path)
self.robustness = robustness
self.missing_amount = missing_amount
self.train = train
self.confidence_dropout = confidence_dropout
self.random_dropout = random_dropout
self.random_dropout_prob = random_dropout_prob
self.high_conf_prob = high_conf_prob
self.low_conf_prob = low_conf_prob
self.low_conf_threshold = low_conf_threshold
self.rng = np.random.default_rng(seed)
def __len__(self) -> int:
return len(self.samples)
def __getitem__(self, idx: int) -> dict[str, torch.Tensor | str]:
sample = self.samples[idx]
joint = np.asarray(sample["joint"], dtype=np.float32)
if self.train and self.random_dropout:
joint = self._random_dropout(joint)
if self.train and self.confidence_dropout:
joint = self._confidence_dropout(joint)
if self.robustness != "clean":
joint = mask_keypoints(joint, self.robustness, self.missing_amount, self.rng)
bone = bone_features(joint)
dyn = dynamics_features(joint)
return {
"video_id": sample["video_id"],
"joint": torch.from_numpy(joint),
"bone": torch.from_numpy(bone),
"dyn": torch.from_numpy(dyn),
"label": torch.tensor(sample["label"], dtype=torch.long),
}
def _confidence_dropout(self, joint: np.ndarray) -> np.ndarray:
conf = joint[..., 2]
probs = np.where(conf < self.low_conf_threshold, self.low_conf_prob, self.high_conf_prob)
mask = self.rng.random(conf.shape) < probs
out = joint.copy()
out[mask] = 0
return out.astype(np.float32)
def _random_dropout(self, joint: np.ndarray) -> np.ndarray:
mask = self.rng.random(joint.shape[:2]) < self.random_dropout_prob
out = joint.copy()
out[mask] = 0
return out.astype(np.float32)
def split_video_ids(video_ids: list[str], ratios: dict[str, float], seed: int) -> dict[str, set[str]]:
ids = sorted(set(video_ids))
random.Random(seed).shuffle(ids)
n = len(ids)
n_train = max(1, int(round(n * ratios["train"])))
n_val = max(1, int(round(n * ratios["val"]))) if n >= 3 else 0
if n_train + n_val >= n:
n_train = max(1, n - 2)
n_val = 1 if n >= 3 else 0
return {
"train": set(ids[:n_train]),
"val": set(ids[n_train:n_train + n_val]),
"test": set(ids[n_train + n_val:]),
}
def split_video_records(
records: list[dict[str, Any]],
ratios: dict[str, float],
seed: int,
group_key: str = "video",
) -> dict[str, set[str]]:
"""Stratified split over video ids or higher-level scenario groups."""
rng = random.Random(seed)
group_labels: dict[str, int] = {}
for rec in records:
gid = record_group_id(rec, group_key)
label = int(rec["label"])
if gid in group_labels and group_labels[gid] != label:
raise ValueError(f"Mixed labels inside group {gid}")
group_labels[gid] = label
by_label: dict[int, list[str]] = {}
for gid, label in group_labels.items():
by_label.setdefault(label, []).append(gid)
group_buckets = {"train": set(), "val": set(), "test": set()}
for groups in by_label.values():
groups = sorted(set(groups))
rng.shuffle(groups)
n = len(groups)
n_train = max(1, int(round(n * ratios["train"])))
n_val = max(1, int(round(n * ratios["val"]))) if n >= 3 else 0
if n_train + n_val >= n:
n_train = max(1, n - 2)
n_val = 1 if n >= 3 else 0
group_buckets["train"].update(groups[:n_train])
group_buckets["val"].update(groups[n_train:n_train + n_val])
group_buckets["test"].update(groups[n_train + n_val:])
video_buckets = {"train": set(), "val": set(), "test": set()}
for rec in records:
gid = record_group_id(rec, group_key)
split = next(k for k, groups in group_buckets.items() if gid in groups)
video_buckets[split].add(str(rec["video_id"]))
return video_buckets
def record_group_id(rec: dict[str, Any], group_key: str) -> str:
video_id = str(rec["video_id"])
if group_key == "video":
return video_id
if group_key == "scenario":
parts = video_id.split("/")
return "/".join(parts[:2]) if len(parts) >= 2 else video_id
raise ValueError(f"Unknown group_key: {group_key}")
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