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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | from __future__ import annotations
import json
import random
import time
from pathlib import Path
from typing import Any
import numpy as np
import torch
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
from torch import nn
from torch.utils.data import DataLoader
from .data import FallClipDataset
from .models import build_model
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def resolve_device(device: str) -> torch.device:
if device == "auto":
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
return torch.device(device)
class FocalLoss(nn.Module):
def __init__(self, gamma: float = 2.0, weight: torch.Tensor | None = None):
super().__init__()
self.gamma = gamma
self.weight = weight
def forward(self, logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
ce = nn.functional.cross_entropy(logits, target, weight=self.weight, reduction="none")
pt = torch.exp(-ce)
return ((1 - pt) ** self.gamma * ce).mean()
def batch_to_device(batch: dict[str, Any], device: torch.device) -> dict[str, Any]:
return {k: (v.to(device) if torch.is_tensor(v) else v) for k, v in batch.items()}
def compute_metrics(y_true: list[int], y_pred: list[int]) -> dict[str, float]:
labels = [0, 1]
cm = confusion_matrix(y_true, y_pred, labels=labels)
tn, fp, fn, tp = cm.ravel() if cm.size == 4 else (0, 0, 0, 0)
spec = tn / (tn + fp) if (tn + fp) else 0.0
return {
"accuracy": float(accuracy_score(y_true, y_pred)),
"precision": float(precision_score(y_true, y_pred, zero_division=0)),
"recall": float(recall_score(y_true, y_pred, zero_division=0)),
"specificity": float(spec),
"f1": float(f1_score(y_true, y_pred, zero_division=0)),
"macro_f1": float(f1_score(y_true, y_pred, average="macro", zero_division=0)),
}
@torch.no_grad()
def evaluate_loader(model: nn.Module, loader: DataLoader, device: torch.device) -> tuple[dict[str, float], float]:
model.eval()
y_true: list[int] = []
y_pred: list[int] = []
start = time.perf_counter()
n = 0
for batch in loader:
batch = batch_to_device(batch, device)
logits = model(batch)
pred = logits.argmax(1).detach().cpu().tolist()
y_pred.extend(pred)
y_true.extend(batch["label"].detach().cpu().tolist())
n += len(pred)
elapsed = max(time.perf_counter() - start, 1e-9)
metrics = compute_metrics(y_true, y_pred)
return metrics, n / elapsed
def train_model(
dataset: str,
method: str,
cfg: dict[str, Any],
epochs: int | None = None,
out_dir: str | Path | None = None,
processed_dataset: str | None = None,
) -> dict[str, Any]:
set_seed(int(cfg["seed"]))
device = resolve_device(cfg.get("device", "auto"))
base = Path("data/processed") / (processed_dataset or dataset)
out = Path(out_dir or Path("results") / dataset / method)
out.mkdir(parents=True, exist_ok=True)
train_ds = FallClipDataset(
base / "train.pkl",
train=True,
confidence_dropout=method == "dynafall" and cfg["dropout"]["enabled"],
random_dropout=method == "dynafall_random_dropout",
random_dropout_prob=cfg["dropout"]["high_conf_prob"],
high_conf_prob=cfg["dropout"]["high_conf_prob"],
low_conf_prob=cfg["dropout"]["low_conf_prob"],
low_conf_threshold=cfg["dropout"]["low_conf_threshold"],
seed=cfg["seed"],
)
val_ds = FallClipDataset(base / "val.pkl")
train_loader = DataLoader(train_ds, batch_size=cfg["batch_size"], shuffle=True, num_workers=cfg["num_workers"])
val_loader = DataLoader(val_ds, batch_size=cfg["batch_size"], shuffle=False, num_workers=cfg["num_workers"])
model = build_model(method, hidden=cfg["model"]["hidden"], num_classes=cfg["model"]["num_classes"]).to(device)
opt = torch.optim.AdamW(model.parameters(), lr=cfg["lr"], weight_decay=cfg["weight_decay"])
class_weight = class_weights_from_dataset(train_ds, cfg["model"]["num_classes"]).to(device)
loss_fn: nn.Module = (
FocalLoss(cfg["loss"]["gamma"], weight=class_weight)
if cfg["loss"]["name"] == "focal"
else nn.CrossEntropyLoss(weight=class_weight)
)
best_f1 = -1.0
best_epoch = 0
patience = int(cfg["patience"])
max_epochs = int(epochs or cfg["epochs"])
history = []
for epoch in range(1, max_epochs + 1):
model.train()
losses = []
for batch in train_loader:
batch = batch_to_device(batch, device)
opt.zero_grad(set_to_none=True)
loss = loss_fn(model(batch), batch["label"])
loss.backward()
opt.step()
losses.append(float(loss.detach().cpu()))
val_metrics, val_fps = evaluate_loader(model, val_loader, device)
row = {"epoch": epoch, "loss": float(np.mean(losses)), "val_fps": val_fps, **{f"val_{k}": v for k, v in val_metrics.items()}}
history.append(row)
if val_metrics["f1"] > best_f1:
best_f1 = val_metrics["f1"]
best_epoch = epoch
torch.save({"model": model.state_dict(), "cfg": cfg, "method": method}, out / "best.pt")
if epoch - best_epoch >= patience:
break
(out / "history.json").write_text(json.dumps(history, indent=2))
return {"best_epoch": best_epoch, "best_val_f1": best_f1, "checkpoint": str(out / "best.pt")}
def class_weights_from_dataset(ds: FallClipDataset, num_classes: int) -> torch.Tensor:
counts = torch.zeros(num_classes, dtype=torch.float32)
for sample in ds.samples:
counts[int(sample["label"])] += 1
counts = counts.clamp_min(1.0)
weights = counts.sum() / (num_classes * counts)
return weights / weights.mean()
def evaluate_checkpoint(
dataset: str,
method: str,
cfg: dict[str, Any],
checkpoint: str | Path | None = None,
split: str = "test",
robustness: str = "clean",
missing_amount: float = 0.0,
tag: str | None = None,
processed_dataset: str | None = None,
out_dir: str | Path | None = None,
) -> dict[str, Any]:
device = resolve_device(cfg.get("device", "auto"))
default_dir = Path(out_dir or Path("results") / dataset / method)
ckpt_path = Path(checkpoint or default_dir / "best.pt")
state = torch.load(ckpt_path, map_location=device)
method = state.get("method", method)
model = build_model(method, hidden=cfg["model"]["hidden"], num_classes=cfg["model"]["num_classes"]).to(device)
model.load_state_dict(state["model"])
ds = FallClipDataset(
Path("data/processed") / (processed_dataset or dataset) / f"{split}.pkl",
robustness=robustness,
missing_amount=missing_amount,
)
loader = DataLoader(ds, batch_size=cfg["batch_size"], shuffle=False, num_workers=cfg["num_workers"])
metrics, fps = evaluate_loader(model, loader, device)
params = sum(p.numel() for p in model.parameters())
result = {"dataset": dataset, "method": method, "split": split, "robustness": robustness, "fps": fps, "params": params, **metrics}
out_dir = default_dir
out_dir.mkdir(parents=True, exist_ok=True)
suffix = tag or f"{split}_{robustness}"
(out_dir / f"metrics_{suffix}.json").write_text(json.dumps(result, indent=2))
return result
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