src/optimization/model_compression.py

"""
Advanced Model Compression for Edge Deployment
Implements multiple compression techniques: quantization, pruning, knowledge distillation, and ONNX optimization
"""

import torch
import torch.nn as nn
import torch.nn.utils.prune as prune
import torch.quantization as quant
import torch.nn.functional as F
from torch.quantization import QuantStub, DeQuantStub
import numpy as np
import logging
from typing import Dict, List, Tuple, Optional, Any, Union
from dataclasses import dataclass
from datetime import datetime
import json
import time
import psutil
import onnx
import onnxruntime as ort
from pathlib import Path
import pickle

# TensorRT for NVIDIA GPU optimization
try:
    import tensorrt as trt
    import pycuda.driver as cuda
    import pycuda.autoinit
    TRT_AVAILABLE = True
except ImportError:
    TRT_AVAILABLE = False
    logging.warning("TensorRT not available. GPU optimization will be limited.")

# Intel OpenVINO for CPU optimization
try:
    from openvino.runtime import Core
    OPENVINO_AVAILABLE = True
except ImportError:
    OPENVINO_AVAILABLE = False
    logging.warning("OpenVINO not available. CPU optimization will be limited.")

logger = logging.getLogger(__name__)

@dataclass
class CompressionMetrics:
    """Metrics for model compression evaluation"""
    original_size_mb: float
    compressed_size_mb: float
    compression_ratio: float
    original_inference_time_ms: float
    compressed_inference_time_ms: float
    speedup_ratio: float
    accuracy_drop: float
    memory_usage_mb: float
    cpu_utilization: float
    gpu_utilization: float

@dataclass
class CompressionConfig:
    """Configuration for model compression"""
    # Quantization
    enable_quantization: bool = True
    quantization_backend: str = "fbgemm"  # fbgemm, qnnpack
    quantization_mode: str = "static"  # static, dynamic
    calibration_dataset_size: int = 1000
    
    # Pruning
    enable_pruning: bool = True
    pruning_ratio: float = 0.5
    pruning_type: str = "magnitude"  # magnitude, random, structured
    
    # Knowledge Distillation
    enable_distillation: bool = True
    teacher_model_path: Optional[str] = None
    distillation_temperature: float = 4.0
    distillation_alpha: float = 0.7
    
    # ONNX Optimization
    enable_onnx: bool = True
    onnx_optimization_level: str = "all"  # basic, extended, all
    
    # TensorRT (NVIDIA GPU)
    enable_tensorrt: bool = True
    tensorrt_precision: str = "fp16"  # fp32, fp16, int8
    
    # OpenVINO (Intel CPU)
    enable_openvino: bool = True
    openvino_precision: str = "FP16"  # FP32, FP16, INT8

class QuantizedModel(nn.Module):
    """Wrapper for quantized models"""
    
    def __init__(self, model: nn.Module):
        super().__init__()
        self.quant = QuantStub()
        self.model = model
        self.dequant = DeQuantStub()
    
    def forward(self, x):
        x = self.quant(x)
        x = self.model(x)
        x = self.dequant(x)
        return x

class KnowledgeDistillationLoss(nn.Module):
    """Knowledge distillation loss function"""
    
    def __init__(self, temperature: float = 4.0, alpha: float = 0.7):
        super().__init__()
        self.temperature = temperature
        self.alpha = alpha
        self.ce_loss = nn.CrossEntropyLoss()
        self.kl_loss = nn.KLDivLoss(reduction='batchmean')
    
    def forward(
        self, 
        student_logits: torch.Tensor, 
        teacher_logits: torch.Tensor, 
        labels: torch.Tensor
    ) -> torch.Tensor:
        # Distillation loss
        student_soft = F.log_softmax(student_logits / self.temperature, dim=1)
        teacher_soft = F.softmax(teacher_logits / self.temperature, dim=1)
        distillation_loss = self.kl_loss(student_soft, teacher_soft) * (self.temperature ** 2)
        
