Update model.py

model.py

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+from timm import create_model
+import torch
+import torch.nn as nn
+from transformers import RobertaModel
+
+EMBEDDING_DIM = 512
+
+class ImageEncoder(nn.Module):
+    def __init__(self):
+        super(ImageEncoder, self).__init__()
+        # Load the Swin Transformer with features_only=True
+        self.swin = create_model("swin_base_patch4_window7_224.ms_in22k", pretrained=True, features_only=True)
+        for param in self.swin.parameters():
+            param.requires_grad = True
+
+        # Get the feature size of the final stage
+        self.swin_output_dim = self.swin.feature_info.channels()[-1]  # Last stage: 1024 channels
+
+        # Define FC layer
+        self.fc1 = nn.Linear(self.swin_output_dim * 7 * 7, EMBEDDING_DIM)  # Flattened input size
+        nn.init.xavier_uniform_(self.fc1.weight)
+        nn.init.zeros_(self.fc1.bias)
+        for param in self.fc1.parameters():
+            param.requires_grad = True
+
+
+    def forward(self, x):
+        # Extract features from Swin
+        swin_features = self.swin(x)[-1]  # Use the last stage feature map (e.g., [B, 1024, 7, 7])
+
+        # Flatten feature map
+        swin_features = swin_features.view(swin_features.size(0), -1)  # Shape: (B, 1024*7*7)
+
+        # Pass through FC layer
+        output = self.fc1(swin_features)  # Shape: (B, embedding_dim)
+        return output
+
+class RobertaEncoder(nn.Module):
+    def __init__(self, roberta_model_path="roberta-base"):
+        super(RobertaEncoder, self).__init__()
+        # Load pre-trained RoBERTa model
+        self.roberta = RobertaModel.from_pretrained(roberta_model_path)
+
+        # Add a linear projection layer to reduce dimensionality
+        self.projection = nn.Linear(self.roberta.config.hidden_size, EMBEDDING_DIM)
+
+        # Initialize the projection layer weights
+        nn.init.xavier_uniform_(self.projection.weight)
+        nn.init.zeros_(self.projection.bias)
+
+        # Allow fine-tuning of the RoBERTa model
+        for param in self.roberta.parameters():
+            param.requires_grad = True
+
+    def forward(self, input_ids, attention_mask):
+        """
+        Forward pass through RoBERTa.
+        Args:
+            input_ids: Tensor of shape (batch_size, seq_length)
+            attention_mask: Tensor of shape (batch_size, seq_length)
+
+        Returns:
+            Embedding: Tensor of shape (batch_size, EMBEDDING_DIM)
+        """
+        roberta_output = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
+        cls_token = roberta_output.last_hidden_state[:, 0, :]  # Use CLS token
+        pooled_output = torch.mean(roberta_output.last_hidden_state, dim=1)  # Mean pooling
+
+        return self.projection(cls_token+pooled_output)
+        
+class LVL(nn.Module):
+    def __init__(self):
+        super(LVL, self).__init__()
+        self.image_encoder = ImageEncoder()
+        self.text_encoder = RobertaEncoder()
+        self.t_prime = nn.Parameter(torch.ones([]) * np.log(0.07))
+        self.b = nn.Parameter(torch.ones([]) * 0)
+
+    def get_images_features(self,images):
+        image_embeddings = self.image_encoder(images)  # (batch_size, EMBEDDING_DIM)
+        image_embeddings = nn.functional.normalize(image_embeddings, p=2, dim=-1)
+        return image_embeddings
+
+    def get_texts_feature(self,input_ids,attention_mask):
+        text_embeddings = self.text_encoder(input_ids, attention_mask)  # (batch_size, EMBEDDING_DIM)
+        text_embeddings = nn.functional.normalize(text_embeddings, p=2, dim=-1)
+        return text_embeddings
+
+    def forward(self, images, input_ids, attention_mask):
+        """
+        Args:
+            images: Tensor of shape (batch_size, 3, 224, 224)
+            input_ids: Tensor of shape (batch_size, seq_length)
+            attention_mask: Tensor of shape (batch_size, seq_length)
+
+        Returns:
+            Image and text embeddings normalized for similarity calculation
+        """
+
+        image_embeddings = self.get_images_features(images)
+        text_embeddings = self.get_texts_feature(input_ids, attention_mask)
+        return image_embeddings, text_embeddings