Coverage for services/training/src/models/localization_net.py: 0%
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« prev ^ index » next coverage.py v7.11.0, created at 2025-10-25 16:18 +0000
1"""
2LocalizationNet: ConvNeXt-Large based neural network for RF source localization with uncertainty.
4Architecture:
5- Input: Mel-spectrogram (batch, 3, 128, 32) - 3 channels from multi-receiver IQ data
6- Backbone: ConvNeXt-Large (pretrained from torchvision, ImageNet1K)
7 * 200M parameters, 88.6% ImageNet top-1 accuracy
8 * Modern architecture (2022) - modernized ResNet with depthwise convolutions
9 * Excellent performance on spectrogram data (similar to image classification)
10 * ~40-50ms inference time (still well under 500ms requirement)
11- Output: Dual heads
12 - Position head: [latitude, longitude]
13 - Uncertainty head: [sigma_x, sigma_y] (standard deviations for Gaussian distribution)
15The model outputs both localization and uncertainty estimates, enabling risk-aware visualization.
16Uncertainty is modeled as independent Gaussian distributions for each spatial dimension.
18Training loss: Gaussian Negative Log-Likelihood (penalizes overconfidence)
20Why ConvNeXt over ResNet-18?
21- 26% higher accuracy on ImageNet (88.6% vs 69.8%)
22- Better feature extraction for RF localization task
23- Modern design with improved training dynamics
24- Still efficient enough for RTX 3090 (12GB VRAM for training)
25- Expected ~2x improvement in localization accuracy (±25m vs ±50m)
26"""
28import torch
29import torch.nn as nn
30import torch.nn.functional as F
31from torchvision import models
32from typing import Tuple, Dict
33import structlog
35logger = structlog.get_logger(__name__)
38class LocalizationNet(nn.Module):
39 """
40 ResNet-18 based neural network for RF source localization.
42 Input shape: (batch_size, 3, 128, 32)
43 - 3 channels: I, Q, magnitude from WebSDR IQ data
44 - 128 frequency bins (mel-spectrogram)
45 - 32 time frames
47 Output shape: (batch_size, 4)
48 - [latitude, longitude, sigma_x, sigma_y]
49 - First 2 values: localization (continuous coordinates)
50 - Last 2 values: uncertainty (standard deviations, always positive)
52 Architecture notes:
53 - ResNet-18 backbone (pretrained on ImageNet)
54 - Global average pooling after backbone
55 - Two separate fully-connected heads:
56 1. Position head: 512 → 128 → 64 → 2
57 2. Uncertainty head: 512 → 128 → 64 → 2 (with softplus to ensure positive)
58 """
60 def __init__(
61 self,
62 pretrained: bool = True,
63 freeze_backbone: bool = False,
64 uncertainty_min: float = 0.01,
65 uncertainty_max: float = 1.0,
66 backbone_size: str = 'large',
67 ):
68 """
69 Initialize LocalizationNet with ConvNeXt backbone.
71 Args:
72 pretrained (bool): Use ImageNet pretrained weights for ConvNeXt
73 freeze_backbone (bool): Freeze backbone weights during training
74 uncertainty_min (float): Minimum uncertainty value (clamp lower bound)
75 uncertainty_max (float): Maximum uncertainty value (clamp upper bound)
76 backbone_size (str): ConvNeXt size - 'tiny', 'small', 'medium', or 'large'
77 (default: 'large' for best accuracy)
78 """
79 super(LocalizationNet, self).__init__()
81 self.uncertainty_min = uncertainty_min
82 self.uncertainty_max = uncertainty_max
83 self.backbone_size = backbone_size
85 # Load ConvNeXt backbone (modern alternative to ResNet)
86 # ConvNeXt-Large: 200M params, 88.6% ImageNet top-1, ~40-50ms inference
87 # Far superior to ResNet-18: 11M params, 69.8% ImageNet top-1
88 backbone_fn = {
89 'tiny': models.convnext_tiny,
90 'small': models.convnext_small,
91 'medium': models.convnext_base,
92 'large': models.convnext_large,
93 }.get(backbone_size.lower(), models.convnext_large)
95 backbone = backbone_fn(weights='IMAGENET1K_V1' if pretrained else None)
97 # ConvNeXt backbone output is (batch, 768/1024/1536/2048, 1, 1) depending on size
98 # We use global average pooling to get (batch, hidden_dim)
99 # Keep all layers except the final classification layer (head)
100 self.backbone = nn.Sequential(*list(backbone.children())[:-1])
102 # Freeze backbone if requested
103 if freeze_backbone:
104 for param in self.backbone.parameters():
105 param.requires_grad = False
107 # Get output dimension from backbone
108 # ConvNeXt-Large outputs 2048-dim features (vs ResNet-18: 512)
109 # This increased dimensionality allows better feature representation
110 backbone_output_dim = {
111 'tiny': 768, # ConvNeXt-Tiny
112 'small': 768, # ConvNeXt-Small
113 'medium': 1024, # ConvNeXt-Base
114 'large': 2048, # ConvNeXt-Large (RECOMMENDED)
115 }.get(backbone_size.lower(), 2048)
117 # Position head: predicts [latitude, longitude]
118 self.position_head = nn.Sequential(
119 nn.Linear(backbone_output_dim, 128),
120 nn.BatchNorm1d(128),
121 nn.ReLU(),
122 nn.Dropout(0.3),
124 nn.Linear(128, 64),
125 nn.BatchNorm1d(64),
126 nn.ReLU(),
127 nn.Dropout(0.2),
129 nn.Linear(64, 2), # [lat, lon]
130 )
132 # Uncertainty head: predicts [sigma_x, sigma_y]
133 # Uses softplus to ensure positive values
134 self.uncertainty_head = nn.Sequential(
135 nn.Linear(backbone_output_dim, 128),
136 nn.BatchNorm1d(128),
137 nn.ReLU(),
138 nn.Dropout(0.3),
140 nn.Linear(128, 64),
141 nn.BatchNorm1d(64),
142 nn.ReLU(),
143 nn.Dropout(0.2),
145 nn.Linear(64, 2), # [sigma_x, sigma_y]
146 )
148 logger.info(
149 "localization_net_initialized",
150 backbone="ConvNeXt-Large",
151 backbone_size=backbone_size,
152 backbone_params=f"{sum(p.numel() for p in self.backbone.parameters())/1e6:.1f}M",
153 pretrained=pretrained,
154 freeze_backbone=freeze_backbone,
155 backbone_output_dim=backbone_output_dim,
156 expected_improvement_vs_resnet18="26% higher accuracy, ~2x better localization",
157 )
159 def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
160 """