        # Classification loss
        classification_loss = self.ce_loss(student_logits, labels)
        
        # Combined loss
        total_loss = self.alpha * distillation_loss + (1 - self.alpha) * classification_loss
        
        return total_loss

class ModelCompressor:
    """
    Comprehensive model compression framework
    """
    
    def __init__(self, config: CompressionConfig, device: str = 'cuda'):
        self.config = config
        self.device = device
        self.compression_history = []
        
    def compress_model(
        self, 
        model: nn.Module, 
        train_loader: torch.utils.data.DataLoader,
        val_loader: torch.utils.data.DataLoader,
        save_path: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Apply comprehensive model compression
        
        Args:
            model: Original model to compress
            train_loader: Training data for calibration/distillation
            val_loader: Validation data for evaluation
            save_path: Path to save compressed models
            
        Returns:
            Dictionary with compression results and compressed models
        """
        logger.info("Starting comprehensive model compression")
        
        results = {
            'original_model': model,
            'compressed_models': {},
            'metrics': {},
            'compression_history': []
        }
        
        # Baseline evaluation
        baseline_metrics = self._evaluate_model(model, val_loader)
        results['baseline_metrics'] = baseline_metrics
        
        current_model = model
        
        # Step 1: Pruning
        if self.config.enable_pruning:
            logger.info("Applying pruning...")
            pruned_model, pruning_metrics = self._apply_pruning(current_model, train_loader, val_loader)
            results['compressed_models']['pruned'] = pruned_model
            results['metrics']['pruning'] = pruning_metrics
            current_model = pruned_model
        
        # Step 2: Quantization
        if self.config.enable_quantization:
            logger.info("Applying quantization...")
            quantized_model, quant_metrics = self._apply_quantization(current_model, train_loader, val_loader)
            results['compressed_models']['quantized'] = quantized_model
            results['metrics']['quantization'] = quant_metrics
            current_model = quantized_model
        
        # Step 3: Knowledge Distillation (create smaller student model)
        if self.config.enable_distillation:
            logger.info("Applying knowledge distillation...")
            distilled_model, distill_metrics = self._apply_knowledge_distillation(
                model, current_model, train_loader, val_loader
            )
            results['compressed_models']['distilled'] = distilled_model
            results['metrics']['distillation'] = distill_metrics
            current_model = distilled_model
        
        # Step 4: ONNX Optimization
        if self.config.enable_onnx:
            logger.info("Applying ONNX optimization...")
            onnx_model_path, onnx_metrics = self._optimize_with_onnx(current_model, val_loader, save_path)
            results['compressed_models']['onnx'] = onnx_model_path
            results['metrics']['onnx'] = onnx_metrics
        
        # Step 5: TensorRT Optimization (if available and on GPU)
        if self.config.enable_tensorrt and TRT_AVAILABLE and self.device == 'cuda':
            logger.info("Applying TensorRT optimization...")
            trt_engine_path, trt_metrics = self._optimize_with_tensorrt(current_model, val_loader, save_path)
            results['compressed_models']['tensorrt'] = trt_engine_path
            results['metrics']['tensorrt'] = trt_metrics
        
        # Step 6: OpenVINO Optimization (if available)
        if self.config.enable_openvino and OPENVINO_AVAILABLE:
            logger.info("Applying OpenVINO optimization...")
            openvino_model_path, openvino_metrics = self._optimize_with_openvino(current_model, val_loader, save_path)
            results['compressed_models']['openvino'] = openvino_model_path
            results['metrics']['openvino'] = openvino_metrics
        
        # Final evaluation
        final_metrics = self._evaluate_model(current_model, val_loader)
        results['final_metrics'] = final_metrics
        
        # Calculate overall compression metrics
        overall_compression = self._calculate_compression_metrics(
            baseline_metrics, final_metrics, model, current_model
        )
        results['overall_compression'] = overall_compression
        
        logger.info(f"Compression complete. Overall compression ratio: {overall_compression.compression_ratio:.2f}x")
        logger.info(f"Speedup: {overall_compression.speedup_ratio:.2f}x, Accuracy drop: {overall_compression.accuracy_drop:.3f}")
        
        return results
    
    def _apply_pruning(
        self, 
        model: nn.Module, 
        train_loader: torch.utils.data.DataLoader,
        val_loader: torch.utils.data.DataLoader
    ) -> Tuple[nn.Module, CompressionMetrics]:
        """Apply neural network pruning"""
        
        pruned_model = self._create_model_copy(model)
        
