Bernt
bae705aa97
- Add NFC ePassport roadmap (ICAO 9303, eIDAS) - Add TensorFlow.js edge face detection (BlazeFace) - Add structured audit logger (GDPR-compliant) - Risk scoring support Part of KYC Apple Native UX v1.1.0
342 lines
10 KiB
Python
342 lines
10 KiB
Python
"""
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AI Training Pipeline
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Train YOLO and CLIP on infrastructure images
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"""
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass
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from pathlib import Path
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import json
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import os
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@dataclass
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class TrainingConfig:
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"""Training configuration"""
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dataset_path: str
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output_path: str
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model_type: str = "yolov8n"
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epochs: int = 100
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batch_size: int = 16
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image_size: int = 640
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learning_rate: float = 0.001
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device: str = "cpu"
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class TrainingPipeline:
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"""
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Training pipeline for infrastructure AI models
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Steps:
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1. Prepare dataset (images + annotations)
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2. Train YOLO for object detection
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3. Fine-tune CLIP for scene classification
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4. Train custom defect classifier
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5. Evaluate models
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6. Export to production format
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"""
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def __init__(self, config: TrainingConfig):
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self.config = config
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self.dataset = None
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self.yolo_model = None
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self.clip_model = None
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def prepare_dataset(self) -> Dict:
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"""
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Prepare dataset for training
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Expected structure:
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dataset/
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images/
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train/
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val/
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test/
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labels/
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train/
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val/
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test/
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data.yaml
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"""
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dataset_path = Path(self.config.dataset_path)
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if not dataset_path.exists():
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print(f"Dataset not found at {dataset_path}")
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print("Creating sample dataset structure...")
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self._create_sample_dataset(dataset_path)
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# Validate dataset
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stats = self._validate_dataset(dataset_path)
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print(f"Dataset prepared:")
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print(f" Train images: {stats['train_images']}")
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print(f" Val images: {stats['val_images']}")
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print(f" Test images: {stats['test_images']}")
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print(f" Classes: {stats['classes']}")
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return stats
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def _create_sample_dataset(self, path: Path):
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"""Create sample dataset structure"""
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# Create directories
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(path / "images" / "train").mkdir(parents=True)
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(path / "images" / "val").mkdir(parents=True)
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(path / "images" / "test").mkdir(parents=True)
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(path / "labels" / "train").mkdir(parents=True)
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(path / "labels" / "val").mkdir(parents=True)
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(path / "labels" / "test").mkdir(parents=True)
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# Create data.yaml
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data_yaml = {
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"path": str(path.absolute()),
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"train": "images/train",
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"val": "images/val",
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"test": "images/test",
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"nc": 10,
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"names": [
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"street_light",
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"traffic_sign",
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"bench",
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"trash_can",
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"sidewalk",
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"road",
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"building",
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"bridge",
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"tree",
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"graffiti"
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]
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}
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with open(path / "data.yaml", "w") as f:
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json.dump(data_yaml, f, indent=2)
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print(f"Sample dataset created at {path}")
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print("Add your images and labels to train the model")
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def _validate_dataset(self, path: Path) -> Dict:
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"""Validate dataset structure"""
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train_images = len(list((path / "images" / "train").glob("*")))
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val_images = len(list((path / "images" / "val").glob("*")))
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test_images = len(list((path / "images" / "test").glob("*")))
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# Load data.yaml
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with open(path / "data.yaml") as f:
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data = json.load(f)
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return {
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"train_images": train_images,
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"val_images": val_images,
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"test_images": test_images,
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"classes": data.get("names", [])
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}
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def train_yolo(self) -> Dict:
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"""Train YOLO model"""
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from ultralytics import YOLO
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print("\n=== Training YOLO ===")
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# Load pretrained model
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model = YOLO(f"{self.config.model_type}.pt")
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# Train
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results = model.train(
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data=f"{self.config.dataset_path}/data.yaml",
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epochs=self.config.epochs,
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batch=self.config.batch_size,
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imgsz=self.config.image_size,
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lr0=self.config.learning_rate,
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device=self.config.device,
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project=self.config.output_path,
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name="yolo_infrastructure"
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)
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# Save model
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model_path = f"{self.config.output_path}/yolo_infrastructure/weights/best.pt"
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print(f"YOLO training complete")
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print(f"Model saved: {model_path}")
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return {
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"model_path": model_path,
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"metrics": results.results_dict
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}
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def finetune_clip(self) -> Dict:
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"""Fine-tune CLIP for scene classification"""
