Delete folder /model with huggingface_hub

model/best.pt

@@ -1,3 +0,0 @@
-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()