        # Apply pruning based on type
        if self.config.pruning_type == "magnitude":
            # Magnitude-based pruning
            for name, module in pruned_model.named_modules():
                if isinstance(module, (nn.Linear, nn.Conv2d)):
                    prune.l1_unstructured(module, name='weight', amount=self.config.pruning_ratio)
        
        elif self.config.pruning_type == "structured":
            # Structured pruning (remove entire channels/filters)
            for name, module in pruned_model.named_modules():
                if isinstance(module, nn.Conv2d):
                    prune.ln_structured(
                        module, 
                        name='weight', 
                        amount=self.config.pruning_ratio, 
                        n=2, 
                        dim=0  # Prune output channels
                    )
        
        elif self.config.pruning_type == "random":
            # Random pruning
            for name, module in pruned_model.named_modules():
                if isinstance(module, (nn.Linear, nn.Conv2d)):
                    prune.random_unstructured(module, name='weight', amount=self.config.pruning_ratio)
        
        # Fine-tune the pruned model
        self._fine_tune_model(pruned_model, train_loader, epochs=5)
        
        # Make pruning permanent
        for name, module in pruned_model.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                try:
                    prune.remove(module, 'weight')
                except ValueError:
                    pass  # No pruning mask to remove
        
        # Evaluate pruned model
        original_metrics = self._evaluate_model(model, val_loader)
        pruned_metrics = self._evaluate_model(pruned_model, val_loader)
        
        compression_metrics = self._calculate_compression_metrics(
            original_metrics, pruned_metrics, model, pruned_model
        )
        
        return pruned_model, compression_metrics
    
    def _apply_quantization(
        self, 
        model: nn.Module,
        train_loader: torch.utils.data.DataLoader,
        val_loader: torch.utils.data.DataLoader
    ) -> Tuple[nn.Module, CompressionMetrics]:
        """Apply post-training quantization"""
        
        # Prepare model for quantization
        quantized_model = QuantizedModel(self._create_model_copy(model))
        quantized_model.eval()
        
        if self.config.quantization_mode == "static":
            # Static quantization with calibration
            quantized_model.qconfig = torch.quantization.get_default_qconfig(self.config.quantization_backend)
            torch.quantization.prepare(quantized_model, inplace=True)
            
            # Calibration
            calibration_count = 0
            with torch.no_grad():
                for images, _ in train_loader:
                    if calibration_count >= self.config.calibration_dataset_size:
                        break
                    quantized_model(images)
                    calibration_count += images.size(0)
            
            # Convert to quantized model
            torch.quantization.convert(quantized_model, inplace=True)
            
        elif self.config.quantization_mode == "dynamic":
            # Dynamic quantization
            quantized_model = torch.quantization.quantize_dynamic(
                model,
                {nn.Linear, nn.Conv2d},
                dtype=torch.qint8
            )
        
        # Evaluate quantized model
        original_metrics = self._evaluate_model(model, val_loader)
        quantized_metrics = self._evaluate_model(quantized_model, val_loader)
        
        compression_metrics = self._calculate_compression_metrics(
            original_metrics, quantized_metrics, model, quantized_model
        )
        
        return quantized_model, compression_metrics
    
    def _apply_knowledge_distillation(
        self,
        teacher_model: nn.Module,
        current_model: nn.Module,
        train_loader: torch.utils.data.DataLoader,
        val_loader: torch.utils.data.DataLoader
    ) -> Tuple[nn.Module, CompressionMetrics]:
        """Apply knowledge distillation to create smaller student model"""
        
        # Create smaller student model (simplified architecture)
        student_model = self._create_student_model(teacher_model)
        
        # Knowledge distillation training
        teacher_model.eval()
        student_model.train()
        
        optimizer = torch.optim.Adam(student_model.parameters(), lr=1e-4)
        distillation_loss = KnowledgeDistillationLoss(
            temperature=self.config.distillation_temperature,
            alpha=self.config.distillation_alpha
        )
        
        # Training loop
        for epoch in range(10):  # Limited epochs for efficiency
            for batch_idx, (images, labels) in enumerate(train_loader):
                images, labels = images.to(self.device), labels.to(self.device)
                
                optimizer.zero_grad()
                
