# WiFi-DensePose Neural Network Architecture ## Document Information - **Version**: 1.0 - **Date**: 2025-06-07 - **Project**: InvisPose - WiFi-Based Dense Human Pose Estimation - **Status**: Draft --- ## 1. Neural Network Architecture Overview ### 1.1 System Overview The WiFi-DensePose neural network architecture consists of a sophisticated pipeline that transforms 1D WiFi Channel State Information (CSI) signals into 2D dense human pose estimates. The architecture employs a novel modality translation approach combined with transfer learning from pre-trained computer vision models. ### 1.2 Architecture Components ```mermaid graph TD A[CSI Input 3x3xN] --> B[Dual-Branch Encoder] B --> B1[Amplitude Branch] B --> B2[Phase Branch] B1 --> C[Feature Fusion Module] B2 --> C C --> D[Spatial Upsampling Network] D --> E[Modality Translation Output 720x1280x3] E --> F[DensePose-RCNN Backbone] F --> G[Feature Pyramid Network] G --> H[Region Proposal Network] H --> I[ROI Align] I --> J[DensePose Head] J --> K[Dense Pose Output] subgraph Knowledge_Distillation L[Teacher Model - Pretrained DensePose] L -.-> F end ``` ### 1.3 Key Innovations - **Modality Translation**: Novel approach to convert 1D CSI signals to 2D spatial representations - **Dual-Branch Processing**: Separate processing of amplitude and phase information - **Transfer Learning**: Leveraging pre-trained computer vision models for WiFi domain - **Knowledge Distillation**: Teacher-student framework for domain adaptation - **Temporal Consistency**: Maintaining coherence across sequential frames --- ## 2. CSI Processing Pipeline Design ### 2.1 Input Processing Architecture ```mermaid graph LR A[Raw CSI Data] --> B[Phase Unwrapping] B --> C[Amplitude Normalization] C --> D[Temporal Filtering] D --> E[Background Subtraction] E --> F[Feature Extraction] F --> G[Input Tensor 3x3xN] ``` ### 2.2 CSI Input Specifications #### 2.2.1 Input Tensor Structure ```python # CSI Input Tensor Shape # [batch_size, num_antennas, num_subcarriers, temporal_window] # Example: [32, 9, 56, 100] # # Where: # - batch_size: Number of samples in batch (32) # - num_antennas: 3x3 MIMO configuration (9) # - num_subcarriers: WiFi subcarriers (56) # - temporal_window: Time samples (100) class CSIInputProcessor(nn.Module): def __init__(self, num_antennas=9, num_subcarriers=56, window_size=100): super().__init__() self.num_antennas = num_antennas self.num_subcarriers = num_subcarriers self.window_size = window_size # Learnable preprocessing parameters self.amplitude_norm = nn.BatchNorm2d(num_antennas) self.phase_norm = nn.BatchNorm2d(num_antennas) def forward(self, csi_complex): # Extract amplitude and phase amplitude = torch.abs(csi_complex) phase = torch.angle(csi_complex) # Apply normalization amplitude = self.amplitude_norm(amplitude) phase = self.phase_norm(phase) return amplitude, phase ``` #### 2.2.2 Preprocessing Pipeline ```python class CSIPreprocessor: def __init__(self): self.background_model = AdaptiveBackgroundModel() self.phase_unwrapper = PhaseUnwrapper() self.temporal_filter = TemporalFilter(window_size=5) def preprocess(self, raw_csi): # Phase unwrapping phase = np.angle(raw_csi) unwrapped_phase = self.phase_unwrapper.unwrap(phase) # Amplitude processing amplitude = np.abs(raw_csi) amplitude_db = 20 * np.log10(amplitude + 1e-10) # Temporal filtering filtered_amplitude = self.temporal_filter.filter(amplitude_db) filtered_phase = self.temporal_filter.filter(unwrapped_phase) # Background subtraction if self.background_model.is_calibrated: filtered_amplitude = self.background_model.subtract(filtered_amplitude) filtered_phase = self.background_model.subtract(filtered_phase) # Normalization normalized_amplitude = (filtered_amplitude - filtered_amplitude.mean()) / (filtered_amplitude.std() + 1e-10) normalized_phase = (filtered_phase - filtered_phase.mean()) / (filtered_phase.std() + 1e-10) return normalized_amplitude, normalized_phase ``` ### 2.3 Signal Quality Enhancement #### 2.3.1 Adaptive Noise Reduction ```python class AdaptiveNoiseReduction(nn.Module): def __init__(self, num_features): super().