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| from dataclasses import dataclass | |
| from typing import Dict, Any, List | |
| import torch | |
| import torch.nn as nn | |
| class PerceptionState: | |
| visual_data: torch.Tensor | |
| audio_data: torch.Tensor | |
| text_data: torch.Tensor | |
| context_vector: torch.Tensor | |
| attention_weights: Dict[str, float] | |
| class VisualProcessor(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| # Visual processing layers would be defined here | |
| def forward(self, visual_input): | |
| # Process visual input | |
| return visual_input if visual_input is not None else torch.zeros(1) | |
| class AudioProcessor(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| # Audio processing layers would be defined here | |
| def forward(self, audio_input): | |
| # Process audio input | |
| return audio_input if audio_input is not None else torch.zeros(1) | |
| class TextProcessor(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| # Text processing layers would be defined here | |
| def forward(self, text_input): | |
| # Process text input | |
| return text_input if text_input is not None else torch.zeros(1) | |
| class ModalityFusion(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| # Fusion layers would be defined here | |
| def forward(self, visual, audio, text): | |
| # Fusion logic | |
| return torch.cat([visual, audio, text], dim=-1) if all(x is not None for x in [visual, audio, text]) else torch.zeros(1) | |
| class MultiModalEncoder(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.visual_encoder = VisualProcessor() | |
| self.audio_encoder = AudioProcessor() | |
| self.text_encoder = TextProcessor() | |
| self.fusion_layer = ModalityFusion() | |
| def forward(self, inputs: Dict[str, torch.Tensor]) -> PerceptionState: | |
| visual_features = self.visual_encoder(inputs.get('visual')) | |
| audio_features = self.audio_encoder(inputs.get('audio')) | |
| text_features = self.text_encoder(inputs.get('text')) | |
| fused_representation = self.fusion_layer( | |
| visual_features, | |
| audio_features, | |
| text_features | |
| ) | |
| return self._create_perception_state(visual_features, audio_features, text_features, fused_representation) | |
| def _create_perception_state(self, visual_features, audio_features, text_features, fused_representation): | |
| # Create an attention weights dictionary | |
| attention_weights = { | |
| 'visual': 0.33, | |
| 'audio': 0.33, | |
| 'text': 0.34 | |
| } | |
| return PerceptionState( | |
| visual_data=visual_features, | |
| audio_data=audio_features, | |
| text_data=text_features, | |
| context_vector=fused_representation, | |
| attention_weights=attention_weights | |
| ) | |