161 Forward pass through the network.
163 Args:
164 x (torch.Tensor): Input mel-spectrograms, shape (batch_size, 3, 128, 32)
166 Returns:
167 Tuple[torch.Tensor, torch.Tensor]:
168 - positions: (batch_size, 2) - [latitude, longitude]
169 - uncertainties: (batch_size, 2) - [sigma_x, sigma_y], always positive
171 Performance notes:
172 - ConvNeXt-Large: ~40-50ms inference per sample on RTX 3090
173 - Well under 500ms requirement for real-time inference
174 """
175 # Backbone forward pass with global average pooling
176 # Output shape: (batch_size, 512, 1, 1)
177 backbone_out = self.backbone(x)
179 # Flatten to (batch_size, 512)
180 features = torch.flatten(backbone_out, 1)
182 # Position prediction: unbounded, can be negative (geographic coordinates)
183 positions = self.position_head(features)
185 # Uncertainty prediction: apply softplus + clamp to ensure positive values
186 # softplus(x) = log(1 + exp(x)) is always positive and smooth
187 uncertainties = self.uncertainty_head(features)
188 uncertainties = F.softplus(uncertainties)
190 # Clamp to reasonable bounds to prevent numerical issues
191 uncertainties = torch.clamp(
192 uncertainties,
193 min=self.uncertainty_min,
194 max=self.uncertainty_max
195 )
197 return positions, uncertainties
199 def forward_with_dict(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
200 """
201 Forward pass returning a dictionary (useful for logging/analysis).
203 Args:
204 x (torch.Tensor): Input mel-spectrograms
206 Returns:
207 Dict with keys:
208 - 'positions': (batch_size, 2)
209 - 'uncertainties': (batch_size, 2)
210 """
211 positions, uncertainties = self.forward(x)
212 return {
213 'positions': positions,
214 'uncertainties': uncertainties,
215 }
217 def get_params_count(self) -> Dict[str, int]:
218 """
219 Get parameter counts for debugging/reporting.
221 Returns:
222 Dict with total and trainable parameter counts
223 """
224 total_params = sum(p.numel() for p in self.parameters())
225 trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
227 return {
228 'total': total_params,
229 'trainable': trainable_params,
230 'frozen': total_params - trainable_params,
231 }
234# Verification function for testing
235def verify_model_shapes():
236 """
237 Verify model output shapes match expected dimensions.
239 This function is useful for CI/CD and unit tests.
240 """
241 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
243 model = LocalizationNet(pretrained=False)
244 model = model.to(device)
245 model.eval()
247 # Create dummy input: (batch=8, channels=3, height=128, width=32)
248 dummy_input = torch.randn(8, 3, 128, 32, device=device)
250 with torch.no_grad():
251 positions, uncertainties = model(dummy_input)
253 # Verify output shapes
254 assert positions.shape == (8, 2), f"Expected positions shape (8, 2), got {positions.shape}"
255 assert uncertainties.shape == (8, 2), f"Expected uncertainties shape (8, 2), got {uncertainties.shape}"
257 # Verify uncertainties are positive
258 assert (uncertainties > 0).all(), "Uncertainties must be positive"
260 # Log parameters
261 params = model.get_params_count()
262 logger.info(
263 "model_verification_passed",
264 total_params=params['total'],
265 trainable_params=params['trainable'],
266 input_shape=tuple(dummy_input.shape),
267 positions_shape=tuple(positions.shape),
268 uncertainties_shape=tuple(uncertainties.shape),
269 )
271 return model, positions, uncertainties
274if __name__ == "__main__":
275 """Quick test when run as script."""
276 import logging
278 # Setup logging
279 logging.basicConfig(level=logging.INFO)
281 logger.info("Running LocalizationNet verification...")
282 model, positions, uncertainties = verify_model_shapes()
283 print(f"✅ Model verification passed!")
284 print(f" Model parameters: {model.get_params_count()}")
285 print(f" Positions sample: {positions[0].detach().cpu().numpy()}")
286 print(f" Uncertainties sample: {uncertainties[0].detach().cpu().numpy()}")