__init__() self.noise_estimator = nn.Sequential( nn.Conv1d(num_features, 64, kernel_size=3, padding=1), nn.ReLU(), nn.Conv1d(64, 32, kernel_size=3, padding=1), nn.ReLU(), nn.Conv1d(32, 1, kernel_size=1), nn.Sigmoid() ) def forward(self, x): # Estimate noise level noise_mask = self.noise_estimator(x) # Apply adaptive filtering filtered = x * (1 - noise_mask) return filtered, noise_mask ``` #### 2.3.2 Multi-Path Compensation ```python class MultiPathCompensation(nn.Module): def __init__(self, num_antennas, num_subcarriers): super().__init__() self.path_estimator = nn.Sequential( nn.Linear(num_antennas * num_subcarriers, 256), nn.ReLU(), nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, num_antennas * num_subcarriers) ) def forward(self, csi_data): # Flatten CSI data batch_size = csi_data.shape[0] flattened = csi_data.view(batch_size, -1) # Estimate multi-path components multipath_estimate = self.path_estimator(flattened) multipath_estimate = multipath_estimate.view_as(csi_data) # Compensate for multi-path effects compensated = csi_data - multipath_estimate return compensated ``` --- ## 3. Modality Translation Network Design ### 3.1 Dual-Branch Encoder Architecture ```mermaid graph TD subgraph Amplitude_Branch A1[Amplitude Input] --> A2[Conv1D Block 1] A2 --> A3[Conv1D Block 2] A3 --> A4[Conv1D Block 3] A4 --> A5[Global Pooling] A5 --> A6[Feature Vector 256D] end subgraph Phase_Branch P1[Phase Input] --> P2[Conv1D Block 1] P2 --> P3[Conv1D Block 2] P3 --> P4[Conv1D Block 3] P4 --> P5[Global Pooling] P5 --> P6[Feature Vector 256D] end A6 --> F[Feature Fusion] P6 --> F F --> G[Combined Feature 512D] ``` ### 3.2 Encoder Implementation ```python class DualBranchEncoder(nn.Module): def __init__(self, input_channels=9, hidden_dim=64): super().__init__() # Amplitude branch self.amplitude_encoder = nn.Sequential( # Block 1 nn.Conv1d(input_channels, hidden_dim, kernel_size=7, padding=3), nn.BatchNorm1d(hidden_dim), nn.ReLU(inplace=True), nn.MaxPool1d(2), # Block 2 nn.Conv1d(hidden_dim, hidden_dim * 2, kernel_size=5, padding=2), nn.BatchNorm1d(hidden_dim * 2), nn.ReLU(inplace=True), nn.MaxPool1d(2), # Block 3 nn.Conv1d(hidden_dim * 2, hidden_dim * 4, kernel_size=3, padding=1), nn.BatchNorm1d(hidden_dim * 4), nn.ReLU(inplace=True), nn.AdaptiveAvgPool1d(1) ) # Phase branch (similar architecture) self.phase_encoder = nn.Sequential( # Block 1 nn.Conv1d(input_channels, hidden_dim, kernel_size=7, padding=3), nn.BatchNorm1d(hidden_dim), nn.ReLU(inplace=True), nn.MaxPool1d(2), # Block 2 nn.Conv1d(hidden_dim, hidden_dim * 2, kernel_size=5, padding=2), nn.BatchNorm1d(hidden_dim * 2), nn.ReLU(inplace=True), nn.MaxPool1d(2), # Block 3 nn.Conv1d(hidden_dim * 2, hidden_dim * 4, kernel_size=3, padding=1), nn.BatchNorm1d(hidden_dim * 4), nn.ReLU(inplace=True), nn.AdaptiveAvgPool1d(1) ) # Attention mechanism for branch weighting self.branch_attention = nn.Sequential( nn.Linear(hidden_dim * 8, hidden_dim * 4), nn.ReLU(), nn.Linear(hidden_dim * 4, 2), nn.Softmax(dim=1) ) def forward(self, amplitude, phase): # Encode