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CLARA Model Architecture
Implements the CLARA model with:
- CLIP Vision Encoder + LoRA
- DeBERTa Text Encoder + LoRA
- Bidirectional Co-Attention Fusion
- Verification Module
- Feedback Module
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPVisionModel, CLIPProcessor, DebertaV2Model, DebertaV2Tokenizer
from peft import get_peft_model, LoraConfig, TaskType
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
@dataclass
class CLARAConfig:
"""Configuration for CLARA model"""
# Encoder settings
vision_encoder: str = "openai/clip-vit-base-patch16"
text_encoder: str = "microsoft/deberta-v3-base"
# LoRA settings
lora_rank: int = 8
lora_alpha: int = 16
lora_dropout: float = 0.05
# Co-attention settings
hidden_dim: int = 512
num_attention_heads: int = 8
num_attention_layers: int = 2
attention_dropout: float = 0.1
# Classification settings
num_classes: int = 3 # Positive, Neutral, Negative
# Other settings
dropout: float = 0.1
freeze_encoders: bool = True
class CoAttentionLayer(nn.Module):
"""Bidirectional Co-Attention Layer"""
def __init__(self, hidden_dim: int, num_heads: int, dropout: float = 0.1):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
# Text queries Vision
self.text_to_vision_attn = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=num_heads,
dropout=dropout,
batch_first=True
)
# Vision queries Text
self.vision_to_text_attn = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=num_heads,
dropout=dropout,
batch_first=True
)
# Layer normalization
self.text_norm = nn.LayerNorm(hidden_dim)
self.vision_norm = nn.LayerNorm(hidden_dim)
# Feed-forward
self.text_ffn = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim * 4, hidden_dim),
nn.Dropout(dropout)
)
self.vision_ffn = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim * 4, hidden_dim),
nn.Dropout(dropout)
)
self.text_ffn_norm = nn.LayerNorm(hidden_dim)
self.vision_ffn_norm = nn.LayerNorm(hidden_dim)
def forward(
self,
text_features: torch.Tensor,
vision_features: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
text_features: [batch_size, seq_len, hidden_dim]
vision_features: [batch_size, num_patches, hidden_dim]
Returns:
text_out: [batch_size, seq_len, hidden_dim]
vision_out: [batch_size, num_patches, hidden_dim]
"""
# Text queries Vision
text_attn_out, _ = self.text_to_vision_attn(
query=text_features,
key=vision_features,
value=vision_features
)
text_features = self.text_norm(text_features + text_attn_out)
# Vision queries Text
vision_attn_out, _ = self.vision_to_text_attn(
query=vision_features,
key=text_features,
value=text_features
)
vision_features = self.vision_norm(vision_features + vision_attn_out)
# Feed-forward
text_out = self.text_ffn_norm(text_features + self.text_ffn(text_features))
vision_out = self.vision_ffn_norm(vision_features + self.vision_ffn(vision_features))
return text_out, vision_out
class VerificationModule(nn.Module):
"""Computes unimodal predictions and consensus signal"""
def __init__(self, hidden_dim: int, num_classes: int, dropout: float = 0.1):
super().__init__()
self.vision_classifier = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, num_classes)
)
self.text_classifier = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, num_classes)
)
def forward(
self,
vision_features: torch.Tensor,
text_features: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
vision_features: [batch_size, hidden_dim]
text_features: [batch_size, hidden_dim]
Returns:
vision_logits: [batch_size, num_classes]
text_logits: [batch_size, num_classes]
consensus: [batch_size, num_classes] - agreement signal
"""
vision_logits = self.vision_classifier(vision_features)
text_logits = self.text_classifier(text_features)
# Consensus signal: absolute difference of probabilities
vision_probs = F.softmax(vision_logits, dim=-1)
text_probs = F.softmax(text_logits, dim=-1)
consensus = torch.abs(vision_probs - text_probs)
return vision_logits, text_logits, consensus
class FeedbackModule(nn.Module):
"""Refines predictions using consensus signal"""
def __init__(self, hidden_dim: int, num_classes: int, dropout: float = 0.1):
super().__init__()
self.refinement = nn.Sequential(
nn.Linear(hidden_dim + num_classes, hidden_dim),
nn.GELU(),
nn.LayerNorm(hidden_dim),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim // 2),
nn.GELU(),
nn.Linear(hidden_dim // 2, num_classes)
)
def forward(
self,
fused_features: torch.Tensor,
consensus: torch.Tensor
) -> torch.Tensor:
"""
Args:
fused_features: [batch_size, hidden_dim]
consensus: [batch_size, num_classes]
Returns:
refined_logits: [batch_size, num_classes]
"""
# Concatenate fused features with consensus signal
combined = torch.cat([fused_features, consensus], dim=-1)
refined_logits = self.refinement(combined)
return refined_logits
class CLARAModel(nn.Module):
"""CLARA: Co-attention Learning for Robust multimodal sentiment Analysis"""