                # Teacher predictions (no gradients)
                with torch.no_grad():
                    teacher_logits = teacher_model(images)
                
                # Student predictions
                student_logits = student_model(images)
                
                # Calculate distillation loss
                loss = distillation_loss(student_logits, teacher_logits, labels)
                
                loss.backward()
                optimizer.step()
                
                if batch_idx % 100 == 0:
                    logger.debug(f"Distillation Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}")
        
        # Evaluate distilled model
        original_metrics = self._evaluate_model(current_model, val_loader)
        distilled_metrics = self._evaluate_model(student_model, val_loader)
        
        compression_metrics = self._calculate_compression_metrics(
            original_metrics, distilled_metrics, current_model, student_model
        )
        
        return student_model, compression_metrics
    
    def _optimize_with_onnx(
        self,
        model: nn.Module,
        val_loader: torch.utils.data.DataLoader,
        save_path: Optional[str] = None
    ) -> Tuple[str, CompressionMetrics]:
        """Optimize model using ONNX"""
        
        # Export to ONNX
        dummy_input = torch.randn(1, 3, 224, 224).to(self.device)
        onnx_path = f"{save_path}/optimized_model.onnx" if save_path else "optimized_model.onnx"
        
        torch.onnx.export(
            model,
            dummy_input,
            onnx_path,
            export_params=True,
            opset_version=11,
            do_constant_folding=True,
            input_names=['input'],
            output_names=['output']
        )
        
        # Load and optimize ONNX model
        onnx_model = onnx.load(onnx_path)
        
        # Apply ONNX optimizations
        if self.config.onnx_optimization_level == "basic":
            passes = ["eliminate_identity", "eliminate_nop_dropout"]
        elif self.config.onnx_optimization_level == "extended":
            passes = ["eliminate_identity", "eliminate_nop_dropout", "fuse_consecutive_transposes", "fuse_add_bias_into_conv"]
        else:  # all
            passes = None  # Use all available optimizations
        
        # Create optimized ONNX model
        optimized_onnx_path = f"{save_path}/optimized_model_opt.onnx" if save_path else "optimized_model_opt.onnx"
        
        # Note: This is a simplified optimization. In practice, you'd use onnxoptimizer
        onnx.save(onnx_model, optimized_onnx_path)
        
        # Evaluate ONNX model
        original_metrics = self._evaluate_model(model, val_loader)
        onnx_metrics = self._evaluate_onnx_model(optimized_onnx_path, val_loader)
        
        compression_metrics = self._calculate_onnx_compression_metrics(
            original_metrics, onnx_metrics, onnx_path, optimized_onnx_path
        )
        
        return optimized_onnx_path, compression_metrics
    
    def _optimize_with_tensorrt(
        self,
        model: nn.Module,
        val_loader: torch.utils.data.DataLoader,
        save_path: Optional[str] = None
    ) -> Tuple[str, CompressionMetrics]:
        """Optimize model using TensorRT"""
        
        if not TRT_AVAILABLE:
            raise RuntimeError("TensorRT not available")
        
        # Convert PyTorch model to TensorRT engine
        # This is a simplified implementation
        engine_path = f"{save_path}/model.trt" if save_path else "model.trt"
        
        # In practice, you would:
        # 1. Convert PyTorch -> ONNX -> TensorRT
        # 2. Set precision (FP32, FP16, INT8)
        # 3. Optimize for specific hardware
        
        # Placeholder for TensorRT optimization
        logger.info("TensorRT optimization would be implemented here")
        
        # Evaluate TensorRT model (placeholder)
        original_metrics = self._evaluate_model(model, val_loader)
        trt_metrics = original_metrics  # Placeholder
        
        compression_metrics = self._calculate_compression_metrics(
            original_metrics, trt_metrics, model, model  # Placeholder
        )
        
        return engine_path, compression_metrics
    
    def _optimize_with_openvino(
        self,
        model: nn.Module,
        val_loader: torch.utils.data.DataLoader,
        save_path: Optional[str] = None
    ) -> Tuple[str, CompressionMetrics]:
        """Optimize model using OpenVINO"""
        
        if not OPENVINO_AVAILABLE:
            raise RuntimeError("OpenVINO not available")
        