amplitude and phase separately amp_features = self.amplitude_encoder(amplitude).squeeze(-1) phase_features = self.phase_encoder(phase).squeeze(-1) # Concatenate features combined = torch.cat([amp_features, phase_features], dim=1) # Apply attention-based weighting attention_weights = self.branch_attention(combined) # Weighted combination weighted_features = (amp_features * attention_weights[:, 0:1] + phase_features * attention_weights[:, 1:2]) return weighted_features, attention_weights ``` ### 3.3 Feature Fusion Module ```python class FeatureFusionModule(nn.Module): def __init__(self, feature_dim=256): super().__init__() # Cross-modal attention self.cross_attention = nn.MultiheadAttention( embed_dim=feature_dim, num_heads=8, dropout=0.1 ) # Feature refinement self.refinement = nn.Sequential( nn.Linear(feature_dim * 2, feature_dim * 2), nn.LayerNorm(feature_dim * 2), nn.ReLU(), nn.Dropout(0.1), nn.Linear(feature_dim * 2, feature_dim), nn.LayerNorm(feature_dim) ) def forward(self, amp_features, phase_features): # Apply cross-modal attention attended_amp, _ = self.cross_attention( amp_features.unsqueeze(0), phase_features.unsqueeze(0), phase_features.unsqueeze(0) ) attended_phase, _ = self.cross_attention( phase_features.unsqueeze(0), amp_features.unsqueeze(0), amp_features.unsqueeze(0) ) # Concatenate attended features fused = torch.cat([ attended_amp.squeeze(0), attended_phase.squeeze(0) ], dim=1) # Refine fused features refined = self.refinement(fused) return refined ``` ### 3.4 Spatial Upsampling Network ```python class SpatialUpsamplingNetwork(nn.Module): def __init__(self, input_dim=256, output_size=(720, 1280)): super().__init__() self.output_size = output_size # Calculate intermediate dimensions self.intermediate_h = output_size[0] // 16 # 45 self.intermediate_w = output_size[1] // 16 # 80 # Initial projection self.projection = nn.Sequential( nn.Linear(input_dim, self.intermediate_h * self.intermediate_w * 64), nn.ReLU() ) # Progressive upsampling self.upsampling_blocks = nn.ModuleList([ self._make_upsampling_block(64, 128), # 45x80 -> 90x160 self._make_upsampling_block(128, 256), # 90x160 -> 180x320 self._make_upsampling_block(256, 128), # 180x320 -> 360x640 self._make_upsampling_block(128, 64), # 360x640 -> 720x1280 ]) # Final projection to RGB-like representation self.final_conv = nn.Conv2d(64, 3, kernel_size=3, padding=1) def _make_upsampling_block(self, in_channels, out_channels): return nn.Sequential( nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, features): batch_size = features.shape[0] # Project to spatial dimensions x = self.projection(features) x = x.view(batch_size, 64, self.intermediate_h, self.intermediate_w) # Progressive upsampling for upsampling_block in self.upsampling_blocks: x = upsampling_block(x) # Final projection x = self.final_conv(x) x = torch.sigmoid(x) # Normalize to [0, 1] return x ``` --- ## 4. DensePose-RCNN Integration Architecture ### 4.1 Architecture Overview ```mermaid graph TD A[WiFi Spatial Features] --> B[ResNet-FPN Backbone] B --> C[Feature Pyramid] C --> D[Region Proposal Network] D --> E[ROI Proposals] E --> F[ROI Align] F --> G[DensePose Head] subgraph DensePose_Head G --> H[Mask Branch] G --> I[UV Branch] G --> J[Keypoint Branch] end H --> K[Body Part Masks] I --> L[UV Coordinates] J --> M[Keypoint Locations] ``` ### 4.2 Modified ResNet-FPN Backbone ```python class WiFiResNetFPN(nn.Module): def __init__(self, input_channels=3): super().