Adding Models and Inference

config.js

@@ -0,0 +1,11 @@
+{
+  "architectures": [
+    "SCOLD"
+  ],
+  "model_type": "clip-base",
+  "image_encoder": "swin_base_patch4_window7_224",
+  "text_encoder": "roberta-base",
+  "embedding_dim": 512,
+  "t_init": 0.07,
+  "b_init": 0.0
+}

encoder.py

@@ -0,0 +1,66 @@
+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)

inference.py

@@ -0,0 +1,33 @@
+import torch
+from model import LVL
+from transformers import RobertaTokenizer
+from PIL import Image
+from torchvision import transforms
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model
+model = LVL()
+model.load_state_dict(torch.load("pytorch_model.bin", map_location=device))
+model.to(device)
+model.eval()
+
+# Load tokenizer
+tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+
+# Image transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+
+def predict(image_path, text):
+    image = transform(Image.open(image_path).convert("RGB")).unsqueeze(0).to(device)
+    tokens = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
+
+    with torch.no_grad():
+        img_feat, txt_feat = model(image, tokens["input_ids"], tokens["attention_mask"])
+        similarity = torch.matmul(img_feat, txt_feat.T).squeeze()
+
+    return similarity.item()

model.py

@@ -0,0 +1,37 @@
+import torch.nn as nn
+import torch
+import numpy as np
+from model.encoder import ImageEncoder, RobertaEncoder
+import torch.nn.functional as F
+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 = F.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 = F.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

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1664be0db36c8a106001016da28c94416ae51671f2c7ae683fe07e90ceaaf352
+size 950112466