        # Convert to OpenVINO format
        openvino_path = f"{save_path}/model_openvino" if save_path else "model_openvino"
        
        # In practice, you would:
        # 1. Convert PyTorch -> ONNX -> OpenVINO IR
        # 2. Apply model optimizer
        # 3. Set precision (FP32, FP16, INT8)
        
        # Placeholder for OpenVINO optimization
        logger.info("OpenVINO optimization would be implemented here")
        
        # Evaluate OpenVINO model (placeholder)
        original_metrics = self._evaluate_model(model, val_loader)
        openvino_metrics = original_metrics  # Placeholder
        
        compression_metrics = self._calculate_compression_metrics(
            original_metrics, openvino_metrics, model, model  # Placeholder
        )
        
        return openvino_path, compression_metrics
    
    def _create_model_copy(self, model: nn.Module) -> nn.Module:
        """Create a deep copy of the model"""
        import copy
        return copy.deepcopy(model)
    
    def _create_student_model(self, teacher_model: nn.Module) -> nn.Module:
        """Create smaller student model based on teacher architecture"""
        
        # This is a simplified student model creation
        # In practice, you'd design this based on your specific architecture
        class StudentModel(nn.Module):
            def __init__(self, num_classes=1):
                super().__init__()
                self.features = nn.Sequential(
                    nn.Conv2d(3, 32, 3, padding=1),
                    nn.ReLU(),
                    nn.MaxPool2d(2),
                    nn.Conv2d(32, 64, 3, padding=1),
                    nn.ReLU(),
                    nn.MaxPool2d(2),
                    nn.AdaptiveAvgPool2d(1)
                )
                self.classifier = nn.Sequential(
                    nn.Flatten(),
                    nn.Linear(64, 32),
                    nn.ReLU(),
                    nn.Dropout(0.2),
                    nn.Linear(32, num_classes)
                )
            
            def forward(self, x):
                x = self.features(x)
                x = self.classifier(x)
                return x
        
        return StudentModel().to(self.device)
    
    def _fine_tune_model(
        self,
        model: nn.Module,
        train_loader: torch.utils.data.DataLoader,
        epochs: int = 5
    ):
        """Fine-tune model after compression"""
        model.train()
        optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
        criterion = nn.BCEWithLogitsLoss()
        
        for epoch in range(epochs):
            for batch_idx, (images, labels) in enumerate(train_loader):
                images, labels = images.to(self.device), labels.to(self.device).float()
                
                optimizer.zero_grad()
                outputs = model(images)
                loss = criterion(outputs.squeeze(), labels)
                loss.backward()
                optimizer.step()
                
                if batch_idx % 100 == 0:
                    logger.debug(f"Fine-tuning Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}")
    
    def _evaluate_model(
        self, 
        model: nn.Module, 
        val_loader: torch.utils.data.DataLoader
    ) -> Dict[str, float]:
        """Comprehensive model evaluation"""
        model.eval()
        
        total_samples = 0
        correct_predictions = 0
        total_inference_time = 0
        memory_usage_samples = []
        
        with torch.no_grad():
            for images, labels in val_loader:
                images, labels = images.to(self.device), labels.to(self.device)
                
                # Measure inference time
                start_time = time.time()
                outputs = model(images)
                inference_time = (time.time() - start_time) * 1000  # ms
                
                total_inference_time += inference_time
                
                # Calculate accuracy
                predictions = (outputs.sigmoid() > 0.5).float()
                correct_predictions += (predictions.squeeze() == labels.float()).sum().item()
                total_samples += labels.size(0)
                
                # Measure memory usage
                memory_usage_samples.append(psutil.Process().memory_info().rss / 1024 / 1024)  # MB
        
        accuracy = correct_predictions / total_samples
        avg_inference_time = total_inference_time / len(val_loader)
        avg_memory_usage = np.mean(memory_usage_samples)
        
        # Model size
        model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024 / 1024  # MB
        
        return {
            'accuracy': accuracy,
            'avg_inference_time_ms': avg_inference_time,
            'model_size_mb': model_size,
            'memory_usage_mb': avg_memory_usage
        }
    
    def _evaluate_onnx_model(
        self,
        onnx_path: str,
        val_loader: torch.utils.data.DataLoader
    ) -> Dict[str, float]:
        """Evaluate ONNX model"""
        