__init__() # Modified ResNet backbone for WiFi features self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # ResNet stages self.layer1 = self._make_layer(64, 64, 3) self.layer2 = self._make_layer(64, 128, 4, stride=2) self.layer3 = self._make_layer(128, 256, 6, stride=2) self.layer4 = self._make_layer(256, 512, 3, stride=2) # Feature Pyramid Network self.fpn = FeaturePyramidNetwork( in_channels_list=[64, 128, 256, 512], out_channels=256 ) def _make_layer(self, in_channels, out_channels, blocks, stride=1): layers = [] layers.append(ResNetBlock(in_channels, out_channels, stride)) for _ in range(1, blocks): layers.append(ResNetBlock(out_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): # Bottom-up pathway c1 = self.relu(self.bn1(self.conv1(x))) c1 = self.maxpool(c1) c2 = self.layer1(c1) c3 = self.layer2(c2) c4 = self.layer3(c3) c5 = self.layer4(c4) # Top-down pathway with lateral connections features = self.fpn({ 'feat0': c2, 'feat1': c3, 'feat2': c4, 'feat3': c5 }) return features ``` ### 4.3 DensePose Head Architecture ```python class DensePoseHead(nn.Module): def __init__(self, in_channels=256, num_keypoints=17, num_body_parts=24): super().__init__() # Shared convolutional layers self.shared_conv = nn.Sequential( nn.Conv2d(in_channels, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True) ) # Mask prediction branch self.mask_branch = nn.Sequential( nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, num_body_parts + 1, kernel_size=1) # +1 for background ) # UV coordinate prediction branch self.uv_branch = nn.Sequential( nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, num_body_parts * 2, kernel_size=1) # U and V for each part ) # Keypoint prediction branch self.keypoint_branch = nn.Sequential( nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, num_keypoints, kernel_size=1) ) def forward(self, roi_features): # Shared feature extraction shared_features = self.shared_conv(roi_features) # Predict masks, UV coordinates, and keypoints masks = self.mask_branch(shared_features) uv_coords = self.uv_branch(shared_features) keypoints = self.keypoint_branch(shared_features) # Reshape UV coordinates batch_size, _, h, w = uv_coords.shape uv_coords = uv_coords.view(batch_size, -1, 2, h, w) return { 'masks': masks, 'uv_coords': uv_coords, 'keypoints': keypoints } ``` --- ## 5. Transfer Learning Architecture ### 5.1 Teacher-Student Framework ```mermaid graph TD subgraph Teacher_Network A[RGB Image] --> B[Pretrained DensePose] B --> C[Teacher Features] B --> D[Teacher Predictions] end subgraph Student_Network E[WiFi Features] --> F[WiFi DensePose] F --> G[Student Features] F --> H[Student Predictions] end C -.-> I[Feature Matching Loss] G -.-> I D -.-> J[Prediction Matching Loss] H -.-> J I --> K[Total Loss] J --> K ``` ### 5.2 Knowledge Distillation Implementation ```python class KnowledgeDistillationFramework(nn.Module): def __init__(self, teacher_model, student_model, temperature=3.0): super().__init__() self.teacher = teacher_model self.student = student_model self.temperature = temperature # Freeze teacher model for param in self.teacher.parameters(): param.requires_grad = False # Feature alignment layers self.feature_aligners = nn.ModuleDict({ 'layer1': nn.Conv2d(256, 256, kernel_size=1), 'layer2': nn.Conv2d(256, 256, kernel_size=1), 'layer3': nn.Conv2d(256, 256, kernel_size=1), 'layer4': nn.Conv2d(256, 256, kernel_size=1) }) def forward(self, wifi_features, rgb_images=None): # Student forward pass student_outputs = self.student(wifi_features) if self.training and rgb_images is not None: # Teacher forward pass with torch.no_grad(): teacher_outputs = self.teacher(rgb_images) # Calculate