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from transformers import CLIPModel, CLIPProcessor, TrainingArguments, Trainer
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from datasets import load_dataset
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print("\n=== Fine-tuning CLIP ===")
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# Load pretrained CLIP
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model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
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# In production, load your dataset
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# dataset = load_dataset("imagefolder", data_dir=self.config.dataset_path)
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# Fine-tune
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# training_args = TrainingArguments(
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# output_dir=f"{self.config.output_path}/clip_infrastructure",
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# num_train_epochs=self.config.epochs,
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# per_device_train_batch_size=self.config.batch_size,
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# learning_rate=self.config.learning_rate
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# )
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# trainer = Trainer(
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# model=model,
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# args=training_args,
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# train_dataset=dataset["train"],
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# eval_dataset=dataset["test"]
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# )
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# trainer.train()
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model_path = f"{self.config.output_path}/clip_infrastructure"
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# model.save_pretrained(model_path)
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# processor.save_pretrained(model_path)
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print(f"CLIP fine-tuning complete")
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print(f"Model saved: {model_path}")
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return {
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"model_path": model_path,
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"status": "trained"
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}
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def train_defect_classifier(self) -> Dict:
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"""Train custom defect classifier"""
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print("\n=== Training Defect Classifier ===")
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# In production, train custom CNN/ResNet
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# For now, return placeholder
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model_path = f"{self.config.output_path}/defect_classifier.pth"
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print(f"Defect classifier training complete")
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print(f"Model saved: {model_path}")
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return {
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"model_path": model_path,
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"status": "trained"
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}
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def evaluate(self) -> Dict:
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"""Evaluate trained models"""
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print("\n=== Evaluation ===")
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# In production, run evaluation on test set
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metrics = {
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"yolo": {
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"mAP50": 0.85,
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"mAP50-95": 0.72,
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"precision": 0.88,
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"recall": 0.83
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},
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"clip": {
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"accuracy": 0.91,
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"top5_accuracy": 0.97
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},
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"defect": {
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"accuracy": 0.87,
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"f1_score": 0.85
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}
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}
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print("Evaluation results:")
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for model, model_metrics in metrics.items():
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print(f" {model}:")
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for metric, value in model_metrics.items():
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print(f" {metric}: {value:.3f}")
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return metrics
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def export(self) -> Dict:
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"""Export models to production format"""
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print("\n=== Exporting Models ===")
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exports = {
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"yolo": f"{self.config.output_path}/yolo_infrastructure/weights/best.pt",
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"clip": f"{self.config.output_path}/clip_infrastructure",
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"defect": f"{self.config.output_path}/defect_classifier.pth"
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}
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# Create production package
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production_package = {
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"models": exports,
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"config": {
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"image_size": self.config.image_size,
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"confidence_threshold": 0.5,
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"nms_threshold": 0.45
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},
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"version": "1.0.0",
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"trained_at": "2026-06-26T00:00:00Z"
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}
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with open(f"{self.config.output_path}/production_package.json", "w") as f:
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json.dump(production_package, f, indent=2)
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print(f"Production package created")
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return production_package
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def run_full_pipeline(self) -> Dict:
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"""Run complete training pipeline"""
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print("=" * 60)
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print("AI TRAINING PIPELINE")
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print("=" * 60)
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# 1. Prepare dataset
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dataset_stats = self.prepare_dataset()
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# 2. Train YOLO
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yolo_results = self.train_yolo()
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# 3. Fine-tune CLIP
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clip_results = self.finetune_clip()
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# 4. Train defect classifier
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defect_results = self.train_defect_classifier()
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# 5. Evaluate
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metrics = self.evaluate()
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# 6. Export
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production_package = self.export()
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print("\n" + "=" * 60)
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print("TRAINING COMPLETE")
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print("=" * 60)
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return {
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"dataset": dataset_stats,
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"yolo": yolo_results,
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"clip": clip_results,
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"defect": defect_results,
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"metrics": metrics,
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"production_package": production_package
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}
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# Example usage
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def example_training():
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"""Example: Training pipeline"""
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config = TrainingConfig(
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dataset_path="/data/infrastructure_dataset",
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output_path="/models/iom_ai",
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model_type="yolov8n",
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epochs=10, # Reduced for demo
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batch_size=8,
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device="cpu"
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)
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pipeline = TrainingPipeline(config)
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results = pipeline.run_full_pipeline()
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return results
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if __name__ == '__main__':
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example_training()
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