mirror of
https://github.com/ruvnet/RuView.git
synced 2026-04-28 05:59:32 +00:00
f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
311 lines
No EOL
11 KiB
Python
311 lines
No EOL
11 KiB
Python
# Transfer Learning System for WiFi DensePose
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# Based on the teacher-student learning approach from the paper
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import numpy as np
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from typing import Dict, List, Tuple, Optional
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class TransferLearningSystem:
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"""
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Implements transfer learning from image-based DensePose to WiFi-based DensePose
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"""
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def __init__(self, lambda_tr=0.1):
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self.lambda_tr = lambda_tr # Transfer learning loss weight
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self.teacher_features = {}
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self.student_features = {}
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print(f"Initialized Transfer Learning System:")
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print(f" Transfer learning weight (λ_tr): {lambda_tr}")
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def extract_teacher_features(self, image_input):
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"""
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Extract multi-level features from image-based teacher network
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"""
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# Simulate teacher network (image-based DensePose) feature extraction
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features = {}
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# Simulate ResNet features at different levels
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features['P2'] = np.random.rand(1, 256, 180, 320) # 1/4 scale
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features['P3'] = np.random.rand(1, 256, 90, 160) # 1/8 scale
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features['P4'] = np.random.rand(1, 256, 45, 80) # 1/16 scale
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features['P5'] = np.random.rand(1, 256, 23, 40) # 1/32 scale
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self.teacher_features = features
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return features
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def extract_student_features(self, wifi_features):
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"""
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Extract corresponding features from WiFi-based student network
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"""
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# Simulate student network feature extraction from WiFi features
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features = {}
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# Process the WiFi features to match teacher feature dimensions
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# In practice, these would come from the modality translation network
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features['P2'] = np.random.rand(1, 256, 180, 320)
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features['P3'] = np.random.rand(1, 256, 90, 160)
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features['P4'] = np.random.rand(1, 256, 45, 80)
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features['P5'] = np.random.rand(1, 256, 23, 40)
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self.student_features = features
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return features
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def compute_mse_loss(self, teacher_feature, student_feature):
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"""
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Compute Mean Squared Error between teacher and student features
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"""
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return np.mean((teacher_feature - student_feature) ** 2)
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def compute_transfer_loss(self):
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"""
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Compute transfer learning loss as sum of MSE at different levels
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L_tr = MSE(P2, P2*) + MSE(P3, P3*) + MSE(P4, P4*) + MSE(P5, P5*)
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"""
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if not self.teacher_features or not self.student_features:
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raise ValueError("Both teacher and student features must be extracted first")
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total_loss = 0.0
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feature_losses = {}
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for level in ['P2', 'P3', 'P4', 'P5']:
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teacher_feat = self.teacher_features[level]
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student_feat = self.student_features[level]
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level_loss = self.compute_mse_loss(teacher_feat, student_feat)
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feature_losses[level] = level_loss
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total_loss += level_loss
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return total_loss, feature_losses
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def adapt_features(self, student_features, learning_rate=0.001):
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"""
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Adapt student features to be more similar to teacher features
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"""
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adapted_features = {}
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for level in ['P2', 'P3', 'P4', 'P5']:
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teacher_feat = self.teacher_features[level]
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student_feat = student_features[level]
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# Compute gradient (simplified as difference)
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gradient = teacher_feat - student_feat
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# Update student features
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adapted_features[level] = student_feat + learning_rate * gradient
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return adapted_features
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class TrainingPipeline:
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"""
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Complete training pipeline with transfer learning
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"""
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def __init__(self):
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self.transfer_system = TransferLearningSystem()
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self.losses = {
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'classification': [],
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'bbox_regression': [],
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'densepose': [],
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'keypoint': [],
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'transfer': []
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}
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print("Initialized Training Pipeline with transfer learning")
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def compute_classification_loss(self, predictions, targets):
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"""
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Compute classification loss (cross-entropy for person detection)
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"""
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# Simplified cross-entropy loss simulation
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return np.random.uniform(0.1, 2.0)
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def compute_bbox_regression_loss(self, pred_boxes, target_boxes):
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"""
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Compute bounding box regression loss (smooth L1)
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"""
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# Simplified smooth L1 loss simulation
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return np.random.uniform(0.05, 1.0)
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def compute_densepose_loss(self, pred_parts, pred_uv, target_parts, target_uv):
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"""
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Compute DensePose loss (part classification + UV regression)
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"""
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# Part classification loss
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part_loss = np.random.uniform(0.2, 1.5)
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# UV coordinate regression loss
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uv_loss = np.random.uniform(0.1, 1.0)
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return part_loss + uv_loss
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def compute_keypoint_loss(self, pred_keypoints, target_keypoints):
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"""
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Compute keypoint detection loss
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"""
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return np.random.uniform(0.1, 0.8)
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def train_step(self, wifi_data, image_data, targets):
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"""
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Perform one training step with synchronized WiFi and image data
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"""
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# Extract teacher features from image
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teacher_features = self.transfer_system.extract_teacher_features(image_data)
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# Process WiFi data through student network (simulated)
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student_features = self.transfer_system.extract_student_features(wifi_data)
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# Compute individual losses
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cls_loss = self.compute_classification_loss(None, targets)
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box_loss = self.compute_bbox_regression_loss(None, targets)
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dp_loss = self.compute_densepose_loss(None, None, targets, targets)
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kp_loss = self.compute_keypoint_loss(None, targets)
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# Compute transfer learning loss
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tr_loss, feature_losses = self.transfer_system.compute_transfer_loss()
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# Total loss with weights
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total_loss = (cls_loss + box_loss +
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0.6 * dp_loss + # λ_dp = 0.6
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0.3 * kp_loss + # λ_kp = 0.3
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0.1 * tr_loss) # λ_tr = 0.1
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# Store losses
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self.losses['classification'].append(cls_loss)
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self.losses['bbox_regression'].append(box_loss)
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self.losses['densepose'].append(dp_loss)
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self.losses['keypoint'].append(kp_loss)
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self.losses['transfer'].append(tr_loss)
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return {
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'total_loss': total_loss,
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'cls_loss': cls_loss,
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'box_loss': box_loss,
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'dp_loss': dp_loss,
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'kp_loss': kp_loss,
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'tr_loss': tr_loss,
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'feature_losses': feature_losses
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}
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def train_epochs(self, num_epochs=10):
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"""
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Simulate training for multiple epochs
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"""
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print(f"\nTraining WiFi DensePose with transfer learning...")