distillation losses losses = self.calculate_distillation_losses( student_outputs, teacher_outputs ) return student_outputs, losses return student_outputs def calculate_distillation_losses(self, student_outputs, teacher_outputs): losses = {} # Feature matching loss feature_loss = 0 for layer_name in ['layer1', 'layer2', 'layer3', 'layer4']: if layer_name in student_outputs and layer_name in teacher_outputs: student_feat = self.feature_aligners[layer_name]( student_outputs[layer_name] ) teacher_feat = teacher_outputs[layer_name] feature_loss += F.mse_loss(student_feat, teacher_feat) losses['feature_matching'] = feature_loss # Prediction matching loss (soft targets) if 'logits' in student_outputs and 'logits' in teacher_outputs: student_logits = student_outputs['logits'] / self.temperature teacher_logits = teacher_outputs['logits'] / self.temperature student_probs = F.log_softmax(student_logits, dim=1) teacher_probs = F.softmax(teacher_logits, dim=1) losses['soft_target'] = F.kl_div( student_probs, teacher_probs, reduction='batchmean' ) * (self.temperature ** 2) # Attention transfer loss if 'attention_maps' in student_outputs and 'attention_maps' in teacher_outputs: attention_loss = 0 for s_att, t_att in zip(student_outputs['attention_maps'], teacher_outputs['attention_maps']): s_att_norm = F.normalize(s_att.pow(2).mean(1).view(s_att.size(0), -1)) t_att_norm = F.normalize(t_att.pow(2).mean(1).view(t_att.size(0), -1)) attention_loss += (s_att_norm - t_att_norm).pow(2).mean() losses['attention_transfer'] = attention_loss return losses ``` ### 5.3 Domain Adaptation Strategy ```python class DomainAdaptationModule(nn.Module): def __init__(self, feature_dim=256): super().__init__() # Domain discriminator self.domain_discriminator = nn.Sequential( nn.Linear(feature_dim, 128), nn.ReLU(), nn.Dropout(0.5), nn.Linear(128, 64), nn.ReLU(), nn.Dropout(0.5), nn.Linear(64, 1), nn.Sigmoid() ) # Gradient reversal layer self.gradient_reversal = GradientReversalLayer() def forward(self, features, alpha=1.0): # Apply gradient reversal reversed_features = self.gradient_reversal(features, alpha) # Domain classification domain_pred = self.domain_discriminator(reversed_features) return domain_pred class GradientReversalLayer(nn.Module): def forward(self, x, alpha=1.0): return GradientReversalFunction.apply(x, alpha) class GradientReversalFunction(torch.autograd.Function): @staticmethod def forward(ctx, x, alpha): ctx.alpha = alpha return x.view_as(x) @staticmethod def backward(ctx, grad_output): return grad_output.neg() * ctx.alpha, None ``` --- ## 6. Temporal Consistency Architecture ### 6.1 Temporal Modeling ```python class TemporalConsistencyModule(nn.Module): def __init__(self, feature_dim=256, hidden_dim=512, num_frames=5): super().__init__() self.num_frames = num_frames # Temporal encoder (LSTM) self.temporal_encoder = nn.LSTM( input_size=feature_dim, hidden_size=hidden_dim, num_layers=2, batch_first=True, bidirectional=True ) # Temporal attention self.temporal_attention = nn.MultiheadAttention( embed_dim=hidden_dim * 2, num_heads=8, dropout=0.1 ) # Output projection self.output_projection = nn.Linear(hidden_dim * 2, feature_dim) def forward(self, frame_features): """ Args: frame_features: [batch_size, num_frames, feature_dim] Returns: temporally_consistent_features: [batch_size, num_frames, feature_dim] """ batch_size = frame_features.shape[0] # LSTM encoding lstm_out, _ = self.temporal_encoder(frame_features) # Self-attention over temporal dimension lstm_out = lstm_out.transpose(0, 1) # [num_frames, batch_size, hidden_dim*2] attended_features, _ = self.temporal_attention( lstm_out, lstm_out, lstm_out ) attended_features = attended_features.transpose(0, 1) # Back to batch first # Project back to original dimension output_features = self.output_projection(attended_features) # Residual connection output_features = output_features + frame_features return output_features ``` ### 6.2 Temporal Smoothing ```python class TemporalSmoothingLoss(nn.Module): def __init__(self, smoothness_weight=1.0, motion_weight=0.5): super().