Implement CSI processing and phase sanitization modules; add unit tests for DensePose and modality translation networks

src/models/modality_translation.py

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+"""Modality translation network for WiFi-DensePose system."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Dict, Any
+
+
+class ModalityTranslationNetwork(nn.Module):
+    """Neural network for translating CSI data to visual feature space."""
+    
+    def __init__(self, config: Dict[str, Any]):
+        """Initialize modality translation network.
+        
+        Args:
+            config: Configuration dictionary with network parameters
+        """
+        super().__init__()
+        
+        self.input_channels = config['input_channels']
+        self.hidden_dim = config['hidden_dim']
+        self.output_dim = config['output_dim']
+        self.num_layers = config['num_layers']
+        self.dropout_rate = config['dropout_rate']
+        
+        # Encoder: CSI -> Feature space
+        self.encoder = self._build_encoder()
+        
+        # Decoder: Feature space -> Visual-like features
+        self.decoder = self._build_decoder()
+        
+        # Initialize weights
+        self._initialize_weights()
+    
+    def _build_encoder(self) -> nn.Module:
+        """Build encoder network."""
+        layers = []
+        
+        # Initial convolution
+        layers.append(nn.Conv2d(self.input_channels, 64, kernel_size=3, padding=1))
+        layers.append(nn.BatchNorm2d(64))
+        layers.append(nn.ReLU(inplace=True))
+        layers.append(nn.Dropout2d(self.dropout_rate))
+        
+        # Progressive downsampling
+        in_channels = 64
+        for i in range(self.num_layers - 1):
+            out_channels = min(in_channels * 2, self.hidden_dim)
+            layers.extend([
+                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
+                nn.BatchNorm2d(out_channels),
+                nn.ReLU(inplace=True),
+                nn.Dropout2d(self.dropout_rate)
+            ])
+            in_channels = out_channels
+        
+        return nn.Sequential(*layers)
+    
+    def _build_decoder(self) -> nn.Module:
+        """Build decoder network."""
+        layers = []
+        
+        # Get the actual output channels from encoder (should be hidden_dim)
+        encoder_out_channels = self.hidden_dim
+        
+        # Progressive upsampling
+        in_channels = encoder_out_channels
+        for i in range(self.num_layers - 1):
+            out_channels = max(in_channels // 2, 64)
+            layers.extend([
+                nn.ConvTranspose2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, output_padding=1),
+                nn.BatchNorm2d(out_channels),
+                nn.ReLU(inplace=True),
+                nn.Dropout2d(self.dropout_rate)
+            ])
+            in_channels = out_channels
+        
+        # Final output layer
+        layers.append(nn.Conv2d(in_channels, self.output_dim, kernel_size=3, padding=1))
+        layers.append(nn.Tanh())  # Normalize output
+        
+        return nn.Sequential(*layers)
+    
+    def _initialize_weights(self):
+        """Initialize network weights."""
+        for m in self.modules():
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass through the network.
+        
+        Args:
+            x: Input CSI tensor of shape (batch_size, channels, height, width)
+            
+        Returns:
+            Translated features tensor
+        """
+        # Validate input shape
+        if x.shape[1] != self.input_channels:
+            raise RuntimeError(f"Expected {self.input_channels} input channels, got {x.shape[1]}")
+        
+        # Encode CSI data
+        encoded = self.encoder(x)
+        
+        # Decode to visual-like features
+        decoded = self.decoder(encoded)
+        
+        return decoded