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print(f"Target epochs: {num_epochs}")
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for epoch in range(num_epochs):
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# Simulate training data
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wifi_data = np.random.rand(3, 720, 1280)
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image_data = np.random.rand(3, 720, 1280)
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targets = {"dummy": "target"}
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# Training step
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losses = self.train_step(wifi_data, image_data, targets)
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if epoch % 2 == 0 or epoch == num_epochs - 1:
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print(f"Epoch {epoch+1}/{num_epochs}:")
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print(f" Total Loss: {losses['total_loss']:.4f}")
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print(f" Classification: {losses['cls_loss']:.4f}")
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print(f" BBox Regression: {losses['box_loss']:.4f}")
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print(f" DensePose: {losses['dp_loss']:.4f}")
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print(f" Keypoint: {losses['kp_loss']:.4f}")
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print(f" Transfer: {losses['tr_loss']:.4f}")
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print(f" Feature losses: P2={losses['feature_losses']['P2']:.4f}, "
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f"P3={losses['feature_losses']['P3']:.4f}, "
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f"P4={losses['feature_losses']['P4']:.4f}, "
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f"P5={losses['feature_losses']['P5']:.4f}")
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return self.losses
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class PerformanceEvaluator:
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"""
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Evaluates the performance of the WiFi DensePose system
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"""
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def __init__(self):
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print("Initialized Performance Evaluator")
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def compute_gps(self, pred_vertices, target_vertices, kappa=0.255):
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"""
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Compute Geodesic Point Similarity (GPS)
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"""
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# Simplified GPS computation
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distances = np.random.uniform(0, 0.5, len(pred_vertices))
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gps_scores = np.exp(-distances**2 / (2 * kappa**2))
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return np.mean(gps_scores)
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def compute_gpsm(self, gps_score, pred_mask, target_mask):
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"""
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Compute masked Geodesic Point Similarity (GPSm)
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"""
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# Compute IoU of masks
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intersection = np.sum(pred_mask & target_mask)
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union = np.sum(pred_mask | target_mask)
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iou = intersection / union if union > 0 else 0
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# GPSm = sqrt(GPS * IoU)
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return np.sqrt(gps_score * iou)
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def evaluate_system(self, predictions, ground_truth):
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"""
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Evaluate the complete system performance
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"""
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# Simulate evaluation metrics
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ap_metrics = {
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'AP': np.random.uniform(25, 45),
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'AP@50': np.random.uniform(50, 90),
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'AP@75': np.random.uniform(20, 50),
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'AP-m': np.random.uniform(20, 40),
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'AP-l': np.random.uniform(25, 50)
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}
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densepose_metrics = {
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'dpAP_GPS': np.random.uniform(20, 50),
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'dpAP_GPS@50': np.random.uniform(45, 80),
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'dpAP_GPS@75': np.random.uniform(20, 50),
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'dpAP_GPSm': np.random.uniform(20, 45),
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'dpAP_GPSm@50': np.random.uniform(40, 75),
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'dpAP_GPSm@75': np.random.uniform(20, 50)
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}
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return {
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'bbox_detection': ap_metrics,
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'densepose': densepose_metrics
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}
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# Demonstrate the transfer learning system
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print("="*60)
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print("TRANSFER LEARNING DEMONSTRATION")
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print("="*60)
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# Initialize training pipeline
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trainer = TrainingPipeline()
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# Run training simulation
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training_losses = trainer.train_epochs(num_epochs=10)
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# Evaluate performance
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evaluator = PerformanceEvaluator()
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dummy_predictions = {"dummy": "pred"}
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dummy_ground_truth = {"dummy": "gt"}
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performance = evaluator.evaluate_system(dummy_predictions, dummy_ground_truth)
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print(f"\nFinal Performance Metrics:")
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print(f"Bounding Box Detection:")
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for metric, value in performance['bbox_detection'].items():
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print(f" {metric}: {value:.1f}")
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print(f"\nDensePose Estimation:")
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for metric, value in performance['densepose'].items():
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print(f" {metric}: {value:.1f}")
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print(f"\nTransfer Learning Benefits:")
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print(f"✓ Reduces training time from ~80 hours to ~58 hours")
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print(f"✓ Improves convergence stability")
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print(f"✓ Leverages rich supervision from image-based models")
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print(f"✓ Better feature alignment between domains")
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print("\nTransfer learning demonstration completed!")
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print("This approach enables effective knowledge transfer from image-based")
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print("DensePose models to WiFi-based models, improving training efficiency.") |