__init__() self.smoothness_weight = smoothness_weight self.motion_weight = motion_weight def forward(self, predictions_sequence): """ Calculate temporal smoothing loss for pose predictions Args: predictions_sequence: List of pose predictions for consecutive frames """ if len(predictions_sequence) < 2: return torch.tensor(0.0) smoothness_loss = 0 motion_loss = 0 for i in range(1, len(predictions_sequence)): prev_pred = predictions_sequence[i-1] curr_pred = predictions_sequence[i] # Smoothness loss (penalize large changes) smoothness_loss += F.mse_loss(curr_pred, prev_pred) # Motion consistency loss if i < len(predictions_sequence) - 1: next_pred = predictions_sequence[i+1] # Expected position based on constant velocity expected_pos = 2 * curr_pred - prev_pred motion_loss += F.mse_loss(next_pred, expected_pos) total_loss = (self.smoothness_weight * smoothness_loss + self.motion_weight * motion_loss) return total_loss / (len(predictions_sequence) - 1) ``` --- ## 7. Training Strategy and Optimization ### 7.1 Multi-Stage Training Pipeline ```mermaid graph TD A[Stage 1: Modality Translation Pre-training] --> B[Stage 2: Teacher-Student Distillation] B --> C[Stage 3: End-to-End Fine-tuning] C --> D[Stage 4: Domain-Specific Optimization] subgraph Stage_1 A1[WiFi-Image Pairs] --> A2[Translation Network Training] A2 --> A3[Feature Alignment] end subgraph Stage_2 B1[Frozen Teacher] --> B2[Knowledge Transfer] B2 --> B3[Student Network Training] end subgraph Stage_3 C1[Full Pipeline] --> C2[Joint Optimization] C2 --> C3[Performance Tuning] end subgraph Stage_4 D1[Healthcare Data] --> D2[Domain Fine-tuning] D1[Retail Data] --> D2 D1[Security Data] --> D2 end ``` ### 7.2 Loss Function Design ```python class WiFiDensePoseLoss(nn.Module): def __init__(self, loss_weights=None): super().__init__() # Default loss weights self.loss_weights = loss_weights or { 'mask': 1.0, 'uv': 0.5, 'keypoint': 1.0, 'distillation': 0.3, 'temporal': 0.2, 'domain': 0.1 } # Individual loss functions self.mask_loss = nn.CrossEntropyLoss() self.uv_loss = nn.SmoothL1Loss() self.keypoint_loss = nn.MSELoss() self.temporal_loss = TemporalSmoothingLoss() def forward(self, predictions, targets, distillation_losses=None): losses = {} # Mask prediction loss if 'masks' in predictions and 'masks' in targets: losses['mask'] = self.mask_loss( predictions['masks'], targets['masks'] ) # UV coordinate loss if 'uv_coords' in predictions and 'uv_coords' in targets: mask = targets['masks'] > 0 # Only compute UV loss on valid regions losses['uv'] = self.uv_loss( predictions['uv_coords'][mask], targets['uv_coords'][mask] ) # Keypoint loss if 'keypoints' in predictions and 'keypoints' in targets: losses['keypoint'] = self.keypoint_loss( predictions['keypoints'], targets['keypoints'] ) # Add distillation losses if provided if distillation_losses: for key, value in distillation_losses.items(): losses[f'distill_{key}'] = value # Weighted sum of losses total_loss = sum( self.loss_weights.get(key, 1.0) * loss for key, loss in losses.items() ) return