references/script_7.py - main - klischa/RuView

references/script_7.py
311 строк11 KB
rUv
Add WiFi DensePose implementation and results
07 июн 2025, 08:23
07 июн 2025, 08:23f3c77b1
# Transfer Learning System for WiFi DensePose
# Based on the teacher-student learning approach from the paper

import numpy as np
from typing import Dict, List, Tuple, Optional

class TransferLearningSystem:
    """
    Implements transfer learning from image-based DensePose to WiFi-based DensePose
    """
    
    def __init__(self, lambda_tr=0.1):
        self.lambda_tr = lambda_tr  # Transfer learning loss weight
        self.teacher_features = {}
        self.student_features = {}
        
        print(f"Initialized Transfer Learning System:")
        print(f"  Transfer learning weight (λ_tr): {lambda_tr}")
    
    def extract_teacher_features(self, image_input):
        """
        Extract multi-level features from image-based teacher network
        """
        # Simulate teacher network (image-based DensePose) feature extraction
        features = {}
        
        # Simulate ResNet features at different levels
        features['P2'] = np.random.rand(1, 256, 180, 320)  # 1/4 scale
        features['P3'] = np.random.rand(1, 256, 90, 160)   # 1/8 scale
        features['P4'] = np.random.rand(1, 256, 45, 80)    # 1/16 scale
        features['P5'] = np.random.rand(1, 256, 23, 40)    # 1/32 scale
        
        self.teacher_features = features
        return features
    
    def extract_student_features(self, wifi_features):
        """
        Extract corresponding features from WiFi-based student network
        """
        # Simulate student network feature extraction from WiFi features
        features = {}
        
        # Process the WiFi features to match teacher feature dimensions
        # In practice, these would come from the modality translation network
        features['P2'] = np.random.rand(1, 256, 180, 320)
        features['P3'] = np.random.rand(1, 256, 90, 160)
        features['P4'] = np.random.rand(1, 256, 45, 80)
        features['P5'] = np.random.rand(1, 256, 23, 40)
        
        self.student_features = features
        return features
    
    def compute_mse_loss(self, teacher_feature, student_feature):
        """
        Compute Mean Squared Error between teacher and student features
        """
        return np.mean((teacher_feature - student_feature) ** 2)
    
    def compute_transfer_loss(self):
        """
        Compute transfer learning loss as sum of MSE at different levels
        L_tr = MSE(P2, P2*) + MSE(P3, P3*) + MSE(P4, P4*) + MSE(P5, P5*)
        """
        if not self.teacher_features or not self.student_features:
            raise ValueError("Both teacher and student features must be extracted first")
        
        total_loss = 0.0
        feature_losses = {}
        
        for level in ['P2', 'P3', 'P4', 'P5']:
            teacher_feat = self.teacher_features[level]
            student_feat = self.student_features[level]
            
            level_loss = self.compute_mse_loss(teacher_feat, student_feat)
            feature_losses[level] = level_loss
            total_loss += level_loss
        
        return total_loss, feature_losses
    
    def adapt_features(self, student_features, learning_rate=0.001):
        """
        Adapt student features to be more similar to teacher features
        """
        adapted_features = {}
        
        for level in ['P2', 'P3', 'P4', 'P5']:
            teacher_feat = self.teacher_features[level]
            student_feat = student_features[level]
            
            # Compute gradient (simplified as difference)
            gradient = teacher_feat - student_feat
            
            # Update student features
            adapted_features[level] = student_feat + learning_rate * gradient
        
        return adapted_features

class TrainingPipeline:
    """
    Complete training pipeline with transfer learning
    """
    
    def __init__(self):
        self.transfer_system = TransferLearningSystem()
        self.losses = {
            'classification': [],
            'bbox_regression': [],
            'densepose': [],
            'keypoint': [],
            'transfer': []
        }
        
        print("Initialized Training Pipeline with transfer learning")
    
    def compute_classification_loss(self, predictions, targets):
        """
        Compute classification loss (cross-entropy for person detection)
        """
        # Simplified cross-entropy loss simulation
        return np.random.uniform(0.1, 2.0)
    
    def compute_bbox_regression_loss(self, pred_boxes, target_boxes):
        """
        Compute bounding box regression loss (smooth L1)
        """
        # Simplified smooth L1 loss simulation
        return np.random.uniform(0.05, 1.0)
    
    def compute_densepose_loss(self, pred_parts, pred_uv, target_parts, target_uv):
        """
        Compute DensePose loss (part classification + UV regression)
        """
        # Part classification loss
        part_loss = np.random.uniform(0.2, 1.5)
        
        # UV coordinate regression loss
        uv_loss = np.random.uniform(0.1, 1.0)
        
        return part_loss + uv_loss
    
    def compute_keypoint_loss(self, pred_keypoints, target_keypoints):
        """
        Compute keypoint detection loss
        """
        return np.random.uniform(0.1, 0.8)
    
    def train_step(self, wifi_data, image_data, targets):
        """
        Perform one training step with synchronized WiFi and image data
        """
        # Extract teacher features from image
        teacher_features = self.transfer_system.extract_teacher_features(image_data)
        
        # Process WiFi data through student network (simulated)
        student_features = self.transfer_system.extract_student_features(wifi_data)
        
        # Compute individual losses
        cls_loss = self.compute_classification_loss(None, targets)
        box_loss = self.compute_bbox_regression_loss(None, targets)
        dp_loss = self.compute_densepose_loss(None, None, targets, targets)
        kp_loss = self.compute_keypoint_loss(None, targets)
        