def __init__(self, config: CLARAConfig):
super().__init__()
self.config = config
# Initialize encoders
self.vision_encoder = CLIPVisionModel.from_pretrained(config.vision_encoder)
self.text_encoder = DebertaV2Model.from_pretrained(config.text_encoder)
# Freeze encoders if specified
if config.freeze_encoders:
for param in self.vision_encoder.parameters():
param.requires_grad = False
for param in self.text_encoder.parameters():
param.requires_grad = False
# Apply LoRA to vision encoder
vision_lora_config = LoraConfig(
r=config.lora_rank,
lora_alpha=config.lora_alpha,
target_modules=["q_proj", "v_proj"],
lora_dropout=config.lora_dropout,
bias="none",
task_type=TaskType.FEATURE_EXTRACTION
)
self.vision_encoder = get_peft_model(self.vision_encoder, vision_lora_config)
# Apply LoRA to text encoder (layers 0-10, full layer 11)
text_lora_config = LoraConfig(
r=config.lora_rank,
lora_alpha=config.lora_alpha,
target_modules=["query_proj", "value_proj"],
layers_to_transform=list(range(11)), # 0-10
lora_dropout=config.lora_dropout,
bias="none",
task_type=TaskType.FEATURE_EXTRACTION
)
self.text_encoder = get_peft_model(self.text_encoder, text_lora_config)
# Projection layers
vision_hidden_size = self.vision_encoder.config.hidden_size
text_hidden_size = self.text_encoder.config.hidden_size
self.vision_projection = nn.Linear(vision_hidden_size, config.hidden_dim)
self.text_projection = nn.Linear(text_hidden_size, config.hidden_dim)
# Co-attention layers
self.co_attention_layers = nn.ModuleList([
CoAttentionLayer(
hidden_dim=config.hidden_dim,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout
)
for _ in range(config.num_attention_layers)
])
# Verification module
self.verification = VerificationModule(
hidden_dim=config.hidden_dim,
num_classes=config.num_classes,
dropout=config.dropout
)
# Prediction head
self.prediction_head = nn.Sequential(
nn.Linear(config.hidden_dim, config.hidden_dim),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.hidden_dim, config.num_classes)
)
# Feedback module
self.feedback = FeedbackModule(
hidden_dim=config.hidden_dim,
num_classes=config.num_classes,
dropout=config.dropout
)
def forward(
self,
pixel_values: torch.Tensor,
input_ids: torch.Tensor,
attention_mask: torch.Tensor
) -> Dict[str, torch.Tensor]:
"""
Args:
pixel_values: [batch_size, 3, 224, 224]
input_ids: [batch_size, seq_len]
attention_mask: [batch_size, seq_len]
Returns:
Dictionary with:
- logits: [batch_size, num_classes]
- vision_logits: [batch_size, num_classes]
- text_logits: [batch_size, num_classes]
- consensus: [batch_size, num_classes]
"""
# Encode vision
vision_outputs = self.vision_encoder(pixel_values=pixel_values)
vision_features = vision_outputs.last_hidden_state # [B, num_patches, hidden]
vision_features = self.vision_projection(vision_features)
# Encode text
text_outputs = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask)
text_features = text_outputs.last_hidden_state # [B, seq_len, hidden]
text_features = self.text_projection(text_features)
# Co-attention fusion
for co_attn_layer in self.co_attention_layers:
text_features, vision_features = co_attn_layer(text_features, vision_features)
# Pool features
vision_pooled = vision_features.mean(dim=1) # [B, hidden_dim]
text_pooled = text_features.mean(dim=1) # [B, hidden_dim]
fused_features = (vision_pooled + text_pooled) / 2 # [B, hidden_dim]
# Verification: unimodal predictions + consensus
vision_logits, text_logits, consensus = self.verification(
vision_pooled, text_pooled
)
# Fused prediction
fused_logits = self.prediction_head(fused_features)
# Feedback: refine with consensus
final_logits = self.feedback(fused_features, consensus)
final_logits = final_logits + fused_logits # Residual connection
return {
"logits": final_logits,
"vision_logits": vision_logits,
"text_logits": text_logits,
"consensus": consensus
}
@classmethod
def from_pretrained(cls, checkpoint_path: str, config: Optional[CLARAConfig] = None):
"""Load model from checkpoint"""
checkpoint = torch.load(checkpoint_path, map_location="cpu")
if config is None:
config = CLARAConfig(**checkpoint.get("config", {}))
model = cls(config)
model.load_state_dict(checkpoint["model_state_dict"])
return model
def save_pretrained(self, save_path: str, **kwargs):
"""Save model checkpoint"""
torch.save({
"model_state_dict": self.state_dict(),
"config": self.config.__dict__,
**kwargs
}, save_path)
def predict(self, pixel_values: torch.Tensor, input_ids: torch.Tensor, attention_mask: torch.Tensor):
"""Inference method"""
self.eval()
with torch.no_grad():
outputs = self.forward(pixel_values, input_ids, attention_mask)
probs = F.softmax(outputs["logits"], dim=-1)
preds = torch.argmax(probs, dim=-1)
confidence, _ = torch.max(probs, dim=-1)
return {
"predictions": preds,
"probabilities": probs,
"confidence": confidence
}
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