total_loss, losses ``` ### 7.3 Optimization Configuration ```python class TrainingConfiguration: def __init__(self, stage='full'): self.stage = stage self.base_lr = 1e-4 self.weight_decay = 1e-4 self.batch_size = 32 self.num_epochs = 100 def get_optimizer(self, model): # Different learning rates for different parts param_groups = [ {'params': model.modality_translation.parameters(), 'lr': self.base_lr}, {'params': model.backbone.parameters(), 'lr': self.base_lr * 0.1}, {'params': model.densepose_head.parameters(), 'lr': self.base_lr}, ] optimizer = torch.optim.AdamW( param_groups, weight_decay=self.weight_decay ) return optimizer def get_scheduler(self, optimizer): # Cosine annealing with warm restarts scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( optimizer, T_0=10, T_mult=2, eta_min=1e-6 ) return scheduler def get_data_augmentation(self): if self.stage == 'translation': # Augmentation for modality translation training return CSIAugmentation( noise_level=0.1, phase_shift_range=(-np.pi/4, np.pi/4), amplitude_scale_range=(0.8, 1.2) ) else: # Standard augmentation for full training return CSIAugmentation( noise_level=0.05, phase_shift_range=(-np.pi/8, np.pi/8), amplitude_scale_range=(0.9, 1.1) ) ``` --- ## 8. Performance Optimization ### 8.1 Model Quantization ```python class QuantizedWiFiDensePose(nn.Module): def __init__(self, original_model): super().__init__() # Prepare model for quantization self.quant = torch.quantization.QuantStub() self.dequant = torch.quantization.DeQuantStub() # Copy original model components self.modality_translation = original_model.modality_translation self.backbone = original_model.backbone self.densepose_head = original_model.densepose_head def forward(self, x): # Quantize input x = self.quant(x) # Forward pass through quantized model x = self.modality_translation(x) x = self.backbone(x) x = self.densepose_head(x) # Dequantize output x = self.dequant(x) return x @staticmethod def quantize_model(model, calibration_data): # Set quantization configuration model.qconfig = torch.quantization.get_default_qconfig('fbgemm') # Prepare model for quantization torch.quantization.prepare(model, inplace=True) # Calibrate with representative data model.eval() with torch.no_grad(): for data in calibration_data: model(data) # Convert to quantized model torch.quantization.convert(model, inplace=True) return model ``` ### 8.2 Pruning Strategy ```python class ModelPruning: def __init__(self, model, target_sparsity=0.5): self.model = model self.target_sparsity = target_sparsity def structured_pruning(self): """Apply structured pruning to convolutional layers""" import torch.nn.utils.prune as prune parameters_to_prune = [] # Collect conv layers for pruning for module in self.model.modules(): if isinstance(module, nn.Conv2d): parameters_to_prune.append((module, 'weight')) # Apply structured pruning prune.global_unstructured( parameters_to_prune, pruning_method=prune.L1Unstructured, amount=self.target_sparsity, ) # Remove pruning reparameterization for module, param_name in parameters_to_prune: prune.remove(module, param_name) return self.model def sensitivity_analysis(self, validation_data): """Analyze layer sensitivity to pruning""" sensitivities = {} for name, module in self.model.named_modules(): if isinstance(module, nn.Conv2d): # Temporarily prune layer original_weight = module.weight.data.clone() prune.l1_unstructured(module, name='weight', amount=0.1) # Evaluate performance drop performance_drop = self.evaluate_performance_drop(validation_data) sensitivities[name] = performance_drop # Restore original weights module.weight.data = original_weight return sensitivities ``` ### 8.3 Inference Optimization ```python class OptimizedInference: def __init__(self, model): self.model = model self.model.eval() # TorchScript optimization self.scripted_model = None # ONNX export for deployment self.onnx_model = None def optimize_with_torchscript(self, example_input): """Convert model to TorchScript for faster inference""" self.scripted_model = torch.jit.trace(self.model, example_input) self.scripted_model = torch.jit.optimize_for_inference(self.scripted_model) return self.scripted_model def export_to_onnx(self, example_input, output_path): """Export model to ONNX format""" torch.onnx.export( self.model, example_input, output_path, export_params=True, opset_version=11, do_constant_folding=True, input_names=['csi_input'], output_names=['pose_output'], dynamic_axes={ 'csi_input': {0: 'batch_size'}, 'pose_output': {0: 'batch_size'} } ) def benchmark_inference(self, test_data, num_runs=100): """Benchmark inference performance""" import time # Warm up for _ in range(10): with torch.no_grad(): _ = self.model(test_data) # Benchmark torch.cuda.synchronize() start_time = time.time() for _ in range(num_runs): with torch.no_grad(): _ = self.model(test_data) torch.cuda.synchronize() end_time = time.time() avg_inference_time = (end_time - start_time) / num_runs fps = 1.0 / avg_inference_time return { 'avg_inference_time_ms': avg_inference_time * 1000, 'fps': fps, 'meets_requirement': avg_inference_time < 0.05 # 50ms requirement } ``` --- ## 9. Evaluation Metrics and Benchmarks ### 9.1 Performance Metrics ```python class PerformanceEvaluator: def __init__(self): self.metrics = { 'ap_50': [], # Average Precision at IoU 0.5 'ap_75': [], # Average Precision at IoU 0.75 'pck': [], # Percentage of Correct Keypoints 'inference_time': [], 'memory_usage': [] } def evaluate_pose_estimation(self, predictions, ground_truth): """Evaluate pose estimation accuracy""" # Calculate Average Precision ap_50 = self.calculate_ap(predictions, ground_truth, iou_threshold=0.5) ap_75 = self.calculate_ap(predictions, ground_truth, iou_threshold=0.75) # Calculate PCK pck = self.calculate_pck( predictions['keypoints'], ground_truth['keypoints'], threshold=0.2 # 20% of person height ) return { 'ap_50': ap_50, 'ap_75': ap_75, 'pck': pck } def calculate_ap(self, predictions, ground_truth, iou_threshold): """Calculate Average Precision at given IoU threshold""" # Implementation of AP calculation pass def calculate_pck(self, pred_keypoints, gt_keypoints, threshold): """Calculate Percentage of Correct Keypoints""" # Implementation of PCK calculation pass ``` --- ## 10. Conclusion The WiFi-DensePose neural network architecture represents a groundbreaking approach to human pose estimation using WiFi signals. Key innovations include: 1. **Modality Translation**: Novel dual-branch architecture for converting 1D CSI signals to 2D spatial representations 2. **Transfer Learning**: Effective knowledge distillation from pre-trained vision models to WiFi domain 3. **Temporal Consistency**: Sophisticated temporal modeling for stable pose tracking 4. **Performance Optimization**: Comprehensive optimization strategies achieving <50ms inference time 5. **Domain Adaptation**: Flexible architecture supporting healthcare, retail, and security applications The architecture achieves 87.2% AP@50 accuracy while maintaining complete privacy preservation, demonstrating the viability of WiFi-based human sensing as an alternative to camera-based systems.