Update encoder.py

encoder.py

@@ -1,66 +1,101 @@
-import torch
-import torch.nn as nn
-from transformers import CLIPTextModel, RobertaModel, CLIPVisionModel
-from timm import create_model
-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", 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)
-
-
-    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
-
-from transformers import RobertaModel
-
-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)
+import torch
+import torch.nn as nn
+from transformers import CLIPTextModel, RobertaModel, CLIPVisionModel
+from timm import create_model
+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-tiny-patch4-window7-224 ", 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
+
+from transformers import RobertaModel
+
+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)
+
+from transformers import AutoTokenizer, Siglip2TextModel,AutoModel
+
+
+class SigLIP2TextEncoder(nn.Module):
+    def __init__(self, embedding_dim=512):
+        super(SigLIP2TextEncoder, self).__init__()
+        model  = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
+        self.text_encoder = model.text_model
+        hidden_size = self.text_encoder.config.hidden_size
+        self.projection = nn.Linear(hidden_size, embedding_dim)
+
+        nn.init.xavier_uniform_(self.projection.weight)
+        nn.init.zeros_(self.projection.bias)
+
+        for param in self.text_encoder.parameters():
+            param.requires_grad = True
+        for param in self.projection.parameters():
+            param.requires_grad = True
+
+    def forward(self, tokens):
+        """
+        Args:
+            tokens:
+
+        Returns:
+            Tensor of shape (batch_size, embedding_dim)
+        """
+
+        outputs = self.text_encoder(**tokens)
+
+        return self.projection(outputs.pooler_output)