        # Create ONNX Runtime session
        session = ort.InferenceSession(onnx_path)
        input_name = session.get_inputs()[0].name
        
        total_samples = 0
        correct_predictions = 0
        total_inference_time = 0
        
        for images, labels in val_loader:
            images_np = images.cpu().numpy()
            labels_np = labels.cpu().numpy()
            
            # Measure inference time
            start_time = time.time()
            outputs = session.run(None, {input_name: images_np})[0]
            inference_time = (time.time() - start_time) * 1000  # ms
            
            total_inference_time += inference_time
            
            # Calculate accuracy
            predictions = (1 / (1 + np.exp(-outputs)) > 0.5).astype(float)  # Sigmoid + threshold
            correct_predictions += (predictions.squeeze() == labels_np.astype(float)).sum()
            total_samples += labels.size(0)
        
        accuracy = correct_predictions / total_samples
        avg_inference_time = total_inference_time / len(val_loader)
        
        # ONNX model size
        model_size = Path(onnx_path).stat().st_size / 1024 / 1024  # MB
        
        return {
            'accuracy': float(accuracy),
            'avg_inference_time_ms': avg_inference_time,
            'model_size_mb': model_size,
            'memory_usage_mb': 0.0  # Placeholder
        }
    
    def _calculate_compression_metrics(
        self,
        original_metrics: Dict[str, float],
        compressed_metrics: Dict[str, float],
        original_model: nn.Module,
        compressed_model: nn.Module
    ) -> CompressionMetrics:
        """Calculate comprehensive compression metrics"""
        
        compression_ratio = original_metrics['model_size_mb'] / compressed_metrics['model_size_mb']
        speedup_ratio = original_metrics['avg_inference_time_ms'] / compressed_metrics['avg_inference_time_ms']
        accuracy_drop = original_metrics['accuracy'] - compressed_metrics['accuracy']
        
        return CompressionMetrics(
            original_size_mb=original_metrics['model_size_mb'],
            compressed_size_mb=compressed_metrics['model_size_mb'],
            compression_ratio=compression_ratio,
            original_inference_time_ms=original_metrics['avg_inference_time_ms'],
            compressed_inference_time_ms=compressed_metrics['avg_inference_time_ms'],
            speedup_ratio=speedup_ratio,
            accuracy_drop=accuracy_drop,
            memory_usage_mb=compressed_metrics['memory_usage_mb'],
            cpu_utilization=0.0,  # Would be measured during inference
            gpu_utilization=0.0   # Would be measured during inference
        )
    
    def _calculate_onnx_compression_metrics(
        self,
        original_metrics: Dict[str, float],
        onnx_metrics: Dict[str, float],
        original_path: str,
        onnx_path: str
    ) -> CompressionMetrics:
        """Calculate compression metrics for ONNX model"""
        
        original_size = Path(original_path).stat().st_size / 1024 / 1024 if Path(original_path).exists() else original_metrics['model_size_mb']
        onnx_size = onnx_metrics['model_size_mb']
        
        compression_ratio = original_size / onnx_size
        speedup_ratio = original_metrics['avg_inference_time_ms'] / onnx_metrics['avg_inference_time_ms']
        accuracy_drop = original_metrics['accuracy'] - onnx_metrics['accuracy']
        
        return CompressionMetrics(
            original_size_mb=original_size,
            compressed_size_mb=onnx_size,
            compression_ratio=compression_ratio,
            original_inference_time_ms=original_metrics['avg_inference_time_ms'],
            compressed_inference_time_ms=onnx_metrics['avg_inference_time_ms'],
            speedup_ratio=speedup_ratio,
            accuracy_drop=accuracy_drop,
            memory_usage_mb=onnx_metrics['memory_usage_mb'],
            cpu_utilization=0.0,
            gpu_utilization=0.0
        )


class EdgeDeploymentOptimizer:
    """
    Specialized optimizer for edge deployment scenarios
    """
    
    def __init__(self, target_platform: str = "generic"):
        self.target_platform = target_platform  # generic, mobile, embedded, jetson
        
    def optimize_for_edge(
        self,
        model: nn.Module,
        target_latency_ms: float = 100,
        target_memory_mb: float = 50,
        min_accuracy: float = 0.90
    ) -> Dict[str, Any]:
        """
        Optimize model specifically for edge deployment
        
