Create model.py

model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoModel, AutoConfig
+
+class EnhancedMoE(nn.Module):
+    def __init__(self, input_dim, num_experts=12, expert_dim=1024, dropout_rate=0.1):
+        super(EnhancedMoE, self).__init__()
+        self.num_experts = num_experts
+        # More sophisticated experts with two layers
+        self.experts = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(input_dim, expert_dim),
+                nn.ReLU(),
+                nn.Dropout(dropout_rate),
+                nn.Linear(expert_dim, expert_dim)
+            ) for _ in range(num_experts)
+        ])
+        # Improved gating with attention-like mechanism
+        self.gating_network = nn.Sequential(
+            nn.Linear(input_dim, expert_dim),
+            nn.ReLU(),
+            nn.Linear(expert_dim, num_experts)
+        )
+        self.layer_norm = nn.LayerNorm(expert_dim)
+
+    def forward(self, x):
+        gating_scores = F.softmax(self.gating_network(x), dim=-1)
+        expert_outputs = torch.stack([expert(x) for expert in self.experts], dim=1)
+        output = torch.sum(gating_scores.unsqueeze(-1) * expert_outputs, dim=1)
+        return self.layer_norm(output)
+
+class UltraSmarterModel(nn.Module):
+    def __init__(
+        self,
+        text_model_name="bert-base-uncased",
+        image_dim=2048,
+        audio_dim=512,
+        num_classes=None,
+        hidden_dim=1024
+    ):
+        super(UltraSmarterModel, self).__init__()
+        
+        # Text processing
+        self.text_config = AutoConfig.from_pretrained(text_model_name)
+        self.text_encoder = AutoModel.from_pretrained(text_model_name)
+        
+        # Enhanced modality experts
+        self.image_expert = EnhancedMoE(image_dim, expert_dim=hidden_dim)
+        self.audio_expert = EnhancedMoE(audio_dim, expert_dim=hidden_dim)
+        
+        # Cross-attention between modalities
+        self.cross_attention = nn.MultiheadAttention(
+            embed_dim=hidden_dim,
+            num_heads=8,
+            batch_first=True
+        )
+        
+        # Fusion and output
+        fused_dim = hidden_dim * 3  # Text + Image + Audio
+        self.fusion_layer = nn.Sequential(
+            nn.Linear(fused_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(0.1)
+        )
+        
+        # Flexible output layer (classification or regression)
+        self.output_dim = num_classes if num_classes else hidden_dim
+        self.output_layer = nn.Linear(hidden_dim, self.output_dim)
+        
+        # Additional improvements
+        self.layer_norm = nn.LayerNorm(hidden_dim)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, text_input, image_input, audio_input):
+        # Text features from CLS token
+        text_features = self.text_encoder(**text_input).last_hidden_state[:, 0, :]
+        text_features = self.dropout(F.relu(text_features))
+        
+        # Process image and audio through enhanced MoE
+        image_features = self.image_expert(image_input)
+        audio_features = self.audio_expert(audio_input)
+        
+        # Reshape for cross-attention (batch_size, seq_len=1, embed_dim)
+        text_features = text_features.unsqueeze(1)
+        image_features = image_features.unsqueeze(1)
+        audio_features = audio_features.unsqueeze(1)
+        
+        # Cross-attention between modalities
+        modality_features = torch.cat([text_features, image_features, audio_features], dim=1)
+        attn_output, _ = self.cross_attention(
+            modality_features, modality_features, modality_features
+        )
+        
+        # Fuse features
+        fused_features = attn_output.reshape(attn_output.size(0), -1)
+        fused_features = self.fusion_layer(fused_features)
+        fused_features = self.layer_norm(fused_features)
+        
+        # Final output
+        output = self.output_layer(fused_features)
+        
+        # Apply softmax/sigmoid if classification
+        if self.output_dim > 1:
+            return F.softmax(output, dim=-1)
+        return output
+
+# Example usage
+if __name__ == "__main__":
+    # Sample inputs
+    batch_size = 4
+    model = UltraSmarterModel(num_classes=10)  # For 10-class classification
+    
+    text_input = {
+        "input_ids": torch.randint(0, 1000, (batch_size, 128)),
+        "attention_mask": torch.ones(batch_size, 128)
+    }
+    image_input = torch.randn(batch_size, 2048)
+    audio_input = torch.randn(batch_size, 512)
+    
+    # Forward pass
+    output = model(text_input, image_input, audio_input)
+    print(f"Output shape: {output.shape}")  # Should be [batch_size, 10]