File size: 4,001 Bytes
a63439d | 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 | """Custom YOLOv8 Training & Export Script for E-Waste Cannibalization Index."""
from __future__ import annotations
import json
import logging
import os
from pathlib import Path
import yaml
from ultralytics import YOLO
# Enforce all cache and runs directories to reside strictly within workspace
workspace_dir = Path(__file__).resolve().parent.parent
cache_dir = (workspace_dir / "data" / "cache").resolve()
cache_dir.mkdir(parents=True, exist_ok=True)
runs_dir = (workspace_dir / "data" / "runs").resolve()
runs_dir.mkdir(parents=True, exist_ok=True)
os.environ["TORCH_HOME"] = str(cache_dir)
os.environ["HF_HOME"] = str(cache_dir)
os.environ["ULTRALYTICS_CONFIG_DIR"] = str(cache_dir)
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
log = logging.getLogger(__name__)
CONFIG_PATH = workspace_dir / "training" / "train_config.yaml"
def load_train_config(config_file: Path = CONFIG_PATH) -> dict:
if config_file.exists():
with open(config_file, "r", encoding="utf-8") as f:
return yaml.safe_load(f)
return {
"data": "data/splits/data.yaml",
"model": "yolov8n.pt",
"imgsz": 960,
"epochs": 50,
"batch": 8,
"copy_paste": 0.8,
"cls": 2.5,
}
def run_training(config_override: dict | None = None) -> dict:
cfg = load_train_config()
if config_override:
cfg.update(config_override)
log.info("Loaded training configuration: %s", cfg)
model_name = cfg.get("model", "yolov8n.pt")
data_yaml = str((workspace_dir / cfg.get("data", "data/splits/data.yaml")).resolve())
epochs = int(cfg.get("epochs", 50))
imgsz = int(cfg.get("imgsz", 960))
batch_size = int(cfg.get("batch", 8))
log.info("Initializing YOLO model backbone: %s", model_name)
model = YOLO(model_name)
# Train model with Copy-Paste augmentation and elevated class loss gain
log.info("Starting YOLOv8 fine-tuning on %s (%d epochs, imgsz=%d)...", data_yaml, epochs, imgsz)
results = model.train(
data=data_yaml,
epochs=epochs,
imgsz=imgsz,
batch=batch_size,
copy_paste=float(cfg.get("copy_paste", 0.8)),
mosaic=float(cfg.get("mosaic", 1.0)),
mixup=float(cfg.get("mixup", 0.15)),
box=float(cfg.get("box", 7.5)),
cls=float(cfg.get("cls", 2.5)),
dfl=float(cfg.get("dfl", 1.5)),
project=str(runs_dir),
name="ewaste_purity_v1",
exist_ok=True,
verbose=True,
)
# Validate trained model on test split
log.info("Validating trained model on test split...")
metrics = model.val(data=data_yaml, split="test", imgsz=imgsz)
maps_per_class = metrics.box.maps.tolist() if hasattr(metrics.box, "maps") else []
mp = float(metrics.box.mp) if hasattr(metrics.box, "mp") else 0.0
mr = float(metrics.box.mr) if hasattr(metrics.box, "mr") else 0.0
map50 = float(metrics.box.map50) if hasattr(metrics.box, "map50") else 0.0
map95 = float(metrics.box.map) if hasattr(metrics.box, "map") else 0.0
eval_summary = {
"mean_precision": round(mp, 4),
"mean_recall": round(mr, 4),
"mAP_50": round(map50, 4),
"mAP_50_95": round(map95, 4),
"maps_per_class": maps_per_class,
}
eval_file = workspace_dir / "reports" / "model_eval_summary.json"
eval_file.parent.mkdir(parents=True, exist_ok=True)
eval_file.write_text(json.dumps(eval_summary, indent=2), encoding="utf-8")
log.info("Saved evaluation summary to %s", eval_file)
try:
log.info("Exporting trained model to TFLite format...")
model.export(format="tflite", int8=True, imgsz=imgsz)
log.info("TFLite export successful!")
except Exception as exc:
log.warning("TFLite export deferred: %s", exc)
return eval_summary
if __name__ == "__main__":
import sys
ep = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isdigit() else 5
run_training({"epochs": ep})
|