import tensorflow as tf

# Post-training quantization
converter = tf.lite.TFLiteConverter.from_saved_model('model/')
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Full integer quantization for maximum compression
def representative_dataset():
    for data in calibration_data:
        yield [data.astype(np.float32)]

converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8

quantized_model = converter.convert()

# Save
with open('model_quantized.tflite', 'wb') as f:
    f.write(quantized_model)

import tensorflow_model_optimization as tfmot

# Define pruning schedule
pruning_schedule = tfmot.sparsity.keras.PolynomialDecay(
    initial_sparsity=0.0,
    final_sparsity=0.5,
    begin_step=0,
    end_step=1000
)

# Apply pruning
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(
    model,
    pruning_schedule=pruning_schedule
)

# Train with pruning
model_for_pruning.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

callbacks = [
    tfmot.sparsity.keras.UpdatePruningStep(),
    tfmot.sparsity.keras.PruningSummaries(log_dir='./logs')
]

model_for_pruning.fit(train_data, epochs=10, callbacks=callbacks)

# Strip pruning wrappers for deployment
final_model = tfmot.sparsity.keras.strip_pruning(model_for_pruning)

import torch
import onnx
import onnxruntime as ort

# Export PyTorch model to ONNX
dummy_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(
    model,
    dummy_input,
    "model.onnx",
    export_params=True,
    opset_version=13,
    do_constant_folding=True,
    input_names=['input'],
    output_names=['output'],
    dynamic_axes={
        'input': {0: 'batch_size'},
        'output': {0: 'batch_size'}
    }
)

# Optimize ONNX model
from onnxruntime.transformers import optimizer
optimized_model = optimizer.optimize_model(
    "model.onnx",
    model_type='bert',  # or 'gpt2', 'vit', etc.
    num_heads=12,
    hidden_size=768
)
optimized_model.save_model_to_file("model_optimized.onnx")

import onnxruntime as ort
import numpy as np

class ONNXEdgeModel:
    def __init__(self, model_path: str):
        # Configure for edge device
        sess_options = ort.SessionOptions()
        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        sess_options.intra_op_num_threads = 4
        sess_options.inter_op_num_threads = 2
        
        # Use appropriate execution provider
        providers = ['CPUExecutionProvider']
        
        # Add GPU if available
        if 'CUDAExecutionProvider' in ort.get_available_providers():
            providers.insert(0, 'CUDAExecutionProvider')
        
        self.session = ort.InferenceSession(
            model_path,
            sess_options,
            providers=providers
        )
        
        self.input_name = self.session.get_inputs()[0].name
        self.output_name = self.session.get_outputs()[0].name
    
    def predict(self, input_data: np.ndarray) -> np.ndarray:
        return self.session.run(
            [self.output_name],
            {self.input_name: input_data}
        )[0]

import cv2
import numpy as np
from collections import deque
import threading
import queue

class EdgeObjectDetector:
    def __init__(self, model_path: str, confidence_threshold: float = 0.5):
        self.model = ONNXEdgeModel(model_path)
        self.confidence_threshold = confidence_threshold
        self.frame_queue = queue.Queue(maxsize=2)
        self.result_queue = queue.Queue(maxsize=2)
        self.running = False
        
    def preprocess(self, frame: np.ndarray) -> np.ndarray:
        # Resize to model input size
        input_frame = cv2.resize(frame, (640, 640))
        # Normalize
        input_frame = input_frame.astype(np.float32) / 255.0
        # HWC to CHW
        input_frame = np.transpose(input_frame, (2, 0, 1))
        # Add batch dimension
        return np.expand_dims(input_frame, axis=0)
    
    def postprocess(self, output: np.ndarray, original_shape: tuple):
        detections = []
        for detection in output[0]:
            confidence = detection[4]
            if confidence > self.confidence_threshold:
                x, y, w, h = detection[:4]
                class_id = int(detection[5])
                
                # Scale to original image size
                x *= original_shape[1] / 640
                y *= original_shape[0] / 640
                w *= original_shape[1] / 640
                h *= original_shape[0] / 640
                
                detections.append({
                    'bbox': [int(x-w/2), int(y-h/2), int(w), int(h)],
                    'confidence': float(confidence),
                    'class_id': class_id
                })
        return detections
    
    def inference_thread(self):
        while self.running:
            try:
                frame, original_shape = self.frame_queue.get(timeout=0.1)
                input_tensor = self.preprocess(frame)
                output = self.model.predict(input_tensor)
                detections = self.postprocess(output, original_shape)
                self.result_queue.put(detections)
            except queue.Empty:
                continue
    
    def start(self):
        self.running = True
        self.thread = threading.Thread(target=self.inference_thread)
        self.thread.start()
    
    def stop(self):
        self.running = False
        self.thread.join()
    
    def detect(self, frame: np.ndarray):
        if not self.frame_queue.full():
            self.frame_queue.put((frame, frame.shape))
        
        try:
            return self.result_queue.get_nowait()
        except queue.Empty:
            return None