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model/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb622fc997c8e7a6c2881662d79a40b619cb5e83e563f8a4f5b5cece7fb73d1c
+size 363250197

model/clip_model.py

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+import torch.nn as nn
+from transformers import DistilBertTokenizer, DistilBertModel,DistilBertConfig
+import timm
+
+def cross_entropy(preds,targets,reduction='none'):
+    log_softmax = nn.LogSoftmax(dim=-1)
+    loss = (-targets * log_softmax(preds)).sum(1)
+    if reduction == 'none':
+        return loss
+    else:
+        return loss.mean()
+    
+
+class TextEncoder(nn.Module):
+    def __init__(self,model_name='distilbert-base-uncased',pretrained=True,trainable=True):
+        super().__init__()
+        if pretrained:
+            self.model = DistilBertModel.from_pretrained(model_name)
+        else:
+            self.model = DistilBertModel(config=DistilBertConfig())
+        
+        for p in self.model.parameters():
+            p.requires_grad = trainable
+        
+        self.target_token_idx =0
+        
+    def forward(self,input_ids,attention_mask):
+        output = self.model(input_ids=input_ids,attention_mask=attention_mask)
+        last_hidden_state = output.last_hidden_state
+        return last_hidden_state[:,self.target_token_idx,:]
+    
+
+class ImageEncoder(nn.Module):
+    def __init__(self,model_name='resnet50',pretrained=True,trainable=True):
+        super().__init__()
+        self.model = timm.create_model(model_name,pretrained,num_classes=0,global_pool="avg")
+        
+        
+        for p in self.model.parameters():
+            p.requires_grad = trainable
+            
+    def forward(self,x):
+        return self.model(x)
+    
+
+class ProjectionHead(nn.Module):
+    def __init__(self,embedding_dim,projection_dim=256,dropout=0.1):
+        super().__init__()
+        self.projection = nn.Linear(embedding_dim,projection_dim)
+        self.gelu = nn.GELU()
+        self.fc = nn.Linear(projection_dim,projection_dim)
+        self.dropout = nn.Dropout(p=dropout)
+        self.layer_norm = nn.LayerNorm(projection_dim)
+    
+    def forward(self,x):
+#         print("shape of input",x.shape)
+        projected = self.projection(x)
+#         print("shape after projection-1",projected.shape)
+        x = self.gelu(projected)
+#         print("shape after Gelu Layer",x.shape)
+        x = self.fc(x)
+#         print("shape after projection-2",x.shape)
+        x = self.dropout(x)
+#         print("shape after dropout 1",x.shape)
+        x = self.layer_norm(x+projected)
+#         print("shape output / Normalization",x.shape)
+        return x
+
+
+class CLIPModel(nn.Module):
+    def __init__(self,temperature=1.0,image_embedding=2048,text_embedding=768):
+        super().__init__()
+        self.image_encoder = ImageEncoder()
+        self.text_encoder = TextEncoder()
+        self.image_projection = ProjectionHead(embedding_dim=image_embedding)
+        self.text_projection = ProjectionHead(embedding_dim=text_embedding)
+        self.temperature = temperature
+    
+    def forward(self,batch):
+        image_features = self.image_encoder(batch['image'])
+        text_features = self.text_encoder(input_ids = batch['input_ids'], attention_mask=batch['attention_mask'])
+        image_embeddings = self.image_projection(image_features)
+#         print('image_embedding_shape',image_embeddings.shape)
+        text_embeddings = self.text_projection(text_features)
+#         print('text_embedding_shape',text_embeddings.shape)
+        
+        
+        #calculating the loss
+        logits = (text_embeddings @ image_embeddings.T) / self.temperature
+#         print("logits size() : ",logits.shape)
+        image_similarity = image_embeddings @ image_embeddings.T
+#         print("image_similarity() : ",image_similarity.shape)
+        text_similarity = text_embeddings @ text_embeddings.T
+#         print("text_similarity() : ",text_similarity.shape)
+        targets = F.softmax((image_similarity + text_similarity )/ 2*self.temperature, dim=-1)
+#         print("targets shape: ",text_similarity.shape)
+        texts_loss = cross_entropy(logits,targets,reduction='none')
+#         print("texts_loss shape",texts_loss)
+        image_loss = cross_entropy(logits.T ,targets.T,reduction='none')
+#         print("image_loss shape",image_loss)
+        loss = (image_loss + texts_loss) / 2.0
+        return loss.mean()