        # Compute transfer learning loss
        tr_loss, feature_losses = self.transfer_system.compute_transfer_loss()
        
        # Total loss with weights
        total_loss = (cls_loss + box_loss + 
                     0.6 * dp_loss +      # λ_dp = 0.6
                     0.3 * kp_loss +      # λ_kp = 0.3
                     0.1 * tr_loss)       # λ_tr = 0.1
        
        # Store losses
        self.losses['classification'].append(cls_loss)
        self.losses['bbox_regression'].append(box_loss)
        self.losses['densepose'].append(dp_loss)
        self.losses['keypoint'].append(kp_loss)
        self.losses['transfer'].append(tr_loss)
        
        return {
            'total_loss': total_loss,
            'cls_loss': cls_loss,
            'box_loss': box_loss,
            'dp_loss': dp_loss,
            'kp_loss': kp_loss,
            'tr_loss': tr_loss,
            'feature_losses': feature_losses
        }
    
    def train_epochs(self, num_epochs=10):
        """
        Simulate training for multiple epochs
        """
        print(f"\nTraining WiFi DensePose with transfer learning...")
        print(f"Target epochs: {num_epochs}")
        
        for epoch in range(num_epochs):
            # Simulate training data
            wifi_data = np.random.rand(3, 720, 1280)
            image_data = np.random.rand(3, 720, 1280)
            targets = {"dummy": "target"}
            
            # Training step
            losses = self.train_step(wifi_data, image_data, targets)
            
            if epoch % 2 == 0 or epoch == num_epochs - 1:
                print(f"Epoch {epoch+1}/{num_epochs}:")
                print(f"  Total Loss: {losses['total_loss']:.4f}")
                print(f"  Classification: {losses['cls_loss']:.4f}")
                print(f"  BBox Regression: {losses['box_loss']:.4f}")
                print(f"  DensePose: {losses['dp_loss']:.4f}")
                print(f"  Keypoint: {losses['kp_loss']:.4f}")
                print(f"  Transfer: {losses['tr_loss']:.4f}")
                print(f"  Feature losses: P2={losses['feature_losses']['P2']:.4f}, "
                      f"P3={losses['feature_losses']['P3']:.4f}, "
                      f"P4={losses['feature_losses']['P4']:.4f}, "
                      f"P5={losses['feature_losses']['P5']:.4f}")
        
        return self.losses

class PerformanceEvaluator:
    """
    Evaluates the performance of the WiFi DensePose system
    """
    
    def __init__(self):
        print("Initialized Performance Evaluator")
    
    def compute_gps(self, pred_vertices, target_vertices, kappa=0.255):
        """
        Compute Geodesic Point Similarity (GPS)
        """
        # Simplified GPS computation
        distances = np.random.uniform(0, 0.5, len(pred_vertices))
        gps_scores = np.exp(-distances**2 / (2 * kappa**2))
        return np.mean(gps_scores)
    
    def compute_gpsm(self, gps_score, pred_mask, target_mask):
        """
        Compute masked Geodesic Point Similarity (GPSm)
        """
        # Compute IoU of masks
        intersection = np.sum(pred_mask & target_mask)
        union = np.sum(pred_mask | target_mask)
        iou = intersection / union if union > 0 else 0
        
        # GPSm = sqrt(GPS * IoU)
        return np.sqrt(gps_score * iou)
    
    def evaluate_system(self, predictions, ground_truth):
        """
        Evaluate the complete system performance
        """
        # Simulate evaluation metrics
        ap_metrics = {
            'AP': np.random.uniform(25, 45),
            'AP@50': np.random.uniform(50, 90),
            'AP@75': np.random.uniform(20, 50),
            'AP-m': np.random.uniform(20, 40),
            'AP-l': np.random.uniform(25, 50)
        }
        
        densepose_metrics = {
            'dpAP_GPS': np.random.uniform(20, 50),
            'dpAP_GPS@50': np.random.uniform(45, 80),
            'dpAP_GPS@75': np.random.uniform(20, 50),
            'dpAP_GPSm': np.random.uniform(20, 45),
            'dpAP_GPSm@50': np.random.uniform(40, 75),
            'dpAP_GPSm@75': np.random.uniform(20, 50)
        }
        
        return {
            'bbox_detection': ap_metrics,
            'densepose': densepose_metrics
        }

# Demonstrate the transfer learning system
print("="*60)
print("TRANSFER LEARNING DEMONSTRATION")
print("="*60)

# Initialize training pipeline
trainer = TrainingPipeline()

# Run training simulation
training_losses = trainer.train_epochs(num_epochs=10)

# Evaluate performance
evaluator = PerformanceEvaluator()
dummy_predictions = {"dummy": "pred"}
dummy_ground_truth = {"dummy": "gt"}

performance = evaluator.evaluate_system(dummy_predictions, dummy_ground_truth)

print(f"\nFinal Performance Metrics:")
print(f"Bounding Box Detection:")
for metric, value in performance['bbox_detection'].items():
    print(f"  {metric}: {value:.1f}")

print(f"\nDensePose Estimation:")
for metric, value in performance['densepose'].items():
    print(f"  {metric}: {value:.1f}")

print(f"\nTransfer Learning Benefits:")
print(f"✓ Reduces training time from ~80 hours to ~58 hours")
print(f"✓ Improves convergence stability")
print(f"✓ Leverages rich supervision from image-based models")
print(f"✓ Better feature alignment between domains")

print("\nTransfer learning demonstration completed!")
print("This approach enables effective knowledge transfer from image-based")
print("DensePose models to WiFi-based models, improving training efficiency.")