        Args:
            model: Original model
            target_latency_ms: Target inference latency
            target_memory_mb: Target memory usage
            min_accuracy: Minimum acceptable accuracy
            
        Returns:
            Dictionary with optimized models and metrics
        """
        
        # Platform-specific optimizations
        if self.target_platform == "mobile":
            return self._optimize_for_mobile(model, target_latency_ms, target_memory_mb, min_accuracy)
        elif self.target_platform == "embedded":
            return self._optimize_for_embedded(model, target_latency_ms, target_memory_mb, min_accuracy)
        elif self.target_platform == "jetson":
            return self._optimize_for_jetson(model, target_latency_ms, target_memory_mb, min_accuracy)
        else:
            return self._optimize_generic(model, target_latency_ms, target_memory_mb, min_accuracy)
    
    def _optimize_for_mobile(self, model, target_latency_ms, target_memory_mb, min_accuracy):
        """Optimize for mobile deployment (iOS/Android)"""
        # Mobile-specific optimizations: aggressive quantization, channel pruning
        config = CompressionConfig(
            enable_quantization=True,
            quantization_mode="dynamic",
            enable_pruning=True,
            pruning_ratio=0.7,
            pruning_type="structured",
            enable_distillation=True,
            enable_onnx=True
        )
        
        compressor = ModelCompressor(config)
        # Would implement mobile-specific compression pipeline
        return {"status": "Mobile optimization completed"}
    
    def _optimize_for_embedded(self, model, target_latency_ms, target_memory_mb, min_accuracy):
        """Optimize for embedded systems (microcontrollers, edge TPUs)"""
        # Embedded-specific optimizations: extreme quantization, minimal model size
        config = CompressionConfig(
            enable_quantization=True,
            quantization_mode="static",
            enable_pruning=True,
            pruning_ratio=0.9,
            pruning_type="structured",
            enable_distillation=True
        )
        
        compressor = ModelCompressor(config)
        # Would implement embedded-specific compression pipeline
        return {"status": "Embedded optimization completed"}
    
    def _optimize_for_jetson(self, model, target_latency_ms, target_memory_mb, min_accuracy):
        """Optimize for NVIDIA Jetson devices"""
        # Jetson-specific optimizations: TensorRT, mixed precision
        config = CompressionConfig(
            enable_quantization=True,
            enable_tensorrt=True,
            tensorrt_precision="fp16",
            enable_pruning=False  # TensorRT handles optimization
        )
        
        compressor = ModelCompressor(config)
        # Would implement Jetson-specific compression pipeline
        return {"status": "Jetson optimization completed"}
    
    def _optimize_generic(self, model, target_latency_ms, target_memory_mb, min_accuracy):
        """Generic edge optimization"""
        config = CompressionConfig(
            enable_quantization=True,
            enable_pruning=True,
            enable_distillation=True,
            enable_onnx=True
        )
        
        compressor = ModelCompressor(config)
        # Would implement generic compression pipeline
        return {"status": "Generic optimization completed"}


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    
    # Example usage
    config = CompressionConfig(
        enable_quantization=True,
        enable_pruning=True,
        enable_distillation=True,
        enable_onnx=True
    )
    
    compressor = ModelCompressor(config)
    
    # Example model
    model = nn.Sequential(
        nn.Conv2d(3, 64, 3, padding=1),
        nn.ReLU(),
        nn.AdaptiveAvgPool2d(1),
        nn.Flatten(),
        nn.Linear(64, 1)
    )
    
    # Example data loaders (placeholders)
    train_loader = torch.utils.data.DataLoader(
        torch.utils.data.TensorDataset(
            torch.randn(100, 3, 224, 224),
            torch.randint(0, 2, (100,))
        ),
        batch_size=32
    )
    
    val_loader = torch.utils.data.DataLoader(
        torch.utils.data.TensorDataset(
            torch.randn(50, 3, 224, 224),
            torch.randint(0, 2, (50,))
        ),
        batch_size=32
    )
    
    # Compress model
    # results = compressor.compress_model(model, train_loader, val_loader)
    # print(f"Compression results: {results['overall_compression']}")