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from dataclasses import dataclass
from typing import Dict, Any, List
import torch
import torch.nn as nn
